325 results about "Inventory level" patented technology

A generative AI-supported system (100) for real-time demand forecasting and inventory optimization in supply chain management, which includes the following: a data collection and integration module configured to aggregate and preprocess structured and unstructured data from multiple sources, including historical sales records, IoT sensors, external market trends, and environmental factors; a generative AI-based forecasting module configured to process input data, recognize complex patterns, and generate highly accurate real-time demand forecasts using deep learning techniques; an inventory optimization module configured to dynamically calculate and adjust inventory levels based on demand forecasts, replenishment plans, lead times, and safety stock requirements; a decision support and analysis module configured to generate actionable insights, provide simulations of multiple scenarios, and recommend cost-effective strategies for supply chain operations; a real-time warning and notification module configured to monitor supply chain activities and inform stakeholders of potential disruptions, anomalies, or demand spikes; a user interface and integration module configured to provide outputs via a user-friendly dashboard and seamlessly integrate with existing enterprise resource planning (ERP), warehouse management systems (WMS), and logistics platforms.

The invention provides an inventory optimization method and system based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: obtaining the multi-dimensional data of a commodity, constructing a dynamic convolution kernel through employing a graph neural network, constructing a soft clustering distribution matrix in combination with a multi-layer perceptron, and obtaining a long-term dependence feature fusing a space dimension and a time dimension. And fusing the short-term and long-term features, inputting the fused short-term and long-term features into a bidirectional long-short-term memory network to generate a sales prediction sequence, and calculating a prediction confidence interval to obtain a final sales prediction result. And calculating a safe inventory level based on a sales prediction result, generating a replenishment decision by using a dynamic planning model based on deep reinforcement learning in combination with an inventory cost function, and finally sending the decision to a supply chain execution system. The sales volume is accurately predicted through the deep learning model, the inventory management is optimized, the storage cost, the stockout cost and the overstock cost are reduced, and the supply chain efficiency is effectively improved.

The invention provides an inventory allocation optimization method and system based on a genetic algorithm, and relates to the technical field of inventory allocation, and the method comprises the steps: obtaining a commodity standard data set, extracting key features in the standard data set, inputting the key features into a pre-trained inventory demand prediction model, and obtaining a demand prediction result of each inventory unit in a period of time in the future; calculating the safety inventory level of each inventory unit by adopting a safety inventory formula, and constructing a multi-target inventory allocation optimization model; solving the multi-target inventory allocation optimization model to obtain an optimal solution set, selecting a solution with an optimal comprehensive evaluation index in the optimal solution set as an optimal inventory allocation scheme, and generating an inventory allocation plan and a distribution path on the basis of the obtained optimal inventory allocation scheme in combination with the category attributes and the distribution priority of each inventory unit. And according to a comparison result of the actual inventory state and the inventory allocation plan, dynamic optimization of inventory allocation is realized in combination with a demand prediction result and a replenishment strategy.

The invention discloses an intelligent supply chain scheduling optimization method and system based on an autonomous controllable large model, and the method comprises the steps: employing a demand prediction agent to predict a future market demand condition according to product sales data; according to supplier supply product data, adopting a supplier management agent to select an optimal supplier as a target supplier; according to the predicted future market demand condition and inventory data, an inventory optimization agent is adopted to generate an optimal inventory replenishment plan; according to the inventory level after the optimal inventory replenishment plan and the logistics transportation data, adopting a logistics scheduling agent to calculate and obtain an optimal transportation path; feedback data of all the agents are collected, performance indexes of all the agents are calculated through the information feedback agents according to the feedback data, and agent parameters are adjusted according to the performance indexes of all the agents; and the strategy output of each agent is integrated into the scheduling strategy of the supply chain. The scheduling strategy can be dynamically scheduled and updated in real time.

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 belongs to the technical field of e-commerce, and provides a commodity information management method and system based on big data analysis, and the method comprises the steps: collecting the multi-dimensional data of commodities on all e-commerce platforms and social media in real time through an automatic crawler program; the collected data are preprocessed; performing deep analysis on the preprocessed data by applying a machine learning algorithm, constructing a commodity feature model, identifying commodity relevance, and predicting a market trend; on the basis of an analysis result, fine classification is automatically carried out on the commodities; through the automatic data acquisition and processing flow, the information processing speed is remarkably increased, human errors are reduced, the data quality is improved, meanwhile, the commodity features and the market trend are accurately predicted by using the deep learning technology and the machine learning algorithm, a scientific basis is provided for operation decision making, and by dynamically adjusting the inventory level, the product quality is improved. The inventory overstock risk is reduced, the overall efficiency of logistics and supply chains is improved, and the operation cost is reduced.

The invention belongs to the technical field of intelligent supply chain management and optimization, discloses a product supply chain comprehensive optimization system and method based on big data, and aims to solve the problem that a traditional supply chain management system based on historical data and a static model is difficult to quickly adapt to changes. Forming a real-time evaluation vector; integrating the environment data, constructing an environment feature vector, and training to obtain a risk prediction model in combination with a real-time evaluation vector, thereby obtaining a risk probability of each node in the future, and obtaining a risk evaluation vector; based on the risk assessment vector, in combination with the inventory level and the transportation path, performing joint optimization on the inventory configuration and the distribution path to generate an optimization strategy; updating each node of the supply chain according to the optimization strategy, monitoring the state of the updated supply chain in real time, and generating a cost prediction vector when abnormity is monitored; and taking corrective measures on the supply chain according to the cost prediction vector to ensure stable operation of the supply chain.

A healthcare supply management system that uses machine learning for predictive supply chain optimization, comprising: a) a data collection and integration module configured to collect and integrate real-time and historical data from multiple sources, including electronic health records, vendor databases, hospital inventory logs, and external market trends. b) a predictive analytics and demand forecasting module that uses machine learning algorithms to analyze data and generate accurate demand forecasts for medical supplies. c) an inventory optimization and automation module that dynamically adjusts inventory levels based on predictive insights and automates orders. d) a supplier management and logistics module that can be used to evaluate supplier performance, track shipments and predict delivery delays. (e) a risk management and disruption mitigation module that identifies potential risks in the supply chain and proposes emergency strategies. f) a decision support and reporting module that provides real-time insights, AI-driven recommendations, and customizable reports through a user-friendly dashboard. (g) a processing unit and a cloud-based database to store and process supply chain-related information and to facilitate seamless data access and communication between the modules.

The invention provides an inventory management method for e-commerce, which integrates links of multi-source data analysis and prediction, intelligent replenishment, dynamic inventory tracking and return management, constructs a demand prediction model by using historical sales records, user behaviors and market dynamic information, sets a safe inventory level based on a prediction result, and provides an inventory management method for e-commerce. An automatic purchase request and a quick response replenishment mechanism are implemented, real-time inventory synchronization is achieved through an API, inventory changes are monitored through the Internet of Things technology, a commodity processing decision is guided for a returned commodity development prediction model, unsalable commodities are regularly examined, products close to the expiration date are reasonably handled, and a KPI is set and comprises the inventory turnover rate and the order accuracy rate. And closed-loop control of inventory management is realized.

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

The invention discloses a manufacturing industry supply chain optimization method, system and equipment based on a large model, and a medium, belongs to the technical field of large model and supply chain management, and aims to solve the technical problem of how to monitor, predict and optimize a supply chain in real time, improve the efficiency of the supply chain and improve the efficiency of the supply chain. According to the technical scheme, the method comprises the following steps: data acquisition and preprocessing: comprehensively acquiring manufacturing industry supply chain data, and carrying out cleaning, duplicate removal, missing value processing, abnormal value detection and data normalization processing on the acquired manufacturing industry supply chain data to obtain preprocessed manufacturing industry supply chain data; building a large supply chain model: building the large supply chain model by utilizing the preprocessed manufacturing industry supply chain data, and training, optimizing and deploying the supply chain model; real-time monitoring and prediction: monitoring the inventory level and each index of the generation progress in the manufacturing industry supply chain in real time through the trained large model; optimizing the decision; and updating and iterating the model.

The invention relates to the technical field of supply chain management, in particular to a part supply chain full-process digital collaborative management system and method, and the method comprises the steps: evaluating supplier data through constructing a dynamic scoring model, and obtaining a supplier score; when the supplier score is lower than a predetermined threshold value, generating a multi-source purchase plan, and dynamically adjusting purchase distribution according to the supplier score; constructing a warehouse-in and warehouse-out part intelligent verification model to analyze warehouse-in and warehouse-out parts, generating an acceptance report and block chain record association, and triggering an alarm and freezing an order when a difference is found; a supply chain risk topological graph is constructed through the supplier fulfillment rate, the logistics data and the market demand fluctuation, and high-risk nodes are identified and early warned in real time; initiating emergency purchase to suppliers with high supplier scores based on high-risk nodes, and optimizing inventory scheduling based on geographic proximity; the inventory level is optimized in real time based on the production line state and the in-transit logistics data, and the safe inventory threshold is dynamically adjusted to cope with supply chain interruption.

The invention relates to the technical field of supply chain management, and discloses a commodity supply chain management method based on the Internet of Things. According to the commodity supply chain management method based on the Internet of Things, errors caused by manual data entry are avoided through time sequence analysis and data filtering processing of the cloud analysis management platform, enterprises can accurately predict future market demands, and the inventory turnover rate, the optimal inventory level and the transportation and storage cost are dynamically calculated, so that the inventory management is optimized, and the enterprise management efficiency is improved. In addition, according to the method, by comprehensively evaluating the delivery timeliness rate of suppliers and the quality defect rate of goods, it is ensured that enterprises can select reliable suppliers, the stability and efficiency of a supply chain are improved, the real-time logistics monitoring and dispatching capacity enables goods to be quickly and efficiently delivered to clients, and the efficiency is improved. The customer satisfaction is comprehensively improved, and the competitiveness and market adaptability of enterprises are promoted.

Various embodiments described herein relate to management of industrial process optimization related to batch operations. In this regard, an optimization request to optimize an industrial process that produces an industrial process product is received. In response to the optimization request, product spent characteristics for one or more blending components of a batch operation subprocess are determined. Also in response to the optimization request, demand data for one or more feed products associated with the one or more blending components is updated based on the product spent characteristics and inventory data indicative of an inventory level for the one or more feed products. Furthermore, a control signal configured based on the demand data is transmitted to a controller configured for optimization associated with the industrial process that produces the industrial process product.

A trained model of an online system is used to generate action recommendations by predicting future demands. The online system gathers in-store data by receiving, from a device of a picker and / or a computing system of an in-store physical receptacle, data with information about an inventory of an item. The online system estimates, based on conversion data for the item, a level of inventory for the item. The trained model is then applied to predict, based on the in-store data and the estimated level of inventory, a demand prediction score indicative of a future demand for the item. The online system generates, based on the estimated level of inventory and the demand prediction score, a depletion metric indicative of a time period until the inventory of the item is depleted. Based on the depletion metric, the online system triggers an action in relation to the inventory of the item.

The invention relates to the technical field of project cost management, in particular to a hydraulic engineering construction cost control system and method, and the system comprises a material consumption analysis module, an inventory early warning calculation module, a dynamic inventory adjustment module, a replenishment trigger optimization module, and a consumption stability evaluation module. According to the method, the material consumption rate is dynamically monitored, the construction progress and engineering quantity are combined to measure and calculate data, a consumption trend curve is established, the actual use condition is compared, the consumption rate deviation is accurately quantified, real-time evaluation of material use is achieved, the number of supply days is calculated in combination with inventory data, the replenishment period is matched, and the replenishment time is dynamically adjusted; the inventory level is optimized, fund occupation is reduced, the stability is evaluated according to the consumption fluctuation range, the maximum consumption fluctuation value is calculated, the demand is adjusted in combination with replenishment, the inventory early warning threshold value is dynamically corrected, the supply chain breakage risk is reduced, the adaptability of construction material management is improved, and the cost control accuracy is improved.

The invention discloses an intelligent management system for clinical department storehouse consumables, relates to the technical field of hospital consumable management, and aims to solve the problems that a traditional consumable management mode often depends on manual recording and manual management, data omission and inaccuracy easily occur, the use condition of the consumables is difficult to track, the replenishment opportunity is likely to be inaccurate, and the use cost is low. According to the system, full-life-cycle tracking of consumables is realized through an RFID tag and a block chain recording module, the authenticity and integrity of data are ensured, the management risk caused by information asymmetry is reduced, the consumable inventory condition and consumable demand prediction are monitored in real time, and the medical service efficiency is improved. According to the technical scheme, the relation between the current inventory level and the safety inventory threshold value is automatically evaluated, early warning is given out, medical service efficiency reduction caused by insufficient inventory is avoided, the replenishment order is automatically generated and transmitted to related departments, the requirement for manual intervention is reduced, and the efficiency and accuracy of the whole replenishment process are improved.

The invention, which relates to the technical field of production line management, discloses an integrated management system applied to a magnetic ring production line, comprising a data acquisition module, a production progress monitoring module, a quality detection module, an equipment management module and an inventory management module. The data acquisition module is used for acquiring various data in the production process in real time by installing various sensors on a production line, such as raw material consumption, production equipment operation parameters, product size, weight and the like; according to the system, comprehensive automation and intelligent monitoring of the production process are realized, the production management efficiency is remarkably improved through implementation of the system, through real-time data acquisition and analysis, the accuracy and timeliness of production decision making are ensured, resource allocation is optimized, raw material and energy consumption is reduced, meanwhile, the inventory level is dynamically adjusted, and the production efficiency is improved. Fund occupation and inventory overstock are reduced, and effective control of cost is realized.

A method, computer program product, and computer system for a cloud-based medication tracking system. The system may include a plurality of intelligent injection devices across multiple healthcare facilities, each having: a barrel in fluid communication with a needle, a piston including a plunger, a microprocessor, a wireless communication module a supply chain tracking system configured to: monitor distribution of an injectable medication, verify availability of the injectable medication, track a global medication delivery pattern relating to the injectable medication, a machine learning system trained to: analyze medication usage data across healthcare entities, predict a supply chain event, optimize an inventory level, and improve a distribution datum.

The invention discloses an intelligent commodity management method, and relates to the technical field of commodity management. Through the design of the whole method, the efficiency can be improved, the intelligent system can automatically manage a plurality of goods management tasks such as inventory management, purchase decision making and demand prediction, manual intervention is reduced, the management efficiency is improved, inventory optimization is facilitated, the intelligent system can accurately predict demands through data analysis and prediction algorithms, and the management efficiency is improved. The inventory level is optimized according to the prediction result, the inventory overstock or stockout phenomenon is avoided, the inventory turnover rate is improved, the cost is reduced conveniently, the intelligent system can help enterprises to optimize purchasing strategies and reduce the inventory cost, the purchasing cost is reduced through price optimization and other modes, and therefore the overall cost of the enterprises is reduced, and the enterprise experience is improved. The customer satisfaction can be improved conveniently under the overall application, and the customer satisfaction can be improved and the customer loyalty can be enhanced by meeting customer requirements in time, accurately predicting the market trend and providing personalized services and intelligent commodity management.

The invention provides a business analysis system and method based on multi-dimensional data, relates to the field of inventory management, and adopts a data processing and coding mode based on artificial intelligence to perform time sequence feature analysis and association of product sales volume time sequence data and product inventory time sequence data. Therefore, whether the product stock needs to be increased or not is intelligently judged according to the time sequence collaborative interaction representation between the time sequence correlation characteristics of the product sales volume and the time sequence correlation characteristics of the product stock. Therefore, complex time sequence modes and dynamic changes in the time sequence data can be effectively captured, meanwhile, time sequence interaction characteristics between sales data and inventory data are integrated, the inventory level can be adjusted more comprehensively and accurately in real time, and automation of inventory decision making is achieved.

The invention provides a supply chain toughness and efficiency collaborative improvement method and system and electronic equipment, and the method comprises the steps: obtaining various supply chain management variable data of a supply chain, such as an inventory level, a supplier attribute, a customer order, transportation time, cost, a risk impact resistance coefficient, a recovery coefficient and the like; then, on the basis of the supply chain management variable data, various to-be-evaluated supply chain strategies meeting one or more constraint conditions of a preset continuous supply constraint condition, a preset factory production capacity constraint condition and a preset order fulfillment constraint condition are screened out, and a supply chain comprehensive target value is obtained through a calculation formula of the supply chain comprehensive target value. And outputting the optimal supply chain strategy with the maximum supply chain comprehensive target value. According to the embodiment of the invention, the optimal supply chain strategy which can guarantee the efficiency of the supply chain, can resist risk impact and can be quickly recovered can be determined, the comprehensive performance of the supply chain is further improved, and the sustainable development of an enterprise is maintained.

The invention discloses a supply chain material data management method and a data management system, and particularly relates to the technical field of data management. Through collecting and centrally storing material data of each link of a supply chain, an inventory turnover rate and residence time are analyzed, a safe inventory level is evaluated, and a purchase plan and inventory control are optimized. In order to solve the possible problem that inventory data is not updated in time or input errors occur, interference of the inventory data on inventory turnover rate calculation is evaluated through real-time analysis and abnormal input monitoring, and the accuracy of the inventory data is ensured; according to the method, the accuracy of the inventory turnover rate calculation result is divided into high, medium and low accuracy levels, and corresponding processing is carried out, so that purchase, production and supply chain decisions of enterprises are optimized, strategic decision errors are reduced, and the overall efficiency and response capability of a supply chain are improved.

The invention discloses an intelligent logistics supply chain management system, and the system comprises a block chain information sharing platform which is responsible for building an information sharing platform based on a block chain, inviting all participants on the supply chain to join, and storing and transmitting the data of each link of the supply chain through the distributed account book technology of the block chain; the product information tracing system is responsible for distributing a unique block chain ID for each fresh food and recording the circulation process of the fresh food in a supply chain; and the collaborative management platform is responsible for integrating and uniformly managing data of purchasing, warehousing, transportation and sales, predicting future sales trend and inventory demand change by using a random forest according to historical sales data and market trend information, and optimizing an inventory level, a reorder strategy, a sales strategy and a promotion activity plan in combination with a genetic algorithm, so as to achieve the purpose of sales promotion. The inventory cost is minimized, the sales demand is met, and the block chain information sharing platform and the collaborative management platform carry out data synchronization.

An intelligent warehouse management method based on the Internet of Things and artificial intelligence comprises the steps that 1) intelligent inventory management is carried out, and each commodity which is put in storage and put out of storage is automatically identified and tracked by utilizing a cargo container label and an image identification technology; the artificial intelligence technology is combined with big data analysis, future inventory demands are predicted, the inventory level is automatically adjusted, and the inventory cost and the stockout risk are reduced; 2) an automatic operation process: monitoring the equipment state in real time through the Internet of Things technology, ensuring the efficient operation of the equipment, and reducing the downtime; 3) performing real-time data analysis and decision support, and collecting various data generated in the storage process, including inventory data, order data and equipment operation data; deep learning and analysis are performed on storage data by using an artificial intelligence technology, intelligent decisions such as inventory prediction and path planning are realized, and storage management and logistics distribution processes are optimized; and 4) cross-system collaboration and information interconnection of a plurality of systems such as data sharing, storage, transportation, sales and the like are carried out to form end-to-end supply chain collaboration.

The invention relates to the technical field of inventory optimization, in particular to an intelligent supply chain demand prediction and inventory optimization system, which comprises a supply chain dynamic demand prediction module for intelligently predicting the demand quantity of a supply chain, and an inventory intelligent optimization module for intelligently optimizing the inventory according to the demand prediction result of the supply chain. The prediction correction and optimization adjustment module is used for dynamically correcting demand prediction and intelligently optimizing and adjusting inventory; according to the invention, through establishment of a three-channel cross validation prediction and anomaly elimination mechanism, single-channel prediction anomaly can be resisted, the stability and accuracy of supply chain demand prediction are improved, and inventory risks caused by prediction errors are reduced; the safe inventory level is dynamically set according to different stages, so that the inventory strategy better conforms to the commodity market change, and the shortage rate and the unsalable inventory are reduced; the correction amplitude is dynamically adjusted through error fluctuation, excessive correction is avoided, and the self-learning and self-evolution ability of a long-term supply chain prediction system is improved.

The invention relates to the technical field of collaborative operation of a production system and a logistics system in an intelligent manufacturing scene, and discloses an order distribution system for multi-bin supply of production raw materials. A data analysis module of the system extracts data from data sources such as a production plan and warehouse information, and generates a production raw material demand order; the scheme solving module is based on a multi-objective optimization model, comprehensively considers factors such as inventory, conveying time and equipment load rate, and calculates an optimal delivery warehouse and quantity; the distribution execution module dispatches the logistics equipment according to an optimization result to complete a warehouse-out task; the monitoring prediction module learns and predicts historical and real-time order data by using a long short-term memory network, and dynamically adjusts an allocation strategy; and the user interaction module provides a human-computer interaction interface. According to the system, efficient and economical production raw material JIT supply is achieved through an intelligent means, the logistics efficiency and the resource utilization rate are remarkably improved, and reliable technical support is provided for production and logistics collaboration in an intelligent manufacturing scene.

The invention relates to the technical field of live broadcast supply chain management, and discloses an agricultural product live broadcast supply chain management system based on a digital twin technology, which comprises a data acquisition module and a supply chain management module. According to the system, monitoring data of all links in an agricultural product supply chain, interaction data of a plurality of live broadcasting rooms and order data are acquired through a data acquisition module and are classified to form a data set, and a supply chain management module analyzes and generates a product score, a region score and a risk index; the supply chain management module is used for measuring the satisfaction degree of consumers to the quality of agricultural products and the regional agricultural product stock level, a supply chain is accurately optimized, and the satisfaction degree of the consumers is high; whether delay or unsalable sales exists in the supply chain, the satisfaction degree of consumers to the quality of agricultural products and the stock level of each provincial warehouse are judged, corresponding judgment results are generated and then transmitted to an enterprise information system, management strategies are dynamically adjusted, and the response speed is high.

The invention relates to a retail industry inventory management and demand prediction system, and the system comprises a data collection subsystem which is used for collecting the multi-dimensional data of the retail industry; the data preprocessing subsystem is used for carrying out cleaning, standardization and feature engineering on the collected data; the model training subsystem is used for generating a commodity demand prediction model by adopting big data and through a machine learning algorithm; the demand prediction subsystem is used for obtaining a prediction result based on the commodity demand prediction model; and the inventory optimization subsystem is used for dynamically adjusting the inventory level according to the prediction result so as to realize inventory optimization. According to the invention, by integrating the big data technology and the machine learning algorithm, automation and intelligence of inventory management and demand prediction are realized, and the management efficiency and the prediction accuracy are improved.

The invention discloses a retail cabinet inventory path decision optimization method and equipment, and the method comprises the following steps: (1), building an inventory path optimization model of a retail cabinet, and enabling the inventory path optimization model to take the minimum sum of the inventory cost, the stockout cost and the vehicle path cost at the current planning period as an objective function, taking a replenishment constraint, a maximum inventory level constraint, a vehicle capacity constraint, a retail cabinet flow balance constraint, a vehicle closed-loop constraint and an elimination sub-loop constraint as constraint conditions; (2) obtaining inventory path related information; and (3) solving the inventory path optimization model by adopting a tabu search method according to the inventory path related information to obtain an optimized inventory decision and path decision. According to the invention, the replenishment decision and the path planning decision are considered at the same time during decision making, and the efficiency is higher.