69 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.

The invention relates to an emergency resource scheduling strategy generation method considering material demand urgency, and the method comprises the steps: representing all equipment on a target system through a GIS technology, obtaining heat supply network information, carrying out the coupling of the heat supply network information and traffic network information corresponding to the target system, and obtaining a geographic information model of a heat supply facility; based on a K-means clustering algorithm, determining an emergency resource scheduling task of the material distribution center according to the position information of the fault point on the geographic information model, the position information of the material distribution center and the load data of the first-aid repair vehicle in the material distribution center; calculating an optimal path of each emergency resource scheduling task by using a grey wolf algorithm to obtain an initial scheduling strategy; according to the method, the demand urgency degree of the fault point is obtained, the initial scheduling strategy is updated according to the demand urgency degree, the emergency resource scheduling strategy is obtained, efficient and accurate emergency resource scheduling can be achieved, and the scheduling efficiency and reliability of the system are remarkably 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.

The invention discloses a cold chain distribution path optimization method based on an improved whale optimization algorithm, and the method is characterized in that the method comprises the following steps: obtaining the initial information of a demand point and a distribution center, and constructing an information matrix; acquiring historical road condition information around the distribution center and each demand point in a traffic management system; constructing a cold-chain logistics distribution path optimization mathematical model; calculating corresponding transportation time; a search proxy address composed of random variables is converted into a vehicle path matrix through a modeling method; calculating oil consumption of the vehicle during normal driving and idling; and classifying the whale population, and solving the model by using a clustering whale optimization algorithm. And vehicle scheduling and path planning are guided. According to the method, factors such as refrigeration, cargo damage, carbon emission, load capacity and traffic conditions are comprehensively considered, a cold-chain low-carbon logistics distribution path optimization model taking the minimum total cost as a target is established, and an improved whale optimization algorithm is provided for optimization solution.

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.

The application discloses a three-level urban and rural common distribution network path planning method based on an adaptive hybrid algorithm, and particularly relates to a distribution path planning method which is optimized by combining a time window, vehicle capacity and path continuity as constraint conditions and an adaptive firework-quantum genetic hybrid algorithm. The application comprises the following steps: firstly, basic information such as coordinates, demand and time window of a city common distribution center, a county and township transfer station and a rural end self-pickup point is obtained, and vehicle resources are initialized; then, a mathematical model with the minimum total distribution cost as the target is constructed based on common distribution, and the model comprises fixed transportation cost, variable transportation cost and time penalty cost. The model is solved by using the adaptive firework-quantum genetic hybrid algorithm, which comprises quantum bit coding to generate an initial solution, an insertion algorithm to optimize the initial solution, a firework algorithm for global search and a quantum genetic algorithm for local optimization. In the optimization process, the diversity and convergence speed of the solution are improved by combining spark disturbance operation, quantum bit rotating gate dynamic adjustment and crossover mutation operator, and finally, the optimal distribution path meeting the time window constraint is output. The application is suitable for path planning of a complex three-level urban and rural distribution network, can significantly reduce transportation cost, improve distribution efficiency and optimize urban and rural logistics resource allocation.

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.

The invention relates to a fresh product periodic distribution scheduling method considering customer service fairness, and belongs to the technical field of logistics distribution scheduling optimization. The method comprises the following steps: in a distribution area, initializing distribution vehicle data, a distribution driver set and a customer set, and constructing a distribution network diagram by taking a distribution center and customer positions as nodes; constructing a customer perception satisfaction evaluation index system, obtaining scores of evaluation indexes in different delivery time periods, and calculating comprehensive perception satisfaction scores corresponding to the delivery time periods; establishing a customer service fairness index based on the comprehensive perception satisfaction score of each receiving time period; and establishing a fresh product periodic distribution scheduling model with minimum total distribution energy consumption and maximum customer service fairness as targets, and resolving a fresh product periodic distribution scheduling scheme by adopting a genetic algorithm. According to the invention, a reasonable and economic distribution path scheme can be generated, the energy consumption cost in the distribution process is effectively optimized, and the fairness of customer service is balanced.

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 invention relates to a power grid material logistics layout and influence factor evaluation method, and belongs to the technical field of electric power material distribution optimization. According to the method, for a complex logistics scene of multiple parking lots, multiple tasks and multiple vehicle types, a unified network model including the parking lots, a distribution center and customer demand points is constructed, and dynamic planning and risk assessment of a distribution path are realized by integrating a time window punishment mechanism, multi-vehicle-type cost optimization and a multi-scene simulation technology. The method specifically comprises the following steps: coding a parking lot, a distribution center and a customer point into network nodes, and designing a path rule of closed-loop distribution after a vehicle starts from the parking lot and is loaded by the distribution center; time window deviation punishment cost is defined, and a total cost minimization target is established in combination with transportation cost and empty driving cost; generating a distribution scheme by adopting a hybrid optimization algorithm combining a genetic algorithm and tabu search; according to the invention, the logistics cost can be effectively reduced, the distribution timeliness is improved, the power grid material emergency response capability is enhanced, and scientific decision support is provided for power enterprises.

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 an underground logistics delivery terminal and system, belonging to the field of logistics delivery technology. The delivery terminal includes a building mailroom, a handling robot, a delivery robot, and multiple floor receiving points. The building mailroom, located in the underground parking garage, connects to external large-item delivery vehicles and underground transport tracks. The handling robot transports large packages unloaded from large-item delivery vehicles to smart self-service lockers. The transport tracks are connected to the building mailroom. Delivery robots transfer standard delivery boxes containing small and medium-sized packages to the building mailroom, and empty boxes and pickup boxes inside the building mailroom are sent into the transport tracks. The standard delivery boxes are then transferred to the receiving points on each floor via elevators, achieving unmanned and contactless delivery. This delivery system delivers and picks up goods between the shared distribution center and the building mailroom, and between the building mailroom and each floor, ensuring that logistics freight and passenger traffic do not interfere with each other, thus improving operational efficiency.

The invention provides an unmanned aerial vehicle distribution center site selection method and system considering an interruption risk, and relates to the technical field of unmanned aerial vehicle logistics networks. In a site selection process, external factors such as interruption risk of a distribution center and demand uncertainty of a demand point are considered, a network total cost model including construction cost, inventory cost and transportation cost is constructed, minimization of the model is taken as a target function, linearization processing is performed on the total cost model, a Lagrange multiplier is introduced, specified constraint conditions are relaxed, and a site selection result is obtained. A relaxation model is obtained, the minimum value of the model is solved, a lower bound solution is obtained, on the basis of the lower bound solution and specified constraint conditions, an immobilized optimization strategy is adopted, the minimum value of an upper bound model is solved, and an upper bound solution is obtained; and according to the multiplier iteration model, updating the Lagrange multiplier, substituting the updated Lagrange multiplier into the relaxation model, and circularly carrying out solving judgment until the solving process meets a preset termination condition, so as to obtain a site selection scheme of the unmanned aerial vehicle distribution center, so that demand point distribution is met, and site selection of the distribution center is more reasonable.

The invention discloses a logistics distribution center site selection method based on a random chaos immune optimization algorithm, and belongs to the technical field of logistics distribution center site selection, and the method comprises the steps: 1, constructing a target function of logistics distribution center site selection; and 2, solving a target function of logistics distribution center site selection by adopting a random chaos immune algorithm. According to the logistics distribution center site selection method based on the random chaos immune optimization algorithm, the problem that a traditional immune algorithm is prone to falling into a local optimal solution is solved, the nonlinear characteristic of chaos mapping and a random adjustment mechanism are combined, the balance of the algorithm between global exploration and local development is effectively coordinated, and the selection accuracy of the logistics distribution center site selection method is improved. According to the method, the accuracy and the stability of site selection of the logistics distribution center can be quickly realized, and the delivery path and the delivery sequence of customers can be reasonably arranged, so that the site selection scheme of the logistics distribution center is fully utilized, and the distribution efficiency is remarkably improved.

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 invention relates to the technical field of unmanned aerial vehicle operation of low-altitude economic intelligent logistics, in particular to a multi-distribution-center unmanned aerial vehicle distribution route planning method, which comprises the following steps of: establishing a model taking minimization of a weighted total flight distance, minimization of robust flight time and maximization of a demand satisfaction rate as decision targets; determining a distribution center by adopting a greedy selection strategy of geometric coverage and dispersity priority; and solving to obtain a Pareto frontier solution set. According to the method, the multi-target optimization model containing the flight time, the flight distance and the demand satisfaction rate is constructed, the delivery centers are selected by adopting the greedy selection strategy based on geometric coverage and dispersity, and the Pareto frontier solution set is solved by applying the NSGA-II algorithm, so that the balance of multiple decision targets is realized, the adaptability to a multi-delivery-center scene is improved, and the method has the advantages of being simple in structure, convenient to operate and high in efficiency. The distribution center selection is optimized to improve the coverage range and the service efficiency, and an optimization tradeoff scheme among the service quality, the distribution efficiency and the operation cost is provided for decision makers.

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.