Control system for product transport

Abstract

The invention relates to a control system (100) for transporting products from a manufacturer's production site to a destination, comprising: a cargo space means (110) for determining an optimal arrangement of the products in a minimum number of transport vehicles; a route means (120) for determining an optimal first route of fully loaded transport vehicles from the production site to the destination and at least one associated optimal freight forwarder; a cargo space supplement means (130) for determining partially loaded transport vehicles from the transport vehicles loaded with the products and for determining a second route with intermediate destinations up to the destination, wherein the intermediate destinations are determined such that additional products located at the intermediate destinations can be loaded into the partially loaded transport vehicles;a route update means (140) for modifying the first and second routes based on real-time data on the first and second routes and outputting final first and second routes.

Description

[0001] The invention relates to a control system and a computer-implemented method for controlling the transport of products from a manufacturer's production site to a destination, as well as a computer program product.

[0002] Today, more than ever, the logistics industry is at the heart of global economic processes, with the optimization of truck loads and transport routes playing a key role. These aspects are not only crucial for increasing efficiency and reducing costs within supply chains, but also contribute significantly to sustainability and improved customer satisfaction.

[0003] In this context, the application of artificial intelligence (AI) is gaining increasing importance, as it offers the potential to transform traditional processes and set new standards in planning precision and flexibility. Cost and time efficiency in logistics directly impacts the competitiveness of companies. Optimized route and load planning not only reduces direct operating costs but also shortens delivery times, which is invaluable, especially for time-critical shipments.

[0004] Furthermore, in light of growing environmental awareness, the reduction of CO2 emissions in the transport sector is increasingly coming into focus. Thoughtful planning can make a significant contribution here by helping to avoid empty runs and maximize the utilization of transport vehicles. In this dynamic and demanding context, AI proves to be a powerful tool. With its ability to analyze large amounts of data and recognize complex patterns, it enables unprecedented precision and adaptability in route and load planning. AI-supported systems can not only consider historical and current data but also react to changes in real time, thus significantly improving decision-making.

[0005] The integration of AI into logistics processes thus marks a turning point, influencing not only operational efficiency but also the strategic direction of companies. In an environment where flexibility, speed, and sustainability are crucial competitive factors, AI-supported optimization of truck loads and transport routes offers a clear path to meet these demands and actively shape the future of logistics.

[0006] It is an object of the present invention to provide a control system and a computer-implemented method that improve upon at least one or more of the aforementioned disadvantages. In particular, it is an object of the present invention to provide a control system for managing the transport of a manufacturer's products, thereby enabling optimization of the logistics process.

[0007] The task is solved, according to one aspect, by a control system for transporting products from a manufacturer's production site to a destination. The control system includes a load optimization tool, also called a Truck Load Optimizer, for determining the optimal arrangement of the products in a minimal number of transport vehicles. The transport vehicles can be pre-assigned. The control system further includes a routing tool, also called a Full Truck Load Optimizer, for determining an optimal first route for fully loaded transport vehicles from the production site to the destination, and for identifying at least one associated optimal carrier. The control system also includes a load optimization tool, also called a Milkrun Optimizer, for identifying partially loaded transport vehicles from the fully loaded vehicles and for determining a second route with intermediate stops to the destination.The intermediate destinations are determined in such a way that additional products located at these destinations can be loaded into the partially loaded transport vehicles. The control system also includes a route update tool, also known as a route planner, for modifying the first and second routes based on real-time data and outputting final first and second routes. These final first and second routes can be output to the transport vehicles, the freight forwarder(s), and / or the manufacturer.

[0008] The Milkrun Optimizer selects the most suitable route and the (best / cheapest) carrier for that route. Similarly, the Full Truck Load Optimizer performs the same task for fully loaded shipments.

[0009] While this component is technically optional for planning which transports are needed, it is essential for controlling the entire process.

[0010] The proposed control system allows for optimized planning and execution of product transport. First, optimal loading is achieved by minimizing the number of transport vehicles required. This results in fewer vehicles, leading to lower CO2 emissions, reduced costs for drivers and vehicles, and less traffic congestion.

[0011] Since not all transport vehicles are necessarily fully loaded, those with remaining cargo space, and therefore only partially loaded, can pick up additional products along the route from the manufacturer to the destination, provided these products are located along a sensible route. This further reduces environmental impact and optimizes logistics. The additional products can differ, at least partially, from the main products. They can also be from a different manufacturer. Furthermore, the route update tool optimizes the predetermined first and second routes by improving them based on real-time data such as maps, weather, and / or traffic information.

[0012] Spatial information can be provided to the cargo space device and / or the cargo space supplementary device, characterizing the cargo space and loading area of ​​the transport vehicles. Product information can also be provided to the cargo space device and / or the cargo space supplementary device, characterizing the packaging size, dimensions, packaging (e.g., pallets), weight, orientation, and / or storage temperature of the products and / or the ancillary products.

[0013] The control system, including the aforementioned and / or subsequent means, can be a distributed system. The various means can be located in one place, for example, at the manufacturer's site. The means can be comprised of one or more computers. Alternatively, the means can be located in different locations and communicate with each other, for example, via a network. The control system can be implemented, at least partially, in a cloud environment.

[0014] The cargo space management system can be configured to determine the optimal arrangement based on a first optimization problem. This first optimization problem can determine the distribution of products across a minimum number of transport vehicles within a predetermined timeframe to a predetermined destination. Consequently, this first optimization problem can perform several tasks. First, it optimizes the number of transports required for each supplier to deliver all orders on time. Second, it generates precise loading instructions for all packaging units and distributes them to suppliers and carriers. These instructions can then be used by either manual workers or autonomous vehicles to load the transport vehicles.

[0015] This first optimization problem can be a discrete optimization problem with constraints. One property of this problem is an objective function that specifies a planning strategy. This strategy might include optimal cargo space utilization and / or the minimum number of transport vehicles. The constraints can restrict the solution space of the first optimization problem. These constraints can include one or more of the following: a maximum cargo space for the transport vehicles, a maximum transport weight for the transport vehicles, load balancing to ensure stability, and a maximum load capacity for packaging units (e.g., pallets) of the products and / or ancillary products.To perform cargo space optimization, various methods are available that can be applied individually or in combination: integer optimization, (meta-)heuristics and / or reinforcement learning-based optimization.

[0016] The route mean can be designed to determine the optimal first route based on a second optimization problem. This second optimization problem can select the best route and the most suitable freight forwarder for fully loaded transport vehicles. Since the planned transport vehicle is already fully loaded, no intermediate destinations need to be included, which significantly simplifies the problem. This allows for the use of other optimization methods, including an exhaustive search. An associated objective function and one or more constraints can be identical to the objective function and constraints of a third optimization problem described subsequently.

[0017] The cargo space augmentation tool can be configured to determine the partially loaded transport vehicles from those loaded with products and to determine the second route with intermediate destinations to the final destination based on the third optimization problem. The third optimization problem can be configured to plan so-called milkruns. These are routes with multiple intermediate destinations where several partial loads, in this case in the form of additional products from different suppliers, can be combined into a single delivery. The optimal route is selected that visits all suppliers while simultaneously optimizing the selected objective function and fulfilling all constraints. The cargo space augmentation tool can either plan future routes or dynamically add new intermediate destinations to shipments based on tracking information.

[0018] This can be a discrete optimization problem with constraints. The third optimization problem can have an objective function and one or more constraints. The objective function can consist of several components that can be executed individually or together. These components can include: minimum total cost, minimum number of trips, fastest delivery, shortest route, minimum fuel and / or energy consumption, and / or minimum emissions. The constraints can restrict the solution space and allow only valid solutions. The constraints can include a maximum cargo capacity of the transport vehicle, a maximum transport weight for the transport vehicle, delivery times of intermediate destinations and / or the final destination, permitted intermediate destinations along the route, and / or framework agreements with carriers and suppliers.

[0019] To solve this third optimization problem, integer optimization, heuristics and / or reinforcement learning-based optimization can be used.

[0020] The real-time data can include maps, weather data and / or traffic data.

[0021] The route update tool can be configured to modify the first and second optimal routes based on data from a risk assessment tool (as described below) to minimize the impact of potential risks. This allows for better management of freight forwarders' transport fleets. Routes can be adjusted depending on the risk assessment. Several options are available for this: planning detours and / or introducing redundant transport routes for high-traffic routes. The route update tool can also be configured to modify the first and second routes while maintaining intermediate destinations and / or the final destination.

[0022] The cargo space extension device can be configured to modify, in particular add, intermediate destinations to the second route based on tracking information from the ancillary products. Tracking information can include location or position information and / or time information from the ancillary products, characterizing a location and time. The tracking information can also include location or position information and / or time information from the transport vehicles.

[0023] The control system can further include a lead time calculator for determining a time and / or time window at which the products at the manufacturing site and / or the ancillary products at the intermediate destinations are to be loaded into the transport vehicles, and for providing corresponding lead time information. The route calculator and / or the load space calculator can be configured to determine the first and second optimal routes, respectively, based on the lead time information. The lead time calculator can also be configured to determine a time or time window that characterizes when the products and / or ancillary products must be ready for shipment. The lead time calculator can determine the time(s) and / or time window based on additional data sources. These additional data sources can include seasonal data and / or weather data.The pickup information can be further output to the route update tool and used by it. Furthermore, a manufacturer's production plan and delivery instructions can be output to the route update tool and used by it to determine the final routes.

[0024] The control system can further include a preselection tool, also called a route selector, for preselecting predetermined routes for carriers, encompassing the manufacturer's location and the destination, and for providing corresponding preselection information. The route tool and / or the cargo space supplement tool can be configured to determine the first or second route based on the preselection information. The preselection tool can be a mechanism for preselecting possible routes for all carriers. Several approaches can be provided for this purpose, which can be applied individually or in combination: selection from predetermined routes and / or creation of new routes based on map data. Further evaluation criteria and / or constraints can be taken into account: contracts with carriers, seasonal restrictions, regulatory restrictions, a cost assessment, and / or the risk information from the risk assessment tool.The routes selected in this step can include multiple intermediate destinations at suppliers or represent a direct connection between a supplier and the destination. Furthermore, transport times and route length can be determined based on historical data from the freight forwarders.

[0025] The route update device can be configured to output the final first and / or second route to the preselection device to provide a loading plan. The loading plan can specify the manufacturer's location, the destination and / or intermediate destinations, and the respective arrival times of the transport vehicles according to the final first and / or second route. Based on this additional information, the preselection device can then perform the preselection.

[0026] The preselection tool can be further trained to preselect predetermined routes based on map data and / or news related to those routes. The news can describe weather conditions and / or events such as concerts, rallies, or political meetings.

[0027] The control system can further include a route splitter, also known as a route divider, which is designed to divide the routes preselected by the preselection tool into route groups and assign these route groups to respective sub-optimization problems in order to determine one or more optimal routes from the route groups. The route splitter can be a mechanism for decomposing the global optimization problem into independent subproblems with non-overlapping routes. These subproblems can generally be solved much more easily and quickly. Additionally, subsequent optimizations can be performed in parallel to reduce runtime.

[0028] The control system can further include the risk assessment tool, also called a risk analyzer, for determining the risk of the first route, the second route, the final first route, and / or the final second route based on news and / or social media information, and for providing corresponding risk information. The route update tool can be configured to adjust the first and / or second optimal route based on the risk information. The risk assessment tool can analyze the possible routes and perform a risk assessment. Potential risks for all delivery routes can be analyzed in order to adjust them if necessary. Current news and social media channels can be used for information evaluation. Risk analysis is particularly important for time-critical deliveries with long transport routes.

[0029] The control system can further include a travel time calculator, also known as a transport time calculator, for determining travel time from the manufacturer's location to the destination based on historical data of traveled routes and for providing travel time information to the manufacturer, particularly for production planning. From this, delivery schedules can be generated for the manufacturer, which then serve as input for potential production planning.

[0030] The control system may further include a user interface for a user, wherein the user interface is configured to display information relating to one or more of the means of the control system according to the first aspect. The user interface may further be configured to receive user input, wherein the user input comprises one or more control inputs relating to the control system. One or more means of the control system can be controlled by means of these control inputs.

[0031] The proposed control system enables cost optimization. Fuel, driver, vehicle maintenance, and road tolls represent a significant portion of a logistics company's operating costs. Optimized planning minimizes distances traveled and maximizes loading efficiency, directly leading to lower operating costs. Furthermore, optimizing routes and loads results in substantial time savings. The transportation sector is one of the world's largest contributors to CO2 emissions. By optimizing routes and maximizing load efficiency, CO2 emissions can be reduced, contributing to sustainability and environmental protection. Analyzing supply chain risks allows for the proactive development of mitigation strategies to reduce the impact of unintended disruptions.

[0032] According to a second aspect, the task is solved by a computer-implemented method for controlling the transport of products from a manufacturer's production site to a destination. The method comprises: - Determining an optimal arrangement of the products in a minimum number of transport vehicles; - Determining an optimal first route for transport vehicles fully loaded with the products from the place of manufacture to the destination and at least one associated optimal freight forwarder; - Determining partially loaded transport vehicles from the transport vehicles loaded with the products and determining a second route with intermediate destinations up to the destination, wherein the intermediate destinations are determined in such a way that additional products located at the intermediate destinations can be loaded into the partially loaded transport vehicles; - Modifying the first and second routes based on real-time data on the first and second routes and outputting final first and second routes.

[0033] Device features that were implemented with respect to the control system according to the first aspect can be implemented as process features of the process according to the second aspect.

[0034] The task is solved, according to a third aspect, by a computer program product comprising instructions that cause a hardware component of a computer and / or a control system, according to the first aspect, to execute the procedure according to the second aspect when the computer program is loaded onto or executed by the hardware component or control system. The control system and / or one or more means of the control system may include such a hardware component. The hardware component may be a processor. Furthermore, the control system and / or the means may include one or more memories for storing the computer program product. The computer program product may be storable on such a memory.

[0035] Preferred embodiments are explained by way of example with reference to the accompanying figures. These show: Fig. 1. A control system for the transport of products from a manufacturer's production site to a destination; and Fig. 2 a computer-implemented method for controlling transports.

[0036] Fig. Figure 1 shows a control system 100 for transporting products from a manufacturer's production site to a destination. The destination can be a customer's location. The control system 100 can include various means 110-190. Means with a solid outline or border are essential features of the control system 100, while means with a dashed outline or border are optional features. Gray-filled fields represent information. The arrows shown can represent an information flow.

[0037] According to the Fig. Figure 1 shows a manufacturer. The manufacturer is trained to produce the products. The products can be at least partially identical and / or different. Consequently, the products can have at least partially different and / or identical packaging, sizes, weights, and the like. The manufacturer produces the products according to a production plan. Furthermore, the manufacturer defines delivery orders. The delivery orders can be issued to one or more suppliers.

[0038] A forecasting tool 150 of the control system 100 is configured to determine the time at which the products are to be loaded into transport vehicles at the manufacturing site. The production plan and / or delivery orders can be provided to the forecasting tool 150 for this purpose. Furthermore, the forecasting tool is configured to provide corresponding pickup information. This pickup information can characterize the time and thus define a pickup time.

[0039] The pickup information and supplier information characterizing the supplier(s) are provided to a loading unit 110 of the control system 100. The loading unit 110 is designed to determine an optimal arrangement of the products in a minimal number of transport vehicles. The transport vehicles can be provided by the supplier(s). Based on the determined optimal arrangement, the transport vehicles can be loaded with the product. This can result in fully loaded or partially loaded transport vehicles.

[0040] The control system 100 further includes a routing tool 120 for determining an optimal first route for transport vehicles fully loaded with products from the manufacturing location to the destination and at least one associated optimal freight forwarder. Since the transport vehicles are already fully loaded, they can travel directly from the manufacturing location to the destination along the first route.

[0041] However, since partially loaded transport vehicles can also occur, the control system 100 further includes a load space supplement device 130 for determining these partially loaded transport vehicles. Furthermore, the load space supplement device 130 is designed to determine a second route with intermediate destinations leading to the final destination. The intermediate destinations are determined in such a way that additional products located at these destinations can be loaded into the partially loaded transport vehicles. Since the partially loaded transport vehicles travel from the production site to the destination, the remaining load space can be used efficiently for the additional products. This allows the actual number of transport vehicles in freight transport to be further reduced.

[0042] Route manager 120 outputs the first optimal route, and cargo space supplement manager 130 outputs the second optimal route. These are each solved based on predetermined optimization problems. These optimization problems include corresponding objective functions and constraints.

[0043] To determine the most efficient route for each loaded transport vehicle, the control system 100 further includes a route update device 140 for modifying the first and second routes based on real-time data. The route update device 140 is further configured to output final first and second routes, with the final first route being driven by the transport vehicles already fully loaded with the product and the final second route by the partially loaded transport vehicles. This real-time data could, for example, include information on the current traffic situation along the first and second optimal routes. If, for instance, there is a traffic jam on a section of the first or second optimal route, it might be advisable to choose a detour at that time.

[0044] Since the carriers offer various predetermined routes and / or areas, the control system 100 further includes a preselection tool 160 for preselecting the carriers' predetermined routes encompassing the manufacturer's location and the destination, and for providing corresponding preselection information. The route tool 120 and / or the cargo space supplement tool 130 can use this preselection information to select possible routes within this pool of predetermined routes. This reduces the solution space and thus enables an efficient search.

[0045] Furthermore, the control system 100 includes a partitioning tool for dividing the routes preselected by the preselection tool 160 into route groups and assigning the partitioned route groups to respective sub-optimization problems in order to determine one or more optimal routes from the route groups. A route group can contain at least one predetermined route. This allows the global optimization problem to be divided into independent sub-optimization problems, which can, in particular, be solved in parallel.

[0046] The control system 100 further includes a risk assessment tool 180 for determining the risk of the first route, the second route, the final first route, and / or the final second route based on news and / or social media information. Furthermore, the risk assessment tool 180 provides corresponding risk information to the route update tool 140, so that the risk of the respective route can be taken into account when finalizing it. News can be current news that impacts traffic. For example, a football match or a concert might be scheduled, and a traffic jam might be expected due to an exceptionally high volume of vehicles. Such a delay can represent a risk and must be taken into account accordingly.

[0047] Finally, the control system 100 further includes a travel time determination device 190 for determining a travel time from the manufacturer's location to the destination based on historical data of traveled routes. The travel time determination device 190 is further configured to provide travel time information to the manufacturer, in particular for the manufacturer's production planning. This feedback enables the manufacturer to consider the delivery schedule in the production plan.

[0048] The control system 100 makes it possible to organize logistics more efficiently. Furthermore, the information is available in an organized and digitally accessible structure, allowing the various devices 110-190 to process it. These devices 110-190 can include at least one memory and one processor. They can communicate via wired and / or wireless connections, such as a network.

[0049] Fig.Figure 2 shows a computer-implemented method 200 for controlling the transport of products from a manufacturer's production site to a destination. The method 200 comprises determining 210 an optimal arrangement of the products in a minimal number of transport vehicles. Furthermore, the method 200 comprises determining 220 an optimal first route for fully loaded transport vehicles from the production site to the destination and at least one associated optimal freight forwarder. The method 200 further comprises determining 230 partially loaded transport vehicles from among the fully loaded transport vehicles and determining a second route with intermediate destinations to the destination, wherein the intermediate destinations are determined such that additional products located at the intermediate destinations can be loaded into the partially loaded transport vehicles.Finally, procedure 200 includes modifying the first and second routes based on real-time data and outputting the final first and second routes. Procedure 200 can be executed by the control system 100. Reference sign 100 control system 110 cargo space 120 route means 130 cargo space accessories 140 route update tools 150 pickup forecast means 160 preselection tools 170 allocation funds 180 risk assessment tools 190 Travel time determination tools 200 Computer-implemented methods for controlling transports 210 Determining an optimal arrangement of the products 220 of an optimal first route for transport vehicles fully loaded with products 230 Determining partially loaded transport vehicles 240 Modifications to the first and second route

Claims

[1] Control system (100) for transporting products from a manufacturer's production site to a destination, comprising: a cargo space means (110) for determining an optimal arrangement of the products in a minimum number of transport vehicles; a route means (120) for determining an optimal first route of transport vehicles fully loaded with the products from the place of manufacture to the place of destination and at least one associated optimal freight forwarder; a cargo space supplementary device (130) for determining partially loaded transport vehicles from the transport vehicles loaded with the products and for determining a second route with intermediate destinations to the destination, wherein the intermediate destinations are determined in such a way that additional products located at the intermediate destinations can be loaded into the partially loaded transport vehicles; a route update means (140) for modifying the first and second routes based on real-time data on the first and second routes and outputting final first and second routes. [2] Control system (100) according to claim 1, wherein the cargo space extension means (130) is configured to change, in particular add intermediate destinations to the second route based on tracking information of the additional products. [3] Control system (100) according to claim 1 or 2, further comprising: a pickup prediction means (150) for determining a time at which the products at the manufacturing location and / or the ancillary products at the intermediate destinations are to be loaded into the transport vehicles, and for providing corresponding pickup information, wherein the route means (120) and / or the cargo space supplement means (130) are trained to determine the first or second route based on the pickup information. [4] Control system (100) according to any one of the preceding claims, further comprising: a preselection means (160) for preselecting from the predetermined routes of the carriers encompassing the place of manufacture and the place of destination and for providing corresponding preselection information, wherein the route means (120) and / or the cargo space supplement means (130) are designed to determine the first or second route based on the preselection information. [5] Control system (100) according to claim 4, wherein the preselection means (160) is further configured to preselect the predetermined routes based on map material and / or messages related to the predetermined routes. [6] Control system (100) according to one of claims 4 or 5, further comprising: a partitioning tool (170) for partitioning the routes preselected by means of the preselection tool (160) into route groups and assigning the partitioned route groups to respective sub-optimization problems in order to determine one or more optimal routes from the route groups. [7] Control system (100) according to any one of the preceding claims, further comprising: a risk assessment tool (180) for determining a risk of the first route, the second route, the final first route and / or the final second route based on news and / or social media information and for providing corresponding risk information, wherein the route update device (140) is trained to adjust the first and / or second route based on the risk information. [8] Control system (100) according to any one of the preceding claims, further comprising: a travel time determination device (190) for determining a travel time from the manufacturer's location to the destination location based on historical data of routes travelled and for providing travel time information to the manufacturer, in particular for the manufacturer's production planning. [9] Control system (100) according to any one of the preceding claims, further comprising: a user interface for a user, wherein the user interface is configured to display information relating to one or more of the means of the control system according to any of the preceding claims, and / or wherein the user interface is further designed to receive user input from the user, wherein the user input includes one or more control inputs relating to the control system. [10] Computer-implemented method (200) for controlling the transport of products from a manufacturer's production site to a destination, comprising: Determine (210) an optimal arrangement of the products in a minimum number of transport vehicles; Determine (220) an optimal first route of transport vehicles fully loaded with the products from the place of manufacture to the place of destination and at least one associated optimal freight forwarder; Determining (230) partially loaded transport vehicles from the transport vehicles loaded with the products and determining a second route with intermediate destinations to the destination, wherein the intermediate destinations are determined in such a way that additional products located at the intermediate destinations can be loaded into the partially loaded transport vehicles; Modifying (240) the first and second routes based on real-time data on the first and second routes and outputting final first and second routes. [11] Computer program product comprising instructions that cause a hardware component of a computer and / or a control system (100) according to any one of claims 1 to 9 to execute the method (200) according to claim 10 when the computer program is loaded onto or executed by the hardware component or the control system (200).

Images