Method and system for operating an automated storage and retrieval system

Abstract

Certain aspects of the disclosure provide for an automated retrieval and storage system, ASRS, to be operated to maximize efficiency of resources, such as automation devices, whilst meeting anticipated demand.

Description

METHOD AND SYSTEM FOR OPERATING AN AUTOMATED STORAGE AND RETRIEVAL SYSTEMINTRODUCTIONTechnical Field.

[0001] Aspects of the present disclosure relate to a method and system of operating an automated storage and retrieval system. More particularly, the disclosure concerns the coordination of automated resources such as load-handling devices and robotic manipulators, to ensure efficient container processing under variable operating conditions.Background

[0002] Some commercial and industrial activities require systems that enable the storage and retrieval of a large number of different products. For example, WO2015 / 185628A2 (Ocado), which is hereby incorporated by reference in its entirety describes an automated storage and fulfilment system (“ASRS”) in which stacks of storage containers are arranged within a grid storage structure. The containers are accessed from above by load-handling devices operative on rails or tracks located on the top of the grid storage structure. WO2015 / 185628A2 also describes how the load-handling devices are controlled to move containers into, within, and out of the grid storage structure. The load-handling devices may be those described in W02015 / 019055A1 (Ocado), which is hereby incorporated by reference in its entirety. In such an ASRS, items within the containers may be accessed by robotic picking stations that use a robotic manipulator or arm, such as that described in W02017 / 081281A1 (Ocado), which is hereby incorporated by reference in its entirety. WO2014203126A1, WO2022243326A1, and WO2023062233, all of which are hereby incorporated by reference in their entireties, describe further systems that can handle the containers as part of an outbound operation of the ASRS. These systems include grid storage structures, load-handling devices, and robotic picking stations. Building upon these concepts, the present disclosure focuses on how such automated resources can be scaled and coordinated to manage fluctuating workloads more efficiently.

[0003] An ASRS may use one or more automated resources to dispatch a customer order. Customer orders are variable and the ASRS has to be able to react to both a spike and downturn in demand. The automated resources must therefore be controlled and coordinated so that throughputlimits are not exceeded, congestion in the grid is avoided, energy is not wasted, and dispatch times are reliably met.

[0004] Accordingly, there is a need in the art for systems and methods to dynamically scale and coordinate the operation of automated resources in response to forecasted demand, thereby reducing power consumption, extending equipment lifetime, and ensuring that dispatch times are achieved.SUMMARY

[0005] A computer-implemented method of operating an automated storage and retrieval system is described. The system comprises a first set of parallel rails or tracks and a second set of parallel rails or tracks extending substantially perpendicularly to the first set of rails or tracks in a substantially horizontal plane to form a grid comprising a plurality of grid spaces. The system further comprises one or more load-handling devices, wherein each load-handling device is configured to move along the first and / or second sets of tracks, and lift a container from beneath the grid and / or lower a container beneath the grid. A controller is in communication with the loadhandling devices and with further automated resources of the system. The further automated resources comprise one or more of storage-container filling stations, robotic picking stations arranged to receive storage containers and delivery containers from the load-handling devices and to transfer items between the storage containers and the delivery containers, buffer regions for temporary storage of containers, combine-and-separate devices arranged to separate and / or combine delivery containers and storage containers, container handlers configured to place delivery containers into loading frames for vehicle dispatch, loading frames, and autonomous mobile robots or automated guided vehicles configured to move the loading frames. The method comprises generating a forecast for orders to be fulfilled by the system, wherein the orders comprise one or more of short lead time orders and long lead time orders. A route profile comprising a plurality of routes is generated, each associated with a vehicle, to deliver the forecasted short lead time orders and long lead time orders on time. The controller determines a dispatch time for each route of the plurality of routes. The controller generates an operational plan for the system dependent on the forecast and the route profile. The operational plan comprises control instructions that scale operation of the automated resources of the system in accordance with the forecasted demand. The scaling is targeted to specific automated resources dependent onperformance data received by the controller from the automated resources, the performance data representing operational characteristics of the automated resources. The system is operated according to the operational plan by executing the scaled control instructions so that the automated resources are adjusted to achieve the dispatch time for each respective route. The controller dynamically generates and executes control instructions that scale the operation of the automated resources of the ASRS, comprising one or more of storage-container filling stations, robotic picking stations, buffer regions, combine-and-separate devices, container handlers, loading frames, and autonomous mobile robots or automated guided vehicles, so that automation is used efficiently across the system. By coordinating activity levels among these automated resources in accordance with anticipated workload variations, the system minimises idle operation and unnecessary automation resource power draw, leading to reduced overall energy consumption while maintaining precise timing and throughput of container handling operations.

[0006] The operational plan may comprise scaling one or more operating parameters of the load-handling devices, including at least one of travel speed, acceleration, and / or deceleration, dependent on forecasted demand. Scaling operating parameters such as travel speed, acceleration, and deceleration allows the load-handling devices to operate at appropriate performance levels for current demand, balancing throughput requirements with energy consumption and mechanical wear.

[0007] The operational plan may comprise scaling a duty cycle of the load-handling devices by varying active and idle periods to reduce energy consumption during low demand and increase throughput during high demand. Varying duty cycles with active and idle periods enables the ASRS to reduce energy consumption during periods of low demand while maintaining the ability to increase throughput during high demand periods, optimizing energy use.

[0008] The operational plan may comprise scaling a number of load-handling devices assigned to container-retrieval tasks dependent on forecasted demand, with surplus devices placed into standby or charging when not required. Adjusting the number of active load-handling devices based on forecasted demand and placing surplus devices into standby or charging modes prevents unnecessary energy expenditure while ensuring sufficient capacity is available when needed.

[0009] The operational plan may comprise scaling an extent of the grid made available to the load-handling devices dependent on forecasted demand. Scaling the extent of the grid madeavailable to load-handling devices according to forecasted demand allows the system to concentrate operations in smaller areas during low demand periods, reducing travel distances and operational overhead.

[0010] The operational plan may comprise scaling a size of one or more buffer regions for storage or delivery containers dependent on forecasted demand. Scaling buffer region sizes based on forecasted demand prevents overflow conditions during peak periods and reduces wasted space during low demand periods, maintaining smooth flow of storage and delivery containers through the system.

[0011] The operational plan may comprise scaling charging schedules for battery-powered automated resources dependent on forecasted demand. Adjusting charging schedules according to forecasted demand ensures that battery-powered automated resources have sufficient charge capacity available during anticipated high-demand periods while avoiding unnecessary charging cycles during low-demand periods.

[0012] Scaling may comprise switching said battery-powered automated resources between low-power and high-performance operating modes dependent on forecasted demand. Switching between low -power and high-performance operating modes allows the system to extend battery life and reduce charging frequency during low demand periods while providing enhanced performance capabilities when forecasted demand requires increased throughput.

[0013] The operational plan may comprise scaling a number of robotic picking stations activated dependent on forecasted demand. Activating robotic picking stations in proportion to forecasted demand reduces energy consumption and maintenance requirements associated with idle stations while ensuring sufficient picking capacity is available to meet anticipated order volumes

[0014] The operational plan may comprise allocating storage and delivery containers among the robotic picking stations to maintain balanced operation and optimise utilisation of the robotic picking stations. Allocating storage and delivery containers to maintain balanced operation across active robotic picking stations prevents individual stations from becoming overloaded or underutilized, maximizing overall system picking throughput and minimizing order processing time.

[0015] The operational plan may comprise scaling operation of one or more combine-and- separate devices such that a number of combine-and-separate devices active concurrently is varied dependent on forecasted demand, and idle devices are powered down when not required. Powering down idle combine-and-separate devices when not required eliminates unnecessary energy consumption during low demand periods while maintaining the ability to activate additional devices when forecasted demand increases.

[0016] The operational plan may comprise scaling operation of container handlers configured to place delivery containers into loading frames, wherein scaling may comprise varying a number of container handlers in use dependent on forecasted demand and / or adjusting a service rate of the container handlers to reduce energy consumption and mechanical wear during low demand or to increase throughput during high demand, and further may comprise adjusting a number of loading frames staged for transfer to vehicles. Adjusting service rates of container handlers and the number of loading frames staged for transfer allows the system to reduce mechanical wear during low demand periods while increasing throughput capacity during high demand periods, extending equipment lifespan while meeting delivery requirements.

[0017] The operational plan may comprise scaling scheduling of container transfers at interfaces between temperature-controlled zones, the transfers being performed by interface equipment that receives containers from load-handling devices of a source zone and provides containers to load-handling devices of a destination zone. Scaling scheduling of container transfers at interfaces between temperature-controlled zones reduces the frequency and duration of interface openings, minimizing thermal energy loss and maintaining stable environmental conditions within each zone.

[0018] Scaling may comprise applying batching of container transfers at a zone interface by grouping multiple containers into transfer windows followed by recovery intervals during which a door, gateway, or barrier at the interface is closed. Batching container transfers into transfer windows followed by recovery intervals allows doors, gateways, or barriers at zone interfaces to remain closed for extended periods, reducing thermal exchange between zones and lowering cooling or heating energy requirements.

[0019] The operational plan may comprise scaling workload allocation among automated resources based on maintenance status, such that resources identified as worn or fault-prone areassigned reduced tasks and healthy resources are allocated increased tasks. Allocating reduced tasks to resources identified as worn or fault-prone while increasing tasks assigned to healthy resources prevents premature failures and extends the operational lifespan of degraded equipment until scheduled maintenance can be performed.

[0020] Scaling factors may be computed from forecasted demand and the performance data received by the controller from the automated resources. Computing scaling factors from both forecasted demand and actual performance data enables the controller to account for real-world operational constraints and capabilities, resulting in more accurate and achievable resource scaling decisions than forecast-only approaches.

[0021] Scaling factors may be updated whenever forecasts are updated or real-time demand deviates from predictions, and updated control instructions may be transmitted to the automated resources so that parameters comprising device speeds, duty-cycle assignments, charging schedules, buffer allocations, picking throughput, interface-transfer rates, container-handler utilisation and service rate, and loading-frame staging are continuously adapted. Updating scaling factors and transmitting updated control instructions whenever forecasts change or real-time demand deviates from predictions enables the system to maintain optimal performance across multiple operational parameters despite unpredictable demand fluctuations, preventing performance degradation during unexpected demand variations.

[0022] The operational plan may interlink automated resources such that storage containers are provided to the grid, the load-handling devices deliver storage and delivery containers to robotic picking stations that transfer items between the storage containers and the delivery containers, filled delivery containers are directed to buffer regions, then to combine-and-separate devices, and subsequently to container handlers that deliver the filled delivery containers into loading frames for vehicle dispatch and receive empty delivery containers returned from vehicles for reintroduction into the grid, wherein the controller coordinates these operations dependent on forecasted demand so that all container movements, item transfer, buffering, separation, delivery, return, and re-storage flows are synchronised. Coordinating the complete sequence of container movements from grid storage through picking, buffering, separation, vehicle loading, and return based on forecasted demand eliminates bottlenecks and idle time between process stages, ensuring smooth flow throughout the entire fulfillment cycle.

[0023] The ASRS may comprise at least one automated resource to process containers for the orders, and the method may further comprise providing one or more containers to at least one vehicle prior to a dispatch time of the vehicle dependent on a route profile associated with the vehicle. Providing containers to vehicles prior to dispatch time based on route profiles allows the ASRS to complete order processing and loading operations in advance of scheduled departures, reducing vehicle waiting time and enabling more reliable adherence to delivery schedules.

[0024] The forecast may comprise a forecasted delivery time and delivery location for each order. Including forecasted delivery time and location for each order enables the controller to prioritize and sequence ASRS operations according to geographic delivery routes and time windows, optimizing the order in which containers are processed to align with efficient vehicle routing.

[0025] The route profile may comprise a breakdown of one or more of short lead-time orders and long lead-time orders on each delivery vehicle and associated delivery times. Breaking down orders by lead-time category on each delivery vehicle enables the controller to differentiate processing priorities and resource allocation strategies for urgent versus non-urgent orders, allowing the ASRS to optimize throughput for time- sensitive deliveries while efficiently handling standard orders.

[0026] Determining a dispatch time for each route of the route profile may comprise modelling the ASRS to determine an earliest possible dispatch time for each route of the route profile when processing orders by the ASRS. Modelling the ASRS to determine earliest possible dispatch times accounts for actual system processing capabilities and constraints, preventing the generation of route profiles with unachievable dispatch schedules and ensuring that planned delivery times are operationally feasible.

[0027] The method may further comprise iteratively generating the route profile until the earliest possible dispatch time for each route of the route profile is earlier than a required dispatch time for each route of the route profile. Iteratively regenerating route profiles until earliest possible dispatch times meet required dispatch times ensures that the final operational plan can reliably achieve scheduled vehicle departures, eliminating the risk of delayed deliveries due to insufficient ASRS processing capacity.

[0028] Generating the operational plan may comprise outputting instructions for the operational plan, and outputting instructions may comprise outputting a control signal for the ASRS. Outputting control signals for the ASRS enables the operational plan to directly actuate automated resources without manual intervention, reducing implementation delays and ensuring that resource scaling adjustments are executed precisely as determined by the forecasted demand analysis.

[0029] The control signal may comprise displaying a timing plan for automated resources available within the ASRS. Displaying a timing plan for automated resources provides operators with visibility into scheduled resource activities and availability, facilitating coordination between automated and manual operations and enabling proactive intervention if deviations from the plan are detected.

[0030] Generating the operational plan may comprise setting a finalisation time for confirming respective orders for processing by the ASRS. Setting a finalisation time for confirming orders establishes a clear cutoff point beyond which orders are committed for processing, allowing the ASRS to lock in resource allocations and prevent last-minute order changes from disrupting the operational plan and scheduled dispatch times.

[0031] The method may be based on a predetermined time. Basing the method on a predetermined time enables the ASRS to operate according to regular, repeating planning cycles, allowing automated resources and personnel to anticipate when operational plans will be generated and facilitating integration with external systems that operate on fixed schedules.

[0032] A computer program comprising instructions is described, wherein the instructions, when executed by a processor, cause the processor to carry out the computer-implemented method described above.

[0033] An automated storage and retrieval system is described. The system comprises a first set of parallel rails or tracks and a second set of parallel rails or tracks extending substantially perpendicularly to the first set of rails or tracks in a substantially horizontal plane to form a grid comprising a plurality of grid spaces. The system comprises one or more load-handling devices, wherein each load-handling device is configured to move along the first and / or second sets of tracks, and lift a container from beneath the grid and / or lower a container beneath the grid. The system comprises further automated resources, the further automated resources comprising one ormore of storage-container filling stations, robotic picking stations arranged to receive storage containers and delivery containers from the load-handling devices and to transfer items between the storage containers and the delivery containers, buffer regions for temporary storage of containers retrieved from the grid, combine-and-separate devices arranged to separate and / or combine delivery containers and storage containers, container handlers configured to place delivery containers into loading frames for vehicle dispatch, loading frames, and autonomous mobile robots or automated guided vehicles configured to move the loading frames. The system comprises a controller in communication with the load-handling devices and the further automated resources, wherein the controller is configured to carry out the computer- implemented method described above.

[0034] The following description and the related drawings set forth in detail certain illustrative features of one or more aspects.DESCRIPTION OF THE DRAWINGS

[0035] The appended figures depict certain aspects and are therefore not to be considered limiting of the scope of this disclosure.

[0036] FIG. 1 shows a conceptual representation of how an ASRS processes containers to dispatch customer orders.

[0037] FIG. 2 shows a grid framework structure and load-handling devices that may be controlled by aspects described herein.

[0038] FIG. 3 shows a single load-handling device with a container-lifting assembly in a lowered configuration that may be controlled by aspects described herein.

[0039] FIG. 4 shows a robotic picking station that may be controlled by aspects described herein.

[0040] FIGS. 5A and 5B are schematic diagrams of the stages in the separation of a delivery container and a storage container in a combination and separation device that may be controlled by aspects described herein.

[0041] FIG. 6 shows a container handling system for loading containers onto a movable frame that may be controlled by aspects described herein.

[0042] FIG. 7 shows an example method for operating an ASRS in accordance with aspects described herein.

[0043] FIGS. 8A and 8B show an example route profile generated by the method of FIG. 7 in accordance with aspects described herein.

[0044] FIGS. 9A and 9B show an example operational plan for ASRS resources to execute the route profile of FIGS. 8A and 8B in accordance with aspects described herein.

[0045] FIG 10 shows an example resource allocation of the operational plan in accordance with aspects described herein.

[0046] FIG. 11 shows an example method of generating the operational plan in accordance with aspects described herein.

[0047] FIG. 12 shows an example user interface for generating and outputting the operational plan.

[0048] FIG. 13 shows an example processing system for implementing aspects described herein.

[0049] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the drawings. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.DETAILED DESCRIPTION

[0050] Aspects of the present disclosure provide apparatuses, methods, processing systems, and computer-readable mediums for an ASRS to be operated to maximize efficiency of resources, such as automation devices and systems, whilst meeting anticipated demand.

[0051] A demand placed on the ASRS is typically responsive to orders received from a client device of a customer via a server device. The client device may access an application or webshop that shows items served by the ASRS. The server and client devices are configured to generate orders to be processed by resources, such as automation devices, of the ASRS. Orders can be placed within a variable time frame before the order is delivered. Orders typically fall into two categories.

[0052] In a first category, orders are received before the day or date of delivery. This type of order is known as a long lead time order (“LLTO”) and may be placed days or weeks in advance of the delivery date. After a LLTO order has been placed and confirmed (e.g., by the server device), the order may be edited / updated at any point up to a first “cut-off time.” After the first cut-off time, the LLTO may no longer be edited and is considered “finalized.” The LLTO is also finalized at the first cut-off time regardless of there being edits / updates. The first cut-off time is configurable, but is set to allow the ASRS to process containers to ensure a delivery occurs at a time requested by the customer. That is, the first cut-off time provides sufficient time for the customer order to be processed by the ASRS for dispatch at a time to meet the requested delivery time. In one example, the first-cut off time may be at least 24 hours before the delivery time.

[0053] In a second category, orders are typically received on the day or date of delivery, and may include orders for delivery in as little as 1 hour. This type of order is known as a short lead time order (“SLTO”) and in one example may be placed within 1 to 12 hours of the requested delivery time. After a SLTO order has been placed and confirmed, the order may be edited / updated at any point up to a second “cut-off time.” After the second cut-off time, the SLTO may no longer be edited and is considered “finalized ” The SLTO is also finalized at the second cut-off time regardless of there being edits / updates. The second cut-off time is configurable, but is generally later than the first cut-off time and set to allow the ASRS to process the containers to ensure the delivery occurs at a time requested by the customer. That is, the second cut-off time provides sufficient time for the customer order to be processed by the ASRS for dispatch at a time to meet the requested delivery time. In one example, the second-cut off time may be at least 1 hour before the delivery time.

[0054] When both LLTOs and SLTOs have been finalized, they can be allocated to storage space in delivery vehicles to ensure that all orders, regardless of type, are delivered on time. Thus, each delivery vehicle may have LLTOs only, SLTOs only, or a mixture of LLTOs and SLTOs. Whilst the ASRS can react to the variable demand of LLTOs by setting the first cut off time accordingly, the second cut-off time being much closer to the delivery time could lead to an influx of SLTO orders that could overwhelm the ASRS and result in several orders, both LLTOs and SLTOs, being dispatched late. The ASRS not being able to react to demand fluctuations creates a technical problem in terms of how to coordinate ASRS automated resources to prevent this happening.

[0055] The aspects discussed below allow the ASRS to react to fluctuating demand whilst maximizing efficiency of resources, such as automation devices and systems, thus providing a technical solution to the aforementioned problem. Specifically, all of the inbound automation devices / systems, container processing automation devices / systems outbound automation devices / systems, and / or associated resources, such as pickers and / or handlers, available to the ASRS may be choreographed or orchestrated to process the containers for all of orders to deliver at expected times.

[0056] FIG. 1 shows a conceptual representation of how an ASRS 9 processes containers to dispatch customer orders.

[0057] A grid storage structure and load-handling devices 1 store and transport containers respectively. Typically, the load-handling devices directly handle one type of container. A first subset of the containers can hold items, and are thus denoted as storage containers. A second subset of containers can hold a delivery container, and thus the container that receives the delivery container therein is considered a second subset. Thus, the container of the first and second subset are identical, but only differ in that the first subset receives items and the second subset receives delivery containers. The delivery container may be heavy duty to cope with handling and transport outside the grid storage structure, such as loading onto delivery vehicles, which may be autonomous vehicles such as vans and / or trucks, and / or unmanned aerial vehicles, such as drones. It will be appreciated however that the second subset of containers may serve as a delivery container directly rather than receive a separate delivery container.

[0058] FIG. 1 shows an example of how the storage and delivery containers are processed. As part of an inbound operation, the storage containers are filled with items at a storage container filling station. The storage filling station may use a vision system and a robotic manipulator for this purpose, a human operator, or both. In either case, the items and storage containers may be tracked using an optical system and, for example, a machine-readable code and / or and RFID tag. The storage containers may enter the grid storage structure via conveyors and or lift devices to be retrieved by the load-handling devices of 1. FIG. 1 thus depicts a process that uses inbound automation devices / systems.

[0059] The grid storage structure and load-handling devices 1 are coupled to picking stations 3 and 4 (also known as workstations). Picking station 3 may be a robotic manipulator operationalabove the tracks of the grid storage structure. Picking station 4 may instead be located on side or below the grid storage structure and operate in a similar way to the storage container filling station. The grid storage structure holds both storage and delivery containers. Each storage container typically holds a number of the same item / product (e.g. one storage container may store soft drinks, and another storage container may store bread; whilst grocery items are used as an example, any product type in principle may be used). Delivery containers contain a number of different items / products as per a customer order. The grid storage structure of the ASRS may have at least one buffer region 5 to hold storage and / or delivery containers until they are ready to be processed further by the load-handling devices. As shown in FIG. 1, a picking task involves moving both the storage and delivery containers to and from picking stations to allow an item from a storage container to be placed into a delivery container. This process is repeated until a delivery container has been filled. FIG. 1 thus depicts a process that uses container processing automation devices / systems or automated resources.

[0060] The delivery container is then moved to the outbound area 6 as part of an outbound task. The outbound area allows the delivery containers to be prepared for dispatch to a customer. The outbound area in this example includes a device 6 which separates (or removes) the delivery container from a container of the second subset. After the delivery containers have been separated, they are automatically loaded onto a delivery frame using device 7. A delivery frame typically holds a number of delivery containers. The delivery frame is then wheeled or loaded onto a delivery vehicle via loading area 8, perhaps via an autonomous mobile robot (“AMR”) or an automated guided vehicle (“AGV”). The customer orders can then be dispatched using the delivery vehicle, which may be an autonomous vehicle. As shown in FIG. 1, the return of a delivery vehicle allows empty delivery containers to be returned to the grid storage structure of the ASRS. FIG. 1 thus depicts a process that uses outbound automation devices / systems or automated resources in respect of loading delivery containers onto a vehicle, and outbound automation devices / systems or automated resources in respect of removing delivery containers from a vehicle to the grid storage structure.

[0061] ASRS 9 may be further sub-divided to operate in different temperature zones, such as ambient, chilled, and frozen, the outputs of which are combined for customer orders. Hence, the other items flow shown in FIG. 1 may be those received from an adjacent or otherwise interconnected ASRS, or other system, which operates at a different temperature.

[0062] The ASRS of FIG. 1 uses several automation systems or automated resources to dispatch a customer order. Customer orders are variable and the ASRS has to be able to react to both a spike and downturn in demand. In either case, all of the automated resources most be controlled and coordinated to ensure that all customer orders are dispatched on time. In some aspects, all of the automated resources may be controlled by a master controller.

[0063] A master controller refers to a centralized processing unit or system designed to govern, coordinate, and regulate the operation of one or more subordinate devices or systems within a network or mechanical assembly, such as an ASRS. The master controller may be configured to execute complex algorithms and protocols to ensure the synchronization and interoperability of various components it controls. A master controller may typically comprise one or more processor units, one or more memory storages for program code and operational data, one or more communication interfaces for data exchange, and one or more input / output modules for interaction with both human operators and peripheral devices.

[0064] A master controller may further be equipped with firmware and / or software that enables it to perform real-time monitoring, data processing, decision-making, and directive issuance to subordinate systems. The master controller may include functionalities for error detection, system diagnostics, and adaptive control mechanisms to modify its operation in response to environmental changes or input fluctuations. In advanced implementations, it may possess machine learning capabilities or adaptive algorithms to optimize performance dynamically.

[0065] In applications involving distributed systems, a master controller functions as the primary node, orchestrating tasks by allocating resources, managing workloads, and maintaining seamless operation through effective communication protocols. Such communication might involve wireless or wired connections, adhering to established standards or proprietary protocols, depending on the specific architecture in which the master controller is implemented.

[0066] FIG. 2 is one example automation device / system or automated resource that may be controlled by aspects described herein. The automation devices / systems of FIG. 2 are examples of container processing automation devices / systems, which may be used in block 1 of FIG. 1. As shown in FIG. 2, stackable containers 10 (i.e. both storage and delivery containers), also known as “bins” or “totes”, are stacked on top of one another to form stacks 12. The stacks 12 are arranged in a grid framework structure 14. The grid framework structure 14 is made up of a plurality ofstorage columns or grid columns. Each grid in the grid framework structure has at least one grid column to store a stack of containers. Each container 10 typically holds a plurality of product items (not shown).

[0067] The grid framework structure 14 comprises a plurality of upright members 16 that support horizontal members 18, 20. A first set of parallel horizontal members 18 is arranged perpendicularly to a second set of parallel horizontal members 20 in a grid pattern comprising respective grid spaces to form a horizontal grid structure 15 supported by the upright members 16. The members 16, 18, 20 are typically manufactured from a robust material, such as metal or composite. The containers 10 are stacked between the members 16, 18, 20 of the grid framework structure 14, so that the grid framework structure 14 guards against horizontal movement of the stacks 12 of containers 10 and guides the vertical movement of the containers 10.

[0068] The top level of the grid framework structure 14 comprises a horizontal grid structure 15, including rails 22 arranged in a grid pattern comprising respective grid spaces across the top of the stacks 12. The rails (or tracks) 22 guide a plurality of load-handling devices 30. A first set 22a of parallel rails 22 guide movement of the robotic load-handling devices 30 in a first direction (e.g. an Y-direction along parallel rails) across the top of the grid framework structure 14. A second set 22b of parallel rails 22, arranged perpendicular to the first set 22a, guide movement of the loadhandling devices 30 in a second direction (e.g. a X-direction along parallel tracks), perpendicular to the first direction. In this way, the rails 22 allow the robotic load-handling devices 30 to move laterally in two dimensions in the horizontal X-Y plane. A load-handling device 30 can be moved into position above any of the stacks 12.

[0069] FIG. 3 is another example automation device / system or automated resource that may be controlled by aspects described herein. The automation device / system of FIG. 3 is an example of a container processing automation device / system, which may be used in block 1 of FIG. 1. FIG. 3 shows a load-handling device 30 which is further described in W02015 / 019055 (Ocado). The load-handling device 30 comprises a vehicle 32, which is arranged to travel on the rails 22 of the grid frame structure 14. A first set of wheels 34, consisting of a pair of wheels 34 on the front of the vehicle 32 and a pair of wheels 34 on the back of the vehicle 32, is arranged to engage with two adjacent rails of the first set 22a of rails 22. Similarly, a second set of wheels 36, consisting of a pair of wheels 36 on each side of the vehicle 32, is arranged to engage with two adjacent railsof the second set 22b of rails 22. Each set of wheels 34, 36 can be lifted and lowered, by way of a direction-change assembly, so that either the first set of wheels 34 or the second set of wheels 36 is engaged with the respective sets of rails 22a, 22b at any one time. For example, when the first set of wheels 34 is engaged with the first set of rails 22a and the second set of wheels 36 is lifted clear from the rails 22, the first set of wheels 34 can be driven, by way of a drive assembly housed in the vehicle 32, to move the load-handling device 30 in the Y-direction. To achieve movement in the X-direction, the first set of wheels 34 is lifted clear of the rails 22, and the second set of wheels 36 is lowered into engagement with the second set 22b of rails 22. The drive assembly can then be used to drive the second set of wheels 36 to move the load-handling device 30 in the X- direction.

[0070] The load-handling device 30 is equipped with a container- lifting device or assembly, e.g. a crane or winch mechanism, to lift a container from above. The container-lifting device comprises a tether (or cable) 38 wound on a spool or reel and a container gripping device 39. The container- lifting device shown in FIG. 3 comprises a set of four tethers 38 extending in a vertical direction. The tethers 38 are connected at or near the respective four corners of the container gripping device (or gripper device) 39, e.g. a lifting frame, for releasable connection to a container 10. The container gripping device 39 is configured to releasably grip the top of a container 10 to lift it from a stack of containers in a storage system. That is, the container gripping device 39 is configured to attach to and / or release from a container, which may be detected by a sensor.

[0071] To remove a container 10 from the top of a stack 12, the load-handling device 30 is first moved in the X- and Y-directions to position the container gripping device 39 in a respective grid space above the stack 12. The container gripping device 39 is then lowered vertically in the Z-direction to engage with the container 10 on the top of the stack 12. The container gripping device 39 grips (e.g., attaches to) the container 10, which may be confirmed by one or more sensor readings, and is then pulled upwards by the tethers 38, with the container 10 attached. At the top of its vertical travel, the container 10 is held above the rails 22 accommodated within the vehicle 32 body (or skeleton). In this way, the load-handling device 30 can be moved to a different position in the X-Y plane, carrying the container 10 along with it, to transport the container 10 to another location. On reaching the target location (e.g. another stack 12, an access point in the storage system, or a conveyor belt) the container (or bin) 10 can be lowered from the container receivingportion and released from the container gripping device 39. It will be appreciated that the automation devices / systems of FIG. 3 may be under the control of a master controller.

[0072] FIG. 4 is another example automation device / system or automated resource that may be controlled by aspects described herein. The automation device / system of FIG. 4 is an example of a container processing automation device / system, which may be used in blocks 3 and / or 4 of FIG. 1. In particular, FIG. 4 shows a robotic picking station 50 mounted on top of the grid framework structure 14, e.g., alongside the load-handling devices 30 (not shown). The robotic picking station 50 comprises a robotic manipulator 52 comprising a robotic arm 54 and an end effector 56 for releasably engaging a product to be manipulated, together with several designated grid cells 60, 62. The end effector 56 may be a suction device 64 connected to a vacuum source by a vacuum line within trunking 66. The robotic manipulator 52 is mounted on a plinth 58 above a single grid cell 60 and, depending on its location on the structure 1, can be surrounded by up to eight other grid cells 62 as shown in FIG. 4. In general, the robotic manipulator 52 is configured to pick an item or product from any one of the containers (e.g. a storage container) located in one of the designated grid cells 62 and place it in another container (e.g. a delivery container) located in another of the designated grid cells 62. The load-handling devices 30 collect containers from, and deliver them to, the designated grid cells 62 as necessary. In this way, the robotic picking station 50 and the load-handling devices 30 work in conjunction (e.g., via control by a master controller) to fulfil a customer order or to redistribute products throughout the storage and retrieval system 1. Each robotic picking station is arranged to receive storage containers and delivery containers from the load-handling devices and to transfer items between the storage containers and the delivery containers. Delivery of the storage and delivery containers to each picking station is coordinated to balance workload and throughput. A picking station on top of the storage and retrieval structure provides an efficient point to carry out a picking operation. Absent this, the containers would have to continually exit and enter an ASRS to perform a picking operation at a picking station that is typically located at a bottom of the grid framework structure. This may involve lowering and raising containers over a significant height a significant number of times with a corresponding power demand as well as wear and tear on the moving parts. The automation device / system of FIG. 4 may be under the control of a master controller.

[0073] FIGS. 5A and 5B show another example automation device / system or automated resource (“combine-and-separate device”) that may be controlled by aspects described herein. Theautomation device / system or automated resource of FIGS. 5A and 5B is an example of a containerprocessing automation device / system or an outbound automation device / system or an inbound automation system, which may be used in block 6 of FIG. 1. A combination conveyor unit 570 transfers a combined delivery container and storage container towards a system 564 as shown in FIG. 5A. When being transferred to the system 564, a clamping device 574 is in the open configuration so as to allow the combined delivery container and storage container to be transferred onto the merge conveyor unit 572 in the raised position. Once on the merge conveyor unit 572 and prior to the clamping device 574 clamping against the at least one side wall of the delivery container, the combined delivery container and storage container (a storage container in which the delivery container is placed) is lifted into engagement with alignment mechanism 586 to correctly position the delivery container into alignment with clamps of the clamping device 574. Once the delivery container is correctly positioned relative to the clamping device 574, the lifting device lowers the merge conveyor unit 572 to disengage the combined delivery container and storage container from the alignment mechanism 586 as shown in FIG. 5B. Once the delivery container is secured by the clamps, the lifting device lowers the merge / separation conveyor unit 572 to separate the storage container from the delivery container. Once in the lowered position, the empty storage container is transported to a storage container station 558. The delivery container clamped by the clamping device is transported to a delivery container station 560. It will be appreciated that the process can be reversed to combine the storage and delivery containers. It will be appreciated that the automation device / system of FIGS. 5A and 5B is under the control of a master controller.

[0074] FIG. 6 is another example automation device / system or automated resource that may be controlled by aspects described herein. The automation device / system of FIG. 6 is an example of a container-processing automation device / system or an outbound automation device / system or an inbound automation device / system, which may be used in block 6 of FIG. 1. A container handler 300 is supported from gantry frame 200. The system further comprises one or more conveyors 500 and one or more loading frames 600. In use, delivery containers 400 will be filled with items at a picking station, for example as discussed above with reference to FIGS. 1 to 4. After the delivery containers have been filled at the picking station then they may be routed to conveyors 500 such that they can be loaded into a vehicle and the contents of the containers can be delivered to a customer. It should be understood that a typical order may comprise a pluralityof delivery containers that will be coordinated for loading onto the same vehicle in a manner that enables efficient unloading and delivery of each order.

[0075] The automation device / system of FIG. 6 enables a delivery container to be automatically received from conveyor 500 and loaded into a container handler 300. The container handler 300 can then be moved on the gantry frame to align itself with an appropriate aperture of one of the loading frames 600 such that the delivery container can be loaded onto the loading frame. Once the loading frame has received all of the delivery containers, it may be transported to a delivery vehicle, using an AMR or AVG for example. It will be appreciated that the automation device / system of FIG. 6 may be under the control of a master controller.

[0076] FIG. 7 shows a method 700 of generating an operational plan for automation de vice / sy stems, such as an ASRS, according to aspects of the invention. The ASRS may include one or more of the automation devices / systems described above.

[0077] In step 710, a forecast of customer orders is generated. The forecast may be for a particular period of time, such as a day. The forecast breaks down the customer order by category, such as into LLTO and SLTO orders. The forecast may have an expected delivery time for each customer order and the items of each customer order. Further, the forecast may include delivery locations for the customer orders. This step may be performed, for example, using an appropriate time series model, such as a machine learning model (e.g., neural network or transformer model) trained to perform time series forecasting. It will be appreciated that a modular approach may be used and step 710 may be performed using different modules that use respective forecasting methods / techniques. That is, any suitable mathematical model may be used for step 710.

[0078] After the number and category of orders has been generated, step 720 can generate a route profile for a plurality of vehicles to deliver the predicted LLTO and SLTO orders on time. A route profile may indicate a plurality of routes for a respective plurality of delivery vehicles. The route profile may show timings for each delivery vehicle. The route profile may show the order category breakdown for each delivery vehicle. In one example, a delivery vehicle may have LLTOs only in a given order. In another example, a delivery vehicle may have SLTOs only in a given order. In yet another example, a delivery vehicle may have a mixture of LLTOs and SLTOs in a given order. The route profile is thus optimized to ensure that the routes allow all of the orders, regardless of category, to be delivered on time. Accordingly, step 720 may create specific routesoptimized for SLTOs. Step 720 may be performed using an appropriate method, such as a Fourier transform method, and or appropriate artificial intelligence model. It will be appreciated that a modular approach may be used and step 720 may be performed using different modules that use respective methods / techniques. That is, any suitable mathematical model may be used for step 720.

[0079] After step 720 has generated a route profile, step 730 determines the dispatch times for each route of the route profile. This may involve determining what time each route should dispatch to ensure that the expected delivery times for all orders are met. As part of step 730, a configuration of the ASRS is modelled / simulated to determine whether all containers for all of the orders can be processed by the ASRS to meet the dispatch times required to ensure all of the orders meet expected delivery times. The simulation may be based on the current or projected resources available to the ASRS, such as number of automation devices / resources and / or pickers and / or vehicles available and respective throughput rates. The order of processing containers may be prioritized according to the order dispatch times. Put another away, step 730 works backwards from the route profile generated by step 720 to see if it is feasible for ASRS to process all orders needed by the route profile on time. Step 730 may be performed using an appropriate method, such as a discrete event model, and / or an appropriate artificial intelligence model. It will be appreciated that a modular approach may be used and step 730 may be performed using different modules that use respective forecasting methods / techniques. That is, any suitable mathematical model may be used for step 730.

[0080] The model / simulation used in step 730 may determine that the dispatch times are not possible due to ASRS constraints, such as lack of resources (e.g., automation resources and / or throughput rates). For example, during peak times, the ASRS may not be able to process the containers at a rate to ensure that all orders are ready for a respective delivery vehicle to dispatch on time. That is, step 730 is used to determine the earliest time each route can dispatch based on the forecasted demand. As shown by step 735, if all of the earliest dispatch times are earlier than the required respective dispatch times, then the method can proceed to step 740. Alternatively, if any of the earliest dispatch times are later than required respective dispatch times, then an iteration of step 720 may occur, as shown in FIG. 7. This may occur during peak times when maximum throughput of the ASRS is reached. Accordingly, step 720 may generate an updated routing profile to change the previously generated order category breakdown for each delivery vehicle. In oneexample, the SLTO only orders and / or mixed orders may be moved to later delivery slots away from peak times. That is the LLTOs are prioritized to meet expected delivery times. Steps 730 and 720 are iteratively run until it is determined that all of the earliest dispatch times are earlier than the required respective dispatch times, at which point the method can proceed to step 740.

[0081] As part of step 730, once it determined that all of the earliest dispatch times are earlier than the required respective dispatch times, the finalization times for each of the orders can be determined. The finalization times determine the respective first and second cut-points for each of the LLTOs and SLTOs. That is, a customer who places a LLTO or STLO will be able to do so right up to the first and second cut-off points respectively for an expected delivery slot. Put another way, provided the orders are received by respective cut-off times, the ASRS will be able to process the containers to meet the expected delivery times according to the route profile.

[0082] In step 740, an operational plan for the ASRS is generated. The operational plan may provide a detailed breakdown for how each automated resource available to the ASRS should prioritize processing of containers. The operational plan may account for a margin of error in the ASRS and assume that less than 100% of the automated resources that could be made available are in fact made available. For example, it may be assumed that only 80% of the resources can in fact be made available. It will be appreciated that the margin of error can be set to any suitable value. In effect, the containers are processed in a particular sequence with respective timings using a required number of automated resources to ensure that the orders for each route of the route profile dispatch on time. It will be appreciated that the number of resources in use may dynamically vary with time according to the forecasted demand. Thus, considering FIG.l, all of automated resources including the inbound automation devices / systems, and / or container processing automation devices / systems, and / or outbound automation devices / systems and / or associated resources such as pickers and / or handlers may be choreographed or orchestrated to process the containers for all orders to deliver at expected times. Step 740 will also account for customer orders requiring items from a combination of ambient, chilled, and frozen. Step 740 may use the results of the modelling / simulation of step 720 to generate the plan. Generating the operational plan may include generating a control signal. The control signal may result in the operational plan or parts thereof being displayed on at least one visual display unit within the ASRS. Further, the control signal may be received by a master controller, which can process the control signal to directly control the automated resources according to the operational plan. That is, each of the automationresources processing containers under control of a master controller, according to the operational plan.

[0083] The operational plan comprises control instructions that scale the operation of the automated resources described above. Scaling can be achieved in a number of ways depending on the type of automated resource, with each measure implemented as an alternative to, or in addition to, other measures, and each configured to improve throughput, reduce energy consumption, and extend equipment lifetime.

[0084] The controller can scale parameters of the load-handling devices in accordance with forecasted demand. One alternative involves increasing maximum travel speed under high demand and reducing maximum travel speed under low demand. Another alternative, or an additional measure, is to scale acceleration and deceleration profiles, with higher acceleration used during peaks to shorten cycle times and lower acceleration used during troughs to reduce mechanical stress and energy draw. As a further alternative or addition, scaling can be achieved by adjusting the duty cycle of the devices. Duty cycle refers to the proportion of time a device’s traction motors, lifting assemblies, and communication systems are actively powered relative to total available operating time. During high demand, duty cycle can be increased, with devices operated continuously or near-continuously, while during low demand duty cycle can be reduced by inserting scheduled idle intervals between moves. Duty cycle scaling can be applied to each device individually, or alternatively to groups of devices, such that some operate at higher duty cycle and others remain in standby. In another alternative or in addition, the number of active devices can be scaled, with surplus devices routed to standby or charging when not required.

[0085] The extent of the grid structure used for container movements can also be scaled. One option is to restrict load-handling activity to a smaller grid region under low demand to reduce travel distance. Alternatively or additionally, grid regions can be expanded under high demand to distribute traffic and prevent congestion.

[0086] The size and configuration of buffer regions may also be scaled. One option is to increase buffer space for delivery containers ahead of a forecasted surge in SLTOs. For example, the LLTOs could be picked and stored in advance of the surge to allow the ASRS to focus on the SLTOs. Alternatively or additionally, buffer allocation may be reduced under low demand, with freed capacity reassigned from delivery to storage containers. To the extent the use of bufferregions is reduced, the load-handling devices will travel less distance overall and have fewer container lifting / raising operations thus saving energy.

[0087] For automated resources with onboard batteries, such as the load-handling devices of FIG. 3 and the autonomous mobile robots or automated guided vehicles used for moving delivery frames in FIG. 6, scaling can be achieved in several ways. As one alternative, charging schedules are adapted so that during low demand more devices are directed to charging stations. Alternatively or additionally, charging may be staggered or deferred during high demand so that devices remain in service. As another alternative or in addition, power consumption modes can be scaled: in low- power mode, devices run with reduced acceleration and lifting speeds, while in high-performance mode devices operate at maximum settings. In another alternative or addition, task allocation is charge-aware, with high-charge devices assigned heavier duty cycles and low-charge devices assigned lighter tasks or charging.

[0088] The number and utilisation of robotic picking stations, such as that in FIG. 4, can also be scaled. As one option, fewer robotic picking stations are active during low demand and more are active during peaks. Alternatively or in addition, utilisation per robotic picking station is varied: robotic picking stations may process fewer containers under light demand or continuous task streams under heavy demand. Another alternative or addition involves distributing tasks across multiple robotic picking stations to balance workload and prevent bottlenecks. In further alternatives or additions, container assignments can be prioritised such that urgent orders are directed to selected stations, while less urgent tasks are delayed.

[0089] For combine-and-separate devices, such as that in FIGS. 5A & 5B, scaling can be achieved by varying how many devices are active concurrently. One option is to operate a single device during low demand and multiple devices during high demand. Alternatively or additionally, idle devices can be powered down, with conveyors and actuators inactive until needed.

[0090] Scaling may also apply to container handlers of FIG.6 that place delivery containers into loading frames. The number of active container handlers and their service rates may be varied dependent on demand. During high demand, more container handlers can be activated and / or operated at higher service rates; during low demand, fewer container handlers are active and / or operate at slower rates to reduce energy consumption and mechanical wear. The number of loadingframes staged for dispatch may also be adjusted, with more frames prepared under high demand and fewer staged when demand is low.

[0091] Scaling is also applied to scheduling at interfaces between temperature-controlled zones, such as conveyors, lifts, or transfer stations that move containers from one zone to another. In operation, load-handling devices within each respective zone interact with the containers at the zone side of the interface: in the source zone, one or more load-handling devices deliver containers to the interface equipment; in the destination zone, one or more load-handling devices collect containers from the interface equipment. This arrangement ensures that the load-handling devices themselves remain within their designated thermal environment, avoiding condensation risks, while containers are passed across zones by the dedicated interface equipment. For example, a zone-to-zone interface may connect a chilled storage zone to either an ambient storage zone or a frozen storage zone, with respective load-handling devices transferring containers to and from the conveyor or lift.

[0092] Where an interface is protected by a door, gateway, or flap barrier, the duration for which that barrier remains open (“open time”) is also managed as part of scaling. Longer cumulative open times increase the risk of heat ingress and moisture transfer between zones.

[0093] Another alternative, or an additional measure, is to apply batching of transfers. In this approach, the controller groups container transfers into short time windows. A batch of containers is moved across the interface in sequence, during which the door or gateway remains open, followed by a defined recovery period in which the barrier is closed or idle. This reduces cumulative open time which reduces the risk of heat ingress and moisture transfer between zones.

[0094] As a further alternative or addition, retrieval sequencing can be adjusted so that frozen items are retrieved as late as possible relative to dispatch, reducing dwell time outside controlled storage, while chilled and ambient items are sequenced earlier or later as appropriate. Another alternative or addition involves scaling staging buffers adjacent to the interfaces, increasing staging space under high demand and reducing it when order volumes are low.

[0095] Scaling can also take account of maintenance status. Diagnostic data such as motor current, vibration levels, cycle counts, and battery health of automation resources is processed alongside demand forecasts. One option is to rest or reduce the workload of degraded automation resources when demand is low, while healthy automation resources cover the reduced volume.Alternatively or in addition, degraded automation resources can still be used during high demand but are restricted to lighter or shorter tasks, while healthy automation resources handle heavier tasks. In further alternatives or additions, task allocation is continuously adjusted as diagnostics change, balancing throughput requirements with automation resource health.

[0096] The computation and updating of scaling is carried out by the master controller as part of operational plan generation. Forecasted demand values are processed together with measurable performance data of the automation resources. Such performance data includes:

[0097] throughput rates, referring to the number of containers per unit time that can be moved by load-handling devices, processed at picking stations or robotic picking stations, or transferred through zone-to-zone interfaces; and / or

[0098] duty cycles, referring to the proportion of time that load-handling devices, conveyors, or actuators are actively powered relative to total available operating time; and / or

[0099] charge states, referring to the state of charge of batteries within load-handling devices and autonomous mobile robots or automated guided vehicles; and / or

[0100] buffer occupancy levels, referring to the number of storage or delivery containers present within buffer regions adjacent to picking, loading, or interface equipment; and / or

[0101] interface telemetry, referring to signals from conveyors, lifts, or doors at temperature zone-to-zone interfaces, including queue length, cycle count, and barrier open / close duration; and / or

[0102] diagnostics, referring to sensor outputs such as motor current draw, vibration levels, cycle counts, or other indicators of automation resource health.

[0103] From these inputs, the controller determines scaling factors for each automation resource. For example, an increase in SLTOs may trigger activation of additional load-handling devices, an increase in duty cycle, expansion of buffers, adjustment of micro-batching parameters, and activation of additional robotic picking stations. For container handlers that load delivery containers into loading frames, scaling factors determine both how many handlers are placed into service and the service rate at which they operate, such that throughput is increased under high demand and energy use and wear are reduced under low demand. A downturn in demand generally may reduce the number of active automation resources, lower duty cycles, decrease buffer space,increase charging activity, reduce the service rate of container handlers, and power down surplus automation resources. Scaling factors are recalculated whenever forecasts are updated or when real-time demand deviates from predictions. Updated control instructions are transmitted to the automation resources so that variables such as load-handling device speeds, duty cycle assignments, charging schedules, buffer allocations, picking throughput, interface transfer rates, container handler utilisation and service rate, and frame loading assignments are continuously adapted to current operating requirements. The scaling may be targeted to specific automation resources in dependence on the type and state of performance data received from those automation resources, including throughput rates, duty cycles, charge states, buffer occupancy levels, interface telemetry such as queue length and door open time at transfer points, and diagnostic data such as motor current, vibration levels, and battery health.

[0104] In optional step 750, when the actual processing of containers is due to occur, the ASRS may be operated according to the operational plan. Additionally or alternatively, one or more containers may be provided to at least one vehicle prior to a dispatch time of the vehicle according to a route profile associated with the vehicle. Step 750 may involve using a control signal generated in step 740.

[0105] Whilst the method of FIG.7 considers both LLTOs and SLTOs, it will be appreciated that either LLTOs or SLTOs can be considered alone. That is, the method of FIG.7 can be used to generate an operational plane for an ASRS that processes LLTOs only or SLTOs only, as well as other categories of orders.

[0106] FIG. 8A shows an example route profile 800 generated by the method of FIG. 7. In this example, the individual routes are separated vertically on the Y-axis. The X-axis represents increasing time from left to right. Each of the routes is represented by a horizontal bar 810 and occupies a respective space in the Y-axis. The left- most point of a horizontal bar represents dispatch time (i.e. a start time of the route), whereas the right-most point represents the end of the route. Therefore, the further a horizontal bar is the right, the later that route starts. As shown in FIG. 8A, each of the routes consists of LLTOs only, or SLTOs only, or a mixture of LLTOs and SLTOs, as indicated by the legend 820 (where, in this example, same day is synonymous with SLTO, and mixed is synonymous with a mixture of LLTOs and SLTOs). The finalization time for each of the routes is shown by 830. This finalization time for each route ensures that the ASRScan process the required containers for all orders on a route to dispatch by the start time of that route. This means the control signal of step 740 can communicate with a server to control finalization times on an application or webshop served by the ASRS.

[0107] FIG. 8B shows a further example route profile 850 generated by the method of FIG. 7. In this example, the individual routes are separated vertically on the Y-axis. The X-axis represents increasing time from left to right. Each of the routes is represented by a horizontal bar 860 and occupies a respective space in the Y-axis. The left- most point of a horizontal bar represents dispatch time (i.e. a start time of the route), whereas the right-most point represents the end of the route. Therefore, the further a horizontal bar is the right, the later that route starts. As shown in FIG. 8B, the exact breakdown of each of the routes in terms of LLTOs and SLTOs, as indicated by 870, may be shown. That is, the number of LLTOs and SLTOs and order of delivery thereof may be shown. The orders in this example are delivered as indicated when moving left to right along the X-axis.

[0108] FIG. 9A show an operational plan 900 for the ASRS resources to execute the route profile of FIGS. 8A and 8B. In this example, the individual routes are separated vertically on the Y-axis. The X-axis represents increasing time from left to right. A route profile 910 similar to that described above in respect of FIGS. 8A and 8B may be shown as part of the operational plan. In addition to the route profile 910, the timings 930 for when different resources within the ASRS should be used is also shown. The resources used in this example are shown in 930. A more detailed view 950 of section 940 of FIG. 9A is shown in FIG. 9B. Here, the detailed timings for when containers for a respective route should be processed by the automation resources available to the ASRS, is shown. The start of the routes are shown by 980. In this example, the timings for the ASRS receiving the orders (denoted by the words “allocation” and “download” in this example), certain container processing resources (denoted by the word “pick”, which in this example is further broken down by ambient, chill, and frozen), and certain outbound resources (denoted by the word “frameload” also in this example broken down by ambient and chill, and “van load”) are shown. During the timings for a given resource, the containers for an order to be dispatched on the corresponding route should be processed using the given resource. When all of the resources are controlled according to the operational plan, the ASRS can react to fluctuating demand as efficiently as possible whilst meeting expected delivery times.

[0109] FIG. 10 shows a further resource allocation 1000 of the operational plan. In this example, the individual resources are separated vertically on the Y-axis. The X-axis represents increasing time from left to right. An individual resource, such as 1010, may be allocated to a specific task from a start time to end time, as indicated by the left to right direction. Each of the resources may be allocated a specific task as shown by 1020 at a given time. However, each individual resource may be re-allocated to a different task at different time. That is, one or more of the individual resources may perform one or more specific tasks over time. In this example, the tasks may be container processing tasks, such as picking at given temperature (e.g. ambient, chilled, frozen) or outbound tasks, denoted in this example by the words “Frameload” and “Vanload.” A resource according to this example may be an automation device / system, such as a robotic manipulator, AMR, or AVR, or a picker / operator. It should be appreciated that the information provided from the examples shown in FIGS. 8-10 can be used to control the ASRS as described above in respect of step 750 of FIG. 7.

[0110] It should also be appreciated information provided shown in FIGS. 8-10 are only examples of the information that may be provided. It is also possible to provide expected customer demand by time in terms of LLTOs and SLTOs, and / or the lead time for each order, and / or the number of delivery vehicles needed and when delivery vehicles are needed, and / or the number of delivery vehicle drivers needed and when delivery vehicle drivers are needed, and / or the number of containers in a buffer areas awaiting subsequent processing (e.g. the grid storage structure and / or a van dispatch area), and / or a breakdown of ambient, chill, and frozen containers processed by time, and / or number of containers loaded onto a delivery frame by time, and / or number of delivery vehicles loaded by time, and / or number of resources, such as a picker, and resource deployment by time. All of the above examples may be presented visually using, for example, a graph or histogram.

[0111] FIG. 11 shows one example 1100 of how to generate an operational plan for a given ASRS configuration.

[0112] A given ASRS may serve a geographic area for deliveries. Thus, block 1110 is used to forecast / predict demand for a geographic area for a period of time, such as a given day or date, for both LLTOs and STLOs. Block 1110 may also forecast / predict both delivery times and delivery locations for the orders. Block 1110 may be performed using an appropriate time series model,such as a machine learning time series forecasting or a deep learning transformer. It will be appreciated that a modular approach may be used and block 1110 may be performed using different modules that use respective forecasting methods / techniques. That is, any suitable mathematical model may be used for block 1110. In any case, block 1110 can process one or more of the inputs shown in Table 1 to generate a forecasted demand.

[0113] Modelling block 1120 then uses the outputs of block 1110 to generate a route profile, for a plurality of vehicles to deliver the predicted / forecasted LLTO and SLTO orders on time as described above in step 720 of FIG. 7. Block 1120 may be performed using an appropriate method, such as a Fourier transform method, and or appropriate artificial intelligence model. It will be appreciated that a modular approach may be used and block 1120 may be performed using different modules that use respective methods / techniques. That is, any suitable mathematical model may be used for block 1120.

[0114] Block 1130 is used to indicate a specific configuration of the ASRS and can receive one or more the inputs shown below in Table 2. These inputs may be presented as outputs after received by block 1120.TABLE 2

[0115] Block 1140 then models the ASRS, based on the outputs provide by blocks 1110 and 1130 to determine whether an initial route profile can be achieved by the ASRS in terms of meeting the required dispatch times. Block 1140 may be performed using an appropriate method, such as a discrete event model, and / or an appropriate artificial intelligence model. It will be appreciated that a modular approach may be used and block 1140 may be performed using different modules that use respective forecasting methods / techniques. That is, any suitable mathematical model may be used for block 1140.

[0116] As shown in FIG. 11, and described above in relation to the method of FIG. 7, an iterative process may be used to ensure that the forecasted / predicted demand can be met by the most recent (i.e. last) route profile generated by block 1120. The iterative loop runs and updates the route profile until it is determined that the ASRS can meet the forecasted / predicted demand in the most efficient way possible.

[0117] Then, the required operational plan to execute the now optimized route profile, is generated in block 1140. From the route profile, an operational plan for the ASRS required to effect the route profile can be produced using block 1150. Put another way, blocks 1120 and 1140 verify that the available resources in the ASRS can be coordinated and / or orchestrated to effect the optimized route profile. The specific ASRS operational plan configured for coordination and / or orchestration of the ASRS is then output using block 1150.

[0118] FIG. 12 shows a user interface 1200 for generating and outputting the operational plan, as per block 1150 in FIG. 11. The user interface may be provided using any suitable computing and display device.

[0119] In section 1210 of the user interface, a number of input elements 1220a-h can be updated to provide the inputs used by blocks 1110 and 1130 of FIG. 11 for example. The inputs can be used by the aspects described above to generate an operational plan as shown in section 1230. In this example, a graphical representation 1231 for a particular aspect of the operational plan for the ASRS is shown. User elements 1232 and 1233 can be used to change the particular aspect currently displayed. Assuming the operational plan is implemented, associated performance metrics are shown in section 1240. Elements 1250a-h correspond to respective metrics such as overall efficiency, individual efficiency, volumes, and order throughput. Example metrics include but are not limited to lead times, lateness, productivity, a number of containers currently in the grid storage structure, a number of containers in buffer areas, revenue / economic metrics, and / or ASRS use as a percentage of maximum capacity.

[0120] FIG. 13 depicts a processing system 1300 for implementing aspects described. In some aspects, processing system 1300 implement logical elements from FIGS. 7 and 11.

[0121] In this example, processing system 1300 includes one or more one or more processors 1302 configured to retrieve and execute instructions stored in one or more memories 1306, which may be volatile memory, such as a random access memory (RAM), or a nonvolatile memory, such as nonvolatile random access memory (NVRAM), or the like. In this example, the one or more memories 1306 include a forecasting component 1350, an ASRS inputs component 1351, a modelling component 1352, a route profile component 1353, and a plan generating component 1354.

[0122] The forecasting component 1350 may be configured to perform at least step 710 of method 700 described with reference to FIG. 7 and / or at least block 1110 of FIG. 11.

[0123] The ASRS inputs component 1351 may be configured to perform and / or control at least step 720 and 730 of method 700 described with reference to FIG. 7 and / or at least block 1130 of FIG. 11.

[0124] The modelling component 1352 may be configured to perform at least step 720 and 730 of method 700 described with reference to FIG. 7 and / or at least block 1140 of FIG. 11.

[0125] The route profile component 1353 may be configured to perform at least step 720 and 730 of method 700 described with reference to FIG. 7 and / or at least block 1120 of FIG. 11.

[0126] The plan generating component 1354 may be configured to perform at least step 740 and 750 of method 700 described with reference to FIG. 7 and / or at least block 1150 of FIG. 11.

[0127] The one or more memories 1306 may include various additional components or data useful for performing described methods in accordance with presently described aspects.

[0128] Instructions 1330 may generally implement any of components 1350-1354 for processing by the one or more processors 602.

[0129] Processing system 1300 may further include a graphics processing unit (GPU) 1308 that is operatively connected to the one or more processors 1302 and to the one or more memories 1306 to offload relevant data from the one or more processors 1302 and process data in parallel with the one or more processors 1302. Processing system 1300 may further include a video display 1316 connected by a video interface 1310, and various input / output devices such as a keyboard 1318, mouse 1320, and disk drive or solid state drive 1322 connected by an I / O interface 1312. In a known manner, the mouse 1320 may be configured to control movement of a cursor in a video display 1316, and to operate various graphical user interface (GUI) controls appearing in the video display 1316 with a mouse button. The disk drive or solid state drive 1322 may be configured to accept computer readable media 1324.

[0130] The processing system 1300 may send and receive data over a network via a network interface 1304, allowing the processing system 1300 to communicate with other suitably configured data processing systems, applications, or devices. One example use of the network interface to communicate finalization or cut-off times to a server processing customer orders. Network interface 604 may generally provide data access to any sort of data network, including personal area networks (PANs), local area networks (LANs), wide area networks (WANs), the Internet, and the like.

[0131] Processing system 1300, which may be an example of a master controller described above, may be implemented in various ways. For example, processing system 1300 may be implemented within on-site, remote, or cloud-based processing equipment.

[0132] It will be appreciated by those skilled in the art that other variations of the embodiments described herein may also be practiced without departing from the scope of the invention. Other modifications are therefore possible.Example Aspects

[0133] The following is a list of example aspects which may be or are claimed.

[0134] Aspect 1. An automated storage and retrieval system (“ASRS”), comprising: a first set of tracks extending in a first direction and a second set of tracks extending in a second direction which is transverse to the first direction, wherein the first and second sets of tracks are in a substantially horizontal plane to form a grid comprising a plurality of grid spaces to form a plurality of vertical storage locations beneath the grid for containers to be guided by uprights in a vertical direction through the plurality of grid spaces, wherein the grid comprises, at least one inbound automation device for containers to enter the grid and / or at least one outbound automation device for containers to exit the grid and load onto a plurality of vehicles; a plurality of load handling devices, wherein each load-handling device is configured to move along the first and / or second sets of tracks, and lift a container from beneath the grid and / or lower a container beneath the grid; and one or more processors configured to: control the load-handling devices to move containers to and / or from the storage locations, the at least one inbound area and / or the at least one exit area to execute a route profile for the plurality of delivery vehicles.

[0135] Aspect 2. The ASRS of Aspect 1, wherein the containers handled directly by the loadhandling device are of a first type, wherein a first subset of the containers store at least one item and a second subset of the containers receive a delivery container of a second type therein, wherein the one or more processors are configured to control each of the plurality of load handling devices to: perform either a picking task or an outbound task to execute the route profile for the plurality of delivery vehicles, wherein a picking task comprises moving the first subset of containers and second subset of containers to and / or from at least one picking station to move items from the first subset of containers to the delivery containers of the second subset of containers, wherein anoutbound task comprises moving a second subset of container with a filled delivery container to the at least one outbound automation device.

[0136] Aspect 3. The ASRS of Aspect 2, wherein the outbound automation comprises at least one of: a combine and separate device configured to place a delivery container of the second type within a container of the first type and remove a delivery container of the second type from a container of the first type; and / or a delivery loading frame device configure to load a plurality of delivery containers for a respective at least one storage frame for a respective vehicle; and wherein the one or more processors are configured to: control the combine and separate device and / or the delivery loading frame to execute the route profile for the plurality of delivery vehicles.

[0137] Aspect 4. The ASRS of any one of Aspects 2-3, wherein the at least one picking station comprises a robotic manipulator to move items from the first subset of containers to the delivery containers of the second subset of containers, wherein the robotic manipulator is operable on or above the first and / or second sets of tracks, wherein the one or more processors are configured to control the robotic manipulator to execute the route profile for the plurality of delivery vehicles.

[0138] Aspect 5. The ASRS of any one of Aspects 2-4, wherein the at least one picking station is located at adjacent the grid and provides an area for an operator to move items from the first subset of containers to the delivery containers of the second subset of containers, wherein the one or more processors are configured to control the picking station to execute the route profile for the plurality of delivery vehicles.

[0139] Aspect 6. The ASRS of any one of Aspects 1-5, further comprising at least one buffer area, wherein the one or more processors are configured to control each of the plurality of load handling devices to move containers to and / or form the buffer area to execute the route profile.

[0140] Aspect ?. The ASRS of any one of Aspects 1-6, wherein the one or more processors are configured to generate the route profile by: generate a forecast for orders to be fulfilled by the ASRS, wherein the orders comprise one or more of short lead time orders, (“SLTOs”), and long lead time orders, (“LLTOs”); generate a route profile comprising a plurality of routes, each associated with a vehicle, to deliver the forecasted SLTOs short lead time orders and LLTOs on time; and wherein the one or more processors are configured to: determine a dispatch time for each route of the plurality of routes; and generate an operational plan for the ASRS to process the ordersfor each respective route of the plurality of routes to achieve a dispatch time associated with the respective route.

[0141] Aspect 8. The ASRS of Aspect 7, wherein the one or more processors are configured to generate the route profile with a breakdown of one or more of SLTOs and LLTOs on each delivery vehicle and associated delivery times.

[0142] Aspect 9. The ASRS of any one of Aspects 7-8, wherein the one or more processors are configured to determine a dispatch time for each route of the route profile by modelling the ASRS to determine an earliest possible dispatch time for each route of the route profile when processing orders by the ASRS.

[0143] Aspect 10. The ASRS of any one of Aspects 7-9, wherein the one or more processors are configured to determine a dispatch time for each route of the route profile by modelling the ASRS to determine an earliest possible dispatch time for each route of the route profile when processing orders by the ASRS.

[0144] Aspect 11. The ASRS of any one of Aspects 7-10, wherein the one or more processors are configured to iteratively generate the route profile until the earliest possible dispatch time for each route of the route profile is earlier than a required dispatch time for each route of the route profile.

[0145] Aspect 12. The ASRS of any one of Aspects 7-11 wherein the one or more processors are configured to generate the operational plan by outputting instructions for the operational plan, wherein outputting instructions comprise outputting a control signal for the ASRS.

[0146] Aspect 13. The ASRS of any one of Aspects 7-12 wherein the one or more processors are configured to generate the operational plan by outputting instructions for the operational plan, wherein outputting instructions comprise outputting a control signal for the ASRS.

[0147] Aspect 14. The ASRS of Aspect 13 wherein the one or more processors are configured to output the control signal to display a timing plan for the load-handling devices, and / or the at least one inbound automation device, and / or the at least one outbound automation device.

[0148] Aspect 15. The ASRS of any one of Aspects 7-13, wherein the one or more processors are configured to generate the operational plan by setting a finalization time for confirming respective orders for processing by the ASRS.

[0149] Aspect 16. The ASRS of any one of Aspects 7-13, wherein the one or more processors are configured to generate the operational plan based on a predetermined time.

[0150] Aspect 17. A computer- implemented method of operating an automated storage and retrieval system (“ASRS”), the method comprising: generating a forecast for orders to be fulfdled by the ASRS, wherein the orders comprise one or more of short lead time orders (“SLTOs”) and long lead time orders (“LLTOs”); generating a route profde comprising a plurality of routes, each associated with a vehicle, to deliver the forecasted SLTOs and LLTOs on time; determining a dispatch time for each route of the plurality of routes; and generating an operational plan for the ASRS configured to process the orders for each respective route of the plurality of routes to achieve a dispatch time associated with the respective route.

[0151] Aspect 18. The computer-implemented method of Aspect 17, wherein: the ASRS comprises at least one automation resource to process containers for the orders, and the method further comprises operating the ASRS according to the operational plan.

[0152] Aspect 19. The computer- implemented method of any one of Aspects 17 or 18, wherein: the ASRS comprises at least one automation resource to process containers for the orders, and the method further comprises providing one or more containers to at least one vehicle prior to a dispatch time of the vehicle according to a route profile associated with the vehicle.

[0153] Aspect 20. The computer- implemented method of Aspect 19, wherein the at least one automation resource comprises one or more of: an inbound automation device / system configured to introduce items to the ASRS; a container processing automation device system configured to move items between containers; and an outbound processing automation device / system configured to effect delivery of the orders.

[0154] Aspect 21. The computer-implemented method of any one of Aspects 17-20, wherein the forecast comprises a forecasted delivery time and delivery location for each order.

[0155] Aspect 22. The computer-implemented method of any one of Aspects 17-21, wherein the route profile comprises a breakdown of one or more of SLTOs and LLTOs on each delivery vehicle and associated delivery times.

[0156] Aspect 23. The computer-implemented method of any one of Aspects 17-22, wherein determining a dispatch time for each route of the route profile comprises modelling the ASRS todetermine an earliest possible dispatch time for each route of the route profile when processing orders by the ASRS.

[0157] Aspect 24. The computer-implemented method of Aspect 23, further comprising iteratively generating the route profile until the earliest possible dispatch time for each route of the route profile is earlier than a required dispatch time for each route of the route profile.

[0158] Aspect 25. The computer- implemented method of Aspect 24, wherein: generating the operational plan comprises outputting instructions for the operational plan, and outputting instructions comprises outputting a control signal for the ASRS.

[0159] Aspect 26. The computer-implemented method of Aspect 25, wherein the control signal comprises displaying a timing plan for resources available within the ASRS.

[0160] Aspect 27. The computer-implemented method of any one of Aspects 17-26, wherein the generating the operational plan comprises setting a finalization time for confirming respective orders for processing by the ASRS.

[0161] Aspect 28. The computer-implemented method of any one of Aspects 17-27, wherein the method is based on a predetermined time.

[0162] Aspect 29. A computer- implemented method for a user interface, the method comprising: receiving at least one input on the user interface; based on the at least one input: generating a forecast for orders to be fulfilled by an automated storage and retrieval system (“ASRS”), wherein the orders comprise one or more of short lead time orders (“SLTOs”) and long lead time orders (“LLTOs”); generating a route profde comprising a plurality of routes, each associated with a vehicle, to deliver the forecasted SLTOs and LLTOs on time; determining a dispatch time for each route of the plurality of routes; and generating an operational plan for the ASRS configured to process the orders for each respective route of the plurality of routes to achieve a dispatch time associated with the respective route; and wherein the method further comprises: outputting the operational plan on the user interface.

[0163] Aspect 30. A processing system, comprising: one or more memories comprising computer-executable instructions; and one or more processors configured to execute the computerexecutable instructions and cause the processing system to perform a method in accordance with any one of Aspects 17-29.

[0164] Aspect 31. A processing system, comprising means for performing a method in accordance with any one of Aspects 17-29.

[0165] Aspect 32. A non-transitory computer-readable medium comprising computerexecutable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform a method in accordance with any one of Aspects 17-29.

[0166] Aspect 33. A computer program product embodied on a computer-readable storage medium comprising code for performing a method in accordance with any one of Aspects 17-29.Additional Considerations

[0167] The preceding description is provided to enable any person skilled in the art to practice the various embodiments described herein. The examples discussed herein are not limiting of the scope, applicability, or embodiments set forth in the claims. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments. For example, changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For instance, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Also, features described with respect to some examples may be combined in some other examples. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method that is practiced using other structure, functionality, or structure and functionality in addition to, or other than, the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

[0168] As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects.

[0169] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples ofthe same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0170] As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” may include resolving, selecting, choosing, establishing and the like.

[0171] The methods disclosed herein comprise one or more steps or actions for achieving the methods. The method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims. Further, the various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and / or software component(s) and / or module(s), including, but not limited to a circuit, an application specific integrated circuit (ASIC), or processor. Generally, where there are operations illustrated in figures, those operations may have corresponding counterpart means-plus- function components with similar numbering.

[0172] Reference to an element in the singular is not intended to mean only one unless specifically so stated, but rather “one or more.” The subsequent use of a definite article (e.g., “the” or “said”) with an element (e.g., “the processor”) is not intended to invoke a singular meaning (e.g., “only one”) on the element unless otherwise specifically stated. For example, reference to an element (e.g., “a processor,” “a controller,” “a memory,” “a transceiver,” “an antenna,” “the processor,” “the controller,” “the memory,” “the transceiver,” “the antenna,” etc.), unless otherwise specifically stated, should be understood to refer to one or more elements (e.g., “one or more processors,” “one or more controllers,” “one or more memories,” “one more transceivers,” etc.). The terms “set” and “group” are intended to include one or more elements, and may be used interchangeably with “one or more.” Where reference is made to one or more elements performing functions (e.g., steps of a method), one element may perform all functions, or more than one element may collectively perform the functions. When more than one element collectivelyperforms the functions, each function need not be performed by each of those elements (e.g., different functions may be performed by different elements) and / or each function need not be performed in whole by only one element (e.g., different elements may perform different sub- functions of a function). Similarly, where reference is made to one or more elements configured to cause another element (e.g., an apparatus) to perform functions, one element may be configured to cause the other element to perform all functions, or more than one element may collectively be configured to cause the other element to perform the functions.The following claims are not intended to be limited to the embodiments shown herein, but are to be accorded the full scope consistent with the language of the claims. Within a claim, reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. No claim element is to be construed under the provisions of 35 U.S.C. § 112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.” All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented method of operating an automated storage and retrieval system ASRS, the ASRS comprising a first set of parallel rails or tracks and a second set of parallel rails or tracks extending substantially perpendicularly to the first set of rails or tracks in a substantially horizontal plane to form a grid comprising a plurality of grid spaces, one or more load-handling devices, wherein each load-handling device is configured to move along the first and / or second sets of tracks, and lift a container from beneath the grid and / or lower a container beneath the grid, and a controller in communication with the load-handling devices and with further automated resources of the ASRS, the further automated resources comprising one or more of storage-container filling stations, robotic picking stations arranged to receive storage containers and delivery containers from the load-handling devices and to transfer items between the storage containers and the delivery containers, buffer regions for temporary storage of containers, combine-and-separate devices arranged to separate and / or combine delivery containers and storage containers, container handlers configured to place delivery containers into loading frames for vehicle dispatch, loading frames, and autonomous mobile robots or automated guided vehicles configured to move the loading frames, the method comprising: generating a forecast for orders to be fulfilled by the ASRS, wherein the orders comprise one or more of short lead time orders, SLTOs, and long lead time orders, LLTOs; generating a route profile comprising a plurality of routes, each associated with a vehicle, to deliver the forecasted SLTOs and LLTOs on time; determining, by the controller, a dispatch time for each route of the plurality of routes; and generating, by the controller, an operational plan for the ASRS dependent on the forecast and the route profile, the operational plan comprising control instructions that scale operation of the automated resources of the ASRS in accordance with the forecasted demand, the scaling being targeted to specific automated resources dependent on performance data received by the controller from the automated resources, the performance data representing operational characteristics of the automated resources; and operating the ASRS according to the operational plan by executing the scaled control instructions so that the automated resources are adjusted to achieve the dispatch time for each respective route.

2. The computer- implemented method of claim 1, wherein the operational plan comprises scaling one or more operating parameters of the load-handling devices, comprising at least one of travel speed, acceleration, and / or deceleration, dependent on forecasted demand.

3. The computer- implemented method of claim 1, wherein the operational plan comprises scaling a duty cycle of the load-handling devices dependent on forecasted demand, by varying active and idle periods to reduce energy consumption during low demand and increase throughput during high demand.

4. The computer-implemented method of any preceding claim, wherein the operational plan comprises scaling a number of load-handling devices assigned to container-retrieval tasks dependent on forecasted demand, with surplus devices placed into standby or charging when not required.

5. The computer- implemented method of any preceding claim, wherein the operational plan comprises scaling an extent of the grid made available to the load-handling devices dependent on forecasted demand.

6. The computer- implemented method of any preceding claim, wherein the operational plan comprises scaling a size of one or more buffer regions for storage or delivery containers dependent on forecasted demand.

7. The computer- implemented method of any preceding claim, wherein the operational plan comprises scaling charging schedules for battery-powered automated resources dependent on forecasted demand.

8. The computer- implemented method of claim 7, wherein scaling comprises switching said battery-powered automated resources between low-power and high-performance operating modes dependent on forecasted demand.

9. The computer- implemented method of any preceding claim, wherein the operational plan comprises scaling a number of robotic picking stations activated dependent on forecasted demand.

10. The computer-implemented method of claim 9, wherein the operational plan comprises allocating storage and delivery containers among the robotic picking stations to maintain balanced operation and optimise utilisation of the robotic picking stations.

11. The computer- implemented method of any preceding claim, wherein the operational plan comprises scaling operation of one or more combine-and-separate devices such that a number of combine-and-separate devices active concurrently is varied dependent on forecasted demand, and idle devices are powered down when not required.

12. The computer-implemented method of any preceding claim, wherein the operational plan comprises scaling operation of container handlers configured to place delivery containers into loading frames, wherein scaling comprises varying a number of container handlers in use dependent on forecasted demand and / or adjusting a service rate of the container handlers to reduce energy consumption and mechanical wear during low demand or to increase throughput during high demand, and further comprises adjusting a number of loading frames staged for transfer to vehicles.

13. The computer- implemented method of any preceding claim, wherein the operational plan comprises scaling scheduling of container transfers at interfaces between temperature-controlled zones, the transfers being performed by interface equipment that receives containers from loadhandling devices of a source zone and provides containers to load-handling devices of a destination zone.

14. The computer-implemented method of claim 13, wherein scaling comprises applying batching of container transfers at a zone interface by grouping multiple containers into transfer windows followed by recovery intervals during which a door, gateway, or barrier at the interface is closed.

15. The computer- implemented method of any preceding claim, wherein the operational plan comprises scaling workload allocation among automated resources based on maintenance status, such that resources identified as worn or fault-prone are assigned reduced tasks and healthy resources are allocated increased tasks.

16. The computer- implemented method of any preceding claim, wherein scaling factors are computed from forecasted demand and the performance data received by the controller from the automated resources.

17. The computer-implemented method of claim 16, wherein scaling factors are updated whenever forecasts are updated or real-time demand deviates from predictions, and updated control instructions are transmitted to the automated resources so that parameters comprising device speeds, duty-cycle assignments, charging schedules, buffer allocations, picking throughput, interface-transfer rates, container-handler utilisation and service rate, and loadingframe staging are continuously adapted.

18. The computer- implemented method of any preceding claim, wherein the operational plan interlinks automated resources such that storage containers are provided to the grid, the loadhandling devices deliver storage and delivery containers to robotic picking stations that transfer items between the storage containers and the delivery containers, filled delivery containers are directed to buffer regions, then to combine-and-separate devices, and subsequently to container handlers that deliver the filled delivery containers into loading frames for vehicle dispatch and receive empty delivery containers returned from vehicles for reintroduction into the grid, wherein the controller coordinates these operations dependent on the forecasted demand so that all container movements, item transfer, buffering, separation, delivery, return, and re-storage flows are synchronised.

19. The computer- implemented method of any preceding claim, wherein the ASRS comprises at least one automated resource to process containers for the orders, and the method further comprises providing one or more containers to at least one vehicle prior to a dispatch time of the vehicle dependent on a route profile associated with the vehicle.

20. The computer-implemented method of any preceding claim, wherein the forecast comprises a forecasted delivery time and delivery location for each order.

21. The computer- implemented method of any preceding claim, wherein the route profile comprises a breakdown of one or more of short lead-time orders and long lead-time orders on each delivery vehicle and associated delivery times.

22. The computer-implemented method of any preceding claim, wherein determining a dispatch time for each route of the route profile comprises modelling the ASRS to determine an earliest possible dispatch time for each route of the route profile when processing orders by theASRS.

23. The computer- implemented method of claim 22, further comprising iteratively generating the route profile until the earliest possible dispatch time for each route of the route profile is earlier than a required dispatch time for each route of the route profile.

24. The computer-implemented method of any preceding claim, wherein generating the operational plan comprises outputting instructions for the operational plan, and outputting instructions comprises outputting a control signal for the ASRS.

25. The computer- implemented method of claim 24, wherein the control signal comprises displaying a timing plan for automated resources available within the ASRS.

26. The computer- implemented method of any preceding claim, wherein generating the operational plan comprises setting a finalisation time for confirming respective orders for processing by the ASRS.

27. The computer- implemented method of any preceding claim, wherein the method is based on a predetermined time.

28. A computer program comprising instructions which, when executed by a processor, cause the processor to carry out the computer- implemented method of any one of claims 1 to 27.

29. An automated storage and retrieval system comprising: a first set of parallel rails or tracks and a second set of parallel rails or tracks extending substantially perpendicularly to the first set of rails or tracks in a substantially horizontal plane to form a grid comprising a plurality of grid spaces; one or more load-handling devices, wherein each load-handling device is configured to move along the first and / or second sets of tracks, and lift a container from beneath the grid and / or lower a container beneath the grid;further automated resources, the further automated resources comprising one or more of storage-container filling stations, robotic picking stations arranged to receive storage containers and delivery containers from the load-handling devices and to transfer items between the storage containers and the delivery containers, buffer regions for temporary storage of containers retrieved from the grid, combine-and-separate devices arranged to separate and / or combine delivery containers and storage containers, container handlers configured to place delivery containers into loading frames for vehicle dispatch, loading frames, and autonomous mobile robots or automated guided vehicles configured to move the loading frames; and a controller in communication with the load-handling devices and the further automated resources, wherein the controller is configured to carry out the computer-implemented method of any one of claims 1 to 27.

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