44 results about "Distribution centre" patented technology

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention provides a task priority and equipment state linkage path adjustment method and related equipment, and relates to the technical field of intelligent distribution center systems. Task queue information, production system feedback information and equipment state information are obtained in real time, the dynamic priority score of each task is calculated based on the information, the urgency of a production critical path and the equipment suitability are integrated in the scores, and therefore it is ensured that priority calculation focuses on the core target of avoiding production interruption. Then, the system ranks the tasks according to the dynamic priority scores, and adopts a strategy of'high priority priority planning + path reservation '; the path of the high-priority task is preferentially planned and reserved on the space-time map, and path conflicts among multiple devices are avoided from the source. The low priority tasks are then directed to avoid these reserved areas. Finally, the planned conflict-free path is issued to the robot equipment for execution, and the overall operation efficiency and stability of the intelligent distribution center system are comprehensively improved.

This invention provides a workflow of processes and procedures, system, platform and method for cross-border delivery using a peer-to-peer (P2P) transshipment network. It connects order placers who wish to transfer packages from any country with travelers who can deliver these items during their trips, establishing a global delivery network. This network leverages unutilized baggage space with travelers and secure relay points to efficiently handle deliveries without relying on traditional distribution centers. The system optimizes route planning and transshipment points, ensuring timely and cost-effective delivery services with the lowest carbon print possible. This invention integrates a robust online user interface, secure payment processing, comprehensive user support, dynamic matching algorithms, and sophisticated logistics management to facilitate efficient, reliable, and cost-effective deliveries. International and global trade is promoted by leveraging travelers' existing routes, reducing the need for dedicated logistics services. It stimulates local economies by providing travelers with new income opportunities.

The invention relates to the technical field of unmanned aerial vehicle operation of low-altitude economic intelligent logistics, in particular to a multi-distribution-center vehicle-unmanned aerial vehicle cooperative distribution route planning method, which comprises the steps of constructing a multi-target and multi-constraint mathematical model adaptive to cooperative distribution decision of multiple distribution centers and heterogeneous vehicle-unmanned aerial vehicle carriers; and solving the mathematical model by using the improved NSGA-II to obtain a route scheme meeting actual distribution requirements. According to the method, a multi-objective optimization model containing total transportation cost, distance and time is constructed, multiple constraints such as vehicle paths and unmanned aerial vehicle cooperation are introduced, a multi-level coded improved NSGA-II algorithm is adopted for solving, feasibility and diversity of solutions are initialized and guaranteed through a multi-strategy population, a multi-objective optimization tradeoff scheme is provided, and the method has the advantages of being high in robustness and high in robustness. The adaptability to a multi-distribution-center scene is improved, the economical efficiency, the space efficiency and the time efficiency of a distribution system are optimized, and the multi-dimensional requirements of intelligent logistics are met.

Examples provide for dynamically replenishing perishable items. Dynamic replenishment data including forecast daily demand data, current inventory data of DCs, and / or supplier constraint data. The replenishment data is used by one or more mixed integer linear programming (MILP) based model(s) to generate results, including supplier-to-recipient breakout orders dynamically adjusted in real-time based on total demand and supply imbalances with specific upper and lower bounds. The system generates results for weekly and / or daily perishable item replenishment results, including transport vehicle assignments and pallet build parameters for reducing shipping costs and minimizing the number of less than truckloads (LTLs) in shipments from suppliers. The pallet build parameters are customized at an item and / or supplier level to reduce the number of partial pallet occurrences in shipments from suppliers to further reduce transportation resource usage and determine the number of cases shipped from each supplier to each recipient for each item daily.

A shipping container is configured to transport goods from a distribution center, to a retail location, and onto the sales floor of the retail location. The shipping container glides on wheels and is stackable. The shipping container is configured to receive forklift forks from multiple sides. At least one of the channels includes a surrounding framework to safeguard against tilting. The lid is selectively mountable in two orientations to close the container or to provide handles for propelling and / or steering the shipping container. An expandable storage location for empty packaging is provided at an exterior of the shipping container.

Systems and methods are disclosed for controlling operations of autonomous vehicles and systems in, for example, logistics yards at distribution, manufacturing, processing and / or other centers for the transfer of goods, materials, and / or other cargo. In some embodiments, an autonomous yard tractor or other vehicle can include one or more systems for locating an over-the-road trailer parked in a yard of a distribution center, engaging the trailer, and moving the trailer to a loading dock for loading / unloading operations in accordance with a workflow procedure provided by a central control system. In other embodiments, an autonomous yard tractor can locate the trailer at the loading dock after the loading / unloading operations, engage the trailer, and move the trailer to a parking location in the yard. In some embodiments, the autonomous yard tractor can include a sensor system configured to detect the position of the trailer relative to, for example, the tractor, and / or the dock station can include a sensor system configured to detect the position of the trailer relative to, for example, the dock station during a docking procedure.

A shipping container is configured to transport goods from a distribution center, to a retail location, and onto the sales floor of the retail location. The shipping container glides on wheels and is stackable. The shipping container is configured to receive forklift forks from multiple sides. At least one of the channels includes a surrounding framework to safeguard against tilting. The lid is selectively mountable in two orientations to close the container or to provide handles for propelling and / or steering the shipping container. An expandable storage location for empty packaging is provided at an exterior of the shipping container.

The application discloses an emergency material site selection and path optimization method based on a whale optimization algorithm, and specifically as follows: firstly, an emergency material site selection model is established based on urgency and customer satisfaction to select an optimal distribution point as a distribution center; secondly, an emergency material distribution model is established based on transportation cost and time conditions to plan a distribution path for disaster points; finally, parameters of the whale optimization algorithm are adjusted, and paths generated in each iteration are stored to obtain a disaster point path planning route and a global path length. The application realizes path optimization of emergency material distribution, ensures timeliness, realizes the target of reducing cost, and provides effective guarantee for transportation of emergency materials.

The application relates to the field of logistics transportation and discloses a multi-distribution center-oriented unmanned vehicle logistics transportation scheduling method and system, which comprises the following steps: constructing a logistics transportation scheduling model; the logistics transportation scheduling model is a model for describing the delivery tasks of vehicles of multiple distribution centers to multiple customers within a preset time range; calculating the attribution between customers, establishing a customer group of the logistics transportation scheduling model according to the attribution; calculating the intimacy between the customer group and the distribution center, and distributing the customer group to the distribution center according to the intimacy; and solving the logistics transportation scheduling model for completing the customer group distribution by using an improved ant colony algorithm to obtain an optimal distribution path of logistics transportation scheduling. The matching relationship between the customer group and the distribution center is improved, and the improved ant colony algorithm is introduced, so that the practicability of the model can be further improved, and the efficiency and quality of logistics transportation scheduling are improved.

Method and system of electric vehicle route planning for multi-service delivery and on-route energy replenishment is disclosed. Last mile delivery is a critical component of supply chains that impacts both customer experience and delivery cost. User request is received by a depot of a delivery environment. The user request comprising a current location of the user, one or more required services, and a time window rendered between each of the required services is processed and a graph is generated for the user request. Further, trained learning agent generates a route map indicating a plurality of waypoint locations for the electric vehicle to visit each node in accordance with minimized trip cost of fleet and time duration where each node has a state action pair for the electric vehicle. The learning agent learns continuously during the interaction with delivery environment and obtains feedback for every associated action.

A conveyance system that does not hinder movement of a conveyance vehicle or a truck and improves operational efficiency of a delivery center includes: a travel plan generating unit to generate a travel plan of a conveyance vehicle, the travel plan being used to execute conveyance of a trailer from a parking lot to a warehouse or conveyance of the trailer from the warehouse to the parking lot by the conveyance vehicle; a recognition unit to detect a position of the trailer on a site; and a parking position determining unit to determine a section in which the trailer is parked by the conveyance vehicle from a detection result by the recognition unit, in which the travel plan generating unit generates the travel plan using the section in which the trailer is parked determined by the parking position determining unit as a parking position of the trailer.

1. Name of the product in this design: Architectural Model (Cigarette Regional Logistics and Distribution Center). 2. Intended use of this design: for use in physical buildings. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1. 5. The bottom surface of this product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted.

A receiving and disbursement system is provided for the receipt of items from fulfillment centers, retail locations, or homes to a designated location, securing the delivered items from theft, or inserting the delivered items directly into a building or building automation system. The receiving and disbursement system is further able to reverse the process in order to automatically retrieve items back from the delivery location to return items to retail locations, homes, businesses, fulfillment or recycling centers for delivery, re-filling, recycling and / or disposal. A system of delivery vehicle interfaces, delivery containers, item trays, and docking stations to facilitate the movement of items from one location to another location and back to the same or other designated location. Locations may include industrial, agricultural, business, government, waste disposal, homes, garages, carports, barns, greenhouses, or inside a home. The system further utilizes autonomous road worthy vehicles, sidewalk vehicles, and drones.

This invention discloses a method for optimizing cold chain logistics vehicle delivery routes based on dual-temperature zone truck compartments, including steps S1: obtaining the coordinates and cargo demand of distribution centers and frozen / refrigerated goods delivery points; S2: determining an initial delivery route scheme consisting of fixed and variable routes based on the full truckload coefficient, less-than-truckload (LTL) demand, distance reduction coefficient, and load coefficient; S3: determining whether the variable route can be modified; if yes, proceed to S4; otherwise, the initial scheme is the optimal scheme; S4: determining the delivery route to be updated based on the remaining capacity and margin coefficient of the variable route vehicle compartments; S5: determining whether the number of vehicles required for the updated scheme is less than the number of vehicles allocated to the initial scheme; if yes, calculating the distance increase coefficient to determine the optimal scheme; otherwise, the initial scheme is the optimal scheme; S6: generating the cold chain logistics delivery route and the number of vehicles based on the optimal scheme. This invention can reduce the number of cold chain logistics vehicles and improve the utilization rate of truck compartment capacity, helping to reduce the transportation costs of enterprises.

The invention relates to the technical field of Internet of Things, in particular to a path planning method and device for various transportation tools for transporting emergency materials after disasters, and the method comprises the steps: firstly, preliminarily planning a transportation path according to the distance between an emergency material distribution center and a plurality of disaster-affected nodes and the bearing capacity of each transportation tool; subsequently, an operator pair set formed by a plurality of removal operators and a plurality of insertion operators in pairs is introduced, operator pairs are adaptively selected through a Q learning algorithm, the preliminarily planned transportation path is recombined and optimized, meanwhile, performance indexes after path recombination are used as reward signals to be fed back to a Q learning model, and the model is driven to iteratively update an operator pair selection strategy; and finally, an iteration ending condition is met, and the transportation path with the performance meeting a preset condition is selected as the final transportation path of the multiple transportation tools. According to the scheme, through collaborative optimization of Q learning and operator pairs, the algorithm is prevented from falling into a local optimal solution, and the finally obtained transportation path can well schedule the transportation means.

Systems and methods relating to management of delivery fleets are disclosed. Such systems and methods include using functionally-aligned machine learning engines to manage aspects of fleet dispatch and routing, inventory level adjustments, and order optimization. The techniques may be applied to manage an automated fleet delivery services system controlling a fleet of autonomous delivery devices (e.g., UAVs) to deliver products from a plurality of distribution hubs to fulfil customer orders. The interconnected machine learning engines enable efficient management of various aspects of such automated fleet delivery services in real time, including optimization of fleet distribution, optimization of inventory distribution, and optimization of order distribution. Optimization of order distribution may be obtained by dynamically adjusting delivery charges for orders based upon current fleet capacity and inventory levels.

The present application relates to the field of automation logistics, aiming at the problems of high empty load rate, poor coordination and easy to fall into local optimum of algorithm of multi-distribution center AGV scheduling in the prior art, a multi-AGV intelligent scheduling optimization method is disclosed, a multi-distribution center semi-open heterogeneous AGV simultaneous collection and distribution path planning model MDHOHVRPSPDTW with mixed time window is constructed. The method uses mixed time window constraints to adapt to the time efficiency demand of different stations, uses the simultaneous collection and distribution mode to reduce the empty load rate of the vehicle, and allows the AGV to return nearby through the semi-open strategy to reduce the empty running. The solving process adopts the multi-subgroup velocity pause particle swarm optimization algorithm MSVPPSO-LNS combined with large neighborhood search, and through the introduction of chaos initialization, velocity pause mechanism and multi-subgroup cooperation strategy, the global optimization ability is enhanced. The experimental results show that the present application can effectively reduce the transportation cost and penalty cost, and significantly improve the flexibility and resource utilization rate of the scheduling system.

The present utility model refers to a device coupled to a robot or another moving system, exerting elastic pressure on the upper part over can containers, allowing to keep the products to be moved as fixed, displacing and turning them, defining their positions and organizing them for later palletization, enhanced with the introduction of locking pins, side wings, a lower metal plate covered by an elastic polymer with an innovative checkerboard design and a compression spring system, in the field of application intended to the industry and distribution centres for beverage and food, in the area of preparation of product container layers, ready before palletization.

A system and a method are provided for a direct to store supply chain to bring high volume, bulk space products directly from suppliers, manufacturers, and vendors to retail locations, thereby bypassing retailer distribution centers. The system comprises an order management and invoicing system configured to facilitate demand signals from retail locations to manufacturers and vendors of desired products. A routing optimization system is configured to continually optimize an aggregate of orders so as to produce lowest-cost shipping solutions. A tracking and notification system is configured to provide manufacturers with clear tracking visibility of en-route deliveries once they leave manufacturing facilities.

The application provides an intelligent medical emergency command and dispatch method and system, relates to the technical field of medical emergency command and dispatch, and constructs emergency resources, demand sites, distribution centers and vehicle sets, takes the emergency resources as source nodes, takes the demand sites as target nodes, takes the distribution centers as common nodes, generates a primary node group, forms a final permanent marked node group through a search algorithm, calculates the dispatch value of each permanent marked node in the permanent marked node group, sorts the permanent marked node group, generates a new marked node group, selects the number of associated paths and the radius of the support range of the permanent marked nodes in the new marked node group, calculates the total time and the total cost of task completion of the new marked node group, and screens the optimal dispatch node and path according to the total time and the total cost of task completion. Real-time monitoring, command and dispatch of rescue are realized, and scientific decision support is provided for emergency command and dispatch of emergencies.

An example embodiment is a trailer for a semi-trailer truck that includes an electric, battery-powered drive train mounted on at least one axel of the trailer. A controller sends and receives signals from a global positioning system and from distribution center personnel. Signals are interpreted by a protocol in the controller to move a powered trailer from its current location to a destination. A trailer control algorithm monitors a plurality of variables and communicates to a controller in each trailer to control a plurality of trailers according to the variables.

A method according to the present disclosure provides a computer-based procedure for distributing a battery from a plurality of distribution depots to one or a plurality of construction sites where a piece of machinery is located. The method according to the present disclosure includes: acquiring mechanical data indicating the position of the machinery at each of the one or the plurality of construction sites; acquiring data on the required current indicating the required amount of electrical current at each of the one or the plurality of construction sites; acquiring battery data indicating the position of the battery stored in each of the plurality of distribution depots; and determining an allocation of a distribution depot from which the battery is delivered to each of the one or the plurality of construction sites based on the mechanical data, the data on the required current, and the battery data.

A system and devices for generating and consuming three-dimensional immersive environments of one or more facilities (e.g., shipping yards, assembly plants, processing plants, warehouses, distribution centers, ports, yards, transports, and the like). A simulation system may be configured to generate a full three-dimensional (3D) virtual environment or mixed reality environment that replicates the physical layout of the facility as well as the operations of the facility. The simulation system may also be utilized to simulate potential operations of the facility, such as with new or modified settings, configurations, arrangements, assignments, equipment, personnel, and the like.

This invention discloses a dynamic collaborative optimization method and system for multi-distribution center route planning, belonging to the interdisciplinary field of artificial intelligence and operations research. The method includes: based on a multi-dimensional evaluation vector, generating and executing partitioning adjustment strategy instructions through semantic understanding and logical reasoning of a large language model agent to iteratively optimize the customer point allocation scheme; constructing an initial path solution population in parallel for each customer point set; dynamically modulating the iterative process of the underlying optimization engine to obtain the optimal path for each sub-problem through real-time monitoring of search state parameters and generation of control instructions by the large language model agent; and finally summarizing to obtain the global planning scheme and its minimum / maximum objective value. This invention solves the problems of blind decomposition and rigid search in traditional methods by using a macro- and micro-level dual-loop large language model agent-guided collaborative mechanism, significantly optimizing the longest path length and improving the solution efficiency and robustness under large-scale, strongly constrained scenarios.

A system and method of controlling order fulfillment from a network of fulfillment centers are provided. The method includes receiving a batch of orders defined by either a threshold number of orders received or a predetermined period of time after a first order is received. The method also includes assembling a model based on the batch of orders. Each module in the model includes algorithms having fulfilment logic and variables based on constraints, external factors and routing objectives of the network. The method also includes receiving a model output including an allocated solution for each of the variables that is resolvable to shipping outcome for each order. The method also includes causing transmission of fulfillment data to each fulfillment center that comprises fulfillment instruction data for filling a complete order from the batch of orders, including identifying product, quantity, packaging instructions, and shipping instructions.

A sorting arrangement for sorting objects, such as parcels of small and medium size, for loading onto a transport vehicle for delivery from a storage facility, such as a distribution centre, said arrangement comprising a pouch sorter having a plurality of hanging pouches for receiving and storing objects therein, including at least one delivery pouch buffer receiving said individually transferred pouch or group of pouches; wherein the pouches are transferred to the delivery pouch buffer in a predetermined sequence for providing a sequential delivery pouch buffer at the delivery vehicle loading position.

The invention provides a single-distribution-center multi-vehicle multi-delivery-point transportation route planning method and system, and relates to the field of data processing, and the method comprises the steps: obtaining the related data of a distribution center, the related data of delivery vehicles, and the related data of delivery points; determining a target function; environment definition, state design, action design and reward design are carried out on the basis of related data of a distribution center, related data of delivery trucks and related data of delivery points, state representation comprises an N + 1-dimensional system state matrix, an N + 1-dimensional operation action vector, an N + 1-dimensional cargo quantity vector and an N + 1-dimensional remaining cargo capacity vector, and N is the number of the delivery points; establishing and training a return estimation model; acquiring a real-time transportation task; and on the basis of the trained return estimation model and the target function, an optimal transportation route planning scheme of the real-time transportation task is generated, and the method has the advantages of improving the effectiveness of route planning and the distribution efficiency in a single-distribution-center multi-vehicle multi-delivery-point scene.