97 results about "Distribution centre" patented technology

A real-time inventory management system that includes: a centralized control platform module configured to receive, consolidate, and process inventory data from multiple sources, including warehouses, retail stores, and distribution centers; an IoT and sensor module with RFID tags, barcode scanners, and smart sensors configured to track inventory items, monitor environmental conditions, and update inventory records in real time; a machine learning and demand forecasting module configured to analyze historical inventory data, market trends, and external factors to predict future demand and optimize inventory levels; an automatic replenishment and replenishment module configured to dynamically generate and execute replenishment orders based on real-time inventory levels and forecasted demand; a data integration and synchronization module configured to enable seamless communication and data synchronization with external systems, including Enterprise Resource Planning (ERP) platforms and supplier networks; a user interface and dashboard module configured to display real-time inventory status, actionable insights, and key performance indicators (KPIs) for decision-making.

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.

The invention discloses a chain retail industry production and marketing MES integration system based on the Internet of Things technology, and belongs to the technical field of the Internet of Things technology, data are collected in all links of a production workshop, a warehouse, a logistics distribution center, a retail store and the like, the data are transmitted through a wired or wireless communication network, the collected data are transmitted to a central database or a cloud platform, and the central database or the cloud platform is connected with the Internet of Things technology. The data analysis and processing module is used for analyzing and processing data transmitted to the central system, potential information and rules in the data are mined by applying a big data analysis technology, a machine learning algorithm and the like, and logistics distribution management arranges logistics distribution of products according to sales orders and inventory conditions through an integrated system. Logistics distribution management is composed of real-time monitoring and intelligent scheduling, inventory management optimization, distribution center layout optimization, information collaboration and sharing, intelligent packaging and loading and unloading and reverse logistics management, and real-time monitoring and intelligent scheduling are composed of comprehensive real-time monitoring and intelligent path planning and scheduling.

The invention discloses a three-level urban and rural joint distribution network path planning method based on an adaptive hybrid algorithm, and particularly relates to a distribution path planning method which takes a time window, vehicle capacity and path coherence as constraint conditions and combines an adaptive firework-quantum genetic hybrid algorithm for optimization. The method comprises the following steps: firstly, acquiring basic information such as coordinates, demand quantity, time windows and the like of an urban common distribution center, county and rural transfer stations and rural end self-pickup points, and initializing vehicle resources; then, a mathematical model with the purpose of minimizing the total distribution cost is constructed based on common distribution, wherein the model comprises fixed transportation cost, variable transportation cost, time penalty cost and the like; and solving the model by adopting a self-adaptive fireworks-quantum genetic hybrid algorithm, which comprises the steps of generating an initial solution by quantum bit coding, optimizing the initial solution by an insertion algorithm, carrying out global search on a fireworks algorithm and carrying out local optimization on a quantum genetic algorithm. In the optimization process, spark disturbance operation, quantum bit revolving door dynamic adjustment and crossover mutation operator are combined to improve the diversity and convergence speed of solutions, and finally the optimal distribution path meeting the time window constraint is output. The method is suitable for path planning of a complex three-level urban and rural distribution network, the transportation cost can be remarkably reduced, the distribution efficiency is improved, and urban and rural logistics resource allocation is optimized.

A system (100) for a supply chain ecosystem comprising computing devices (160, 260, 460) having a processor (264) and memory storing computer-executable instructions that, when executed by the processor (264), cause the system (100) to obtain extrinsic data (1102) for processing. During a first processing, the extrinsic data (1102) is processed using a first neural network model (266, 466) trained with extrinsic training data to generate a first set of processed data (1104). Intrinsic data (1202) is obtained for processing. During a second processing, the intrinsic data (1202) and the first set of processed data (1104) are processed using a second neural network model (266, 466) trained with intrinsic training data and processed extrinsic training data to generate a second set of processed data (1204). Based on the second set of processed data (1204n), one or more unplanned and unanticipated events to a supply chain disruption are determined.

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 multi-target agricultural product logistics distribution path optimization method based on an ant colony algorithm, and particularly relates to the technical field of distribution path optimization, and the method comprises the steps: constructing an initial distribution graph with time window constraint based on the geographic coordinates of a distribution center and a delivery point, and monitoring the distribution sequence state of agricultural products in real time in the distribution process, thereby achieving the optimal distribution path optimization of the agricultural products. The method can accurately identify the upside-down distribution sequence caused by emergency, calculates the frequent opening and closing influence coefficient of the compartment door according to the obtained opening and closing information of the compartment door when the distribution sequence is in an abnormal state, and calculates the frequent starting and stopping influence coefficient of the cold-chain equipment according to the starting and stopping information of the cold-chain equipment. And constructing an agricultural product corrosion rate influence evaluation model, outputting an agricultural product corrosion rate influence evaluation index, evaluating the influence rise degree of the cold chain environment on the agricultural product corrosion rate, and improving the existing ant colony algorithm based on the agricultural product corrosion rate influence evaluation index to optimize the logistics distribution path of the agricultural product.

The invention provides a material distribution route planning method, a material distribution route planning device and electronic equipment, and the method comprises the steps: obtaining a resource parameter set and distribution demand information of each distribution center, the resource parameter set comprising vehicle parameters of various vehicle types; a distribution route planning model is constructed according to a distribution route constraint set, and the distribution route constraint set comprises a vehicle use and cost association constraint condition, a cargo complete distribution constraint condition, a cargo loading and unloading and load relation constraint condition and a load capacity constraint condition; and based on the freight cost minimization function, inputting the resource parameter set and the delivery demand information to a delivery route planning model, and outputting a target delivery route and a target freight total cost corresponding to the target delivery route.

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.

According to the multi-distribution-center collaborative medicine distribution model and the double-population genetic simulated annealing algorithm provided by the invention, aiming at the timeliness, multi-center collaboration and customer satisfaction requirements of medicine distribution, a mathematical model taking the total cost minimization as a target is constructed. The model comprehensively considers the vehicle fixed cost, the transportation cost, the customer satisfaction cost and the goods damage cost, introduces a soft time window function based on cosine distribution to quantify the influence of delivery delay on the cost, optimizes resource allocation through a multi-center order splitting strategy, and solves the problems of low delivery efficiency and high cost of a traditional single-center mode. According to the characteristics of the model, a double-population genetic simulated annealing algorithm (DPGSA) is designed. According to the algorithm, the global search capability is enhanced through a double-population coevolution mechanism, the local optimization efficiency is improved in combination with the Metropolis criterion of the simulated annealing algorithm, the defect of premature convergence of a traditional genetic algorithm is effectively avoided, the drug transportation and distribution path is optimized, and the distribution cost is minimized.

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 invention provides a rescue vehicle dynamic path optimization method based on an improved rolling time domain. The method comprises the following steps: carrying out real-time statistics on rescue node information in the operation time of a rescue distribution center; determining a target function according to the obtained node number and the coordinate information of the nodes, and constraining and initializing algorithm parameters; wherein the objective function is the minimum value of the sum of the vehicle transportation cost, the total delay of the start service time of the node with the time window exceeding the right time window and the number of the node transportation time exceeding the specified time limit; dividing the operation time of the rescue distribution center into a plurality of time domains, and obtaining an optimal path and optimization cost by real-time rescue node information in each time domain through a simulated annealing algorithm; and calculating the total time length cost, and summarizing the optimal path of the total time domain time length. According to the method, the response speed of the rescue vehicle can be increased, the delay of the rescue vehicle is reduced, meanwhile, prediction errors can be corrected through continuously rolling windows, and the robustness of the path is improved.

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 invention relates to a cold chain vehicle path optimization method considering on-demand recovery of a turnover transportation unit, and the method comprises the following steps: (1) recognizing a problem scene of a cold chain joint distribution problem considering on-demand recovery of the turnover transportation unit RTIs, mapping the cold chain joint distribution problem into a semi-open multi-parking-lot vehicle path problem combined with the characteristics of a simultaneous delivery and pickup vehicle path problem; (2) modeling a semi-open multi-parking-lot vehicle path problem into a mixed integer nonlinear programming model; and (3) solving the mixed integer nonlinear programming model by adopting a hybrid adaptive large neighborhood search algorithm to obtain a cold chain vehicle path. Compared with the prior art, the method has the advantages that the utilization rate of the RTIs in the cold-chain logistics distribution process is increased, and the cold-chain distribution cost under the scene that multiple distribution centers share logistics resources is reduced.

Disclosed is a delivery system including an order information transmission step of transmitting customer order information to a manager server, a shipping preparation step of transmitting the order information to a distribution center to prepare for shipping, a delivery preparation step of preparing packaging configured to store a product and an electronic paper display (EPD) configured to display delivery information about the packaging, a delivery-start tagging step of tagging a manager terminal including a second Bluetooth communication system and a second near-field communication system on the packaging including a first Bluetooth communication system and a first near-field communication system using near-field communication to activate the EPD, and a state information transmission step of collecting internal or external state information about the packaging during delivery of the product to transmit the collected state information to a delivery driver terminal including a third Bluetooth communication system and a third near-field communication system.

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.