Multi-factory-oriented material neatness verification and collaborative delivery scheduling method and multi-factory-oriented material neatness verification and collaborative delivery scheduling system

Abstract

The invention discloses a multi-factory-oriented material registration verification and collaborative delivery scheduling method and system, and the method comprises the steps: obtaining the material inventory data, material attribute data, production plan data and order demand data of each factory in real time, and constructing a multi-dimensional data set; establishing a multi-factory unified material coding system and a dynamic data mapping relation based on the multi-dimensional data set; obtaining a bill of material of a target order, performing material alignment verification on the bill of material based on the multi-dimensional data set, the material coding system and the dynamic data mapping relationship, and outputting a verification report; based on the verification report, integrating to-be-delivered order information of each factory, constructing a global order priority sequence, and generating a cross-factory order combination scheme through a multi-objective optimization algorithm; and through dynamic verification and cooperative scheduling of the multi-factory material neat property, a delivery strategy can be adjusted in time according to dynamic changes of material inventory, production plans, transportation resources and the like of each factory.

Description

Technical Field

[0001] This invention relates to the field of supply chain collaborative management technology, and in particular to a method and system for material completeness verification and collaborative shipment scheduling for multiple factories. Background Technology

[0002] In manufacturing, while multi-factory production models can increase capacity and diversify risks, they also expose many management shortcomings. Currently, material completeness verification is mainly done manually. Because different factories produce different products, and the types and quantities of materials are numerous and varied, manual verification requires checking each item one by one from a massive amount of data, making the work extremely tedious.

[0003] Moreover, manual operation is susceptible to fatigue, emotional factors, and other distractions. Prolonged, high-intensity verification work makes it difficult for employees to maintain a high level of concentration. Any oversight can lead to errors in material quantity statistics or mismatched specifications. These errors can trigger a chain reaction in the production process, like dominoes, resulting in production stoppages and increased costs.

[0004] In terms of shipment scheduling, each factory operates independently, planning its own shipment schedule and focusing only on its own order needs, lacking communication and collaboration with other factories. This makes it impossible to effectively integrate transportation resources, often resulting in empty vehicles, unreasonable transportation routes, and a significant increase in transportation costs. At the same time, due to the different shipment progress of each factory, goods are difficult to arrive at their destinations at the same time, greatly extending the delivery cycle, affecting customer satisfaction, and even damaging the company's market reputation.

[0005] Therefore, there is an urgent need for a method and system for material completeness verification and collaborative delivery scheduling across multiple factories to solve the above problems. Summary of the Invention

[0006] The present invention aims to at least partially solve one of the technical problems in the aforementioned technologies. Therefore, a first aspect of the present invention aims to propose a method for material completeness verification and collaborative delivery scheduling across multiple factories. Through dynamic verification and collaborative scheduling of material completeness across multiple factories, delivery strategies can be adjusted in a timely manner based on dynamic changes in material inventory, production plans, and transportation resources at each factory.

[0007] The second objective of this invention is to provide a material completeness verification and collaborative delivery scheduling system for multiple factories.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a method for material kitting verification and collaborative shipment scheduling across multiple factories, comprising: Real-time acquisition of material inventory data, material attribute data, production plan data, and order demand data from each factory to construct a multi-dimensional dataset; and based on the multi-dimensional dataset, establishment of a unified material coding system and dynamic data mapping relationship for multiple factories; Obtain the bill of materials for the target order, perform material completeness verification on the bill of materials based on the multi-dimensional dataset, material coding system and dynamic data mapping relationship, and output a verification report; Based on the verification report, the order information of pending shipment from each factory is integrated to construct a global order priority sequence, and a cross-factory order combination scheme is generated through a multi-objective optimization algorithm. Based on the aforementioned cross-factory order combination scheme, real-time access to transportation resource pool data is used to generate a target delivery scheduling plan through route planning and capacity allocation algorithms. The target order's shipment is scheduled based on the target shipment scheduling plan.

[0009] Preferably, the completeness of the bill of materials is verified based on the multi-dimensional dataset, material coding system, and dynamic data mapping relationship, and a verification report is output, including: The bill of materials for the target order is parsed to extract the quantity requirements, specifications, and delivery time requirements for each material. Based on a unified material coding system and dynamic data mapping, the material inventory data of each factory is matched to screen out candidate materials that meet the specifications. The candidate materials are combined with their batch information and expiration dates to eliminate materials that have exceeded their expiration dates and to calculate the effective inventory quantity. Based on the effective inventory quantity, it is determined whether the effective inventory of a single factory meets the material quantity requirements. If it does, it is marked as a single factory complete set. If it does not, the feasibility of multi-factory joint complete set is calculated based on the cross-factory transfer cycle and transfer cost, and a cross-factory material transfer list is generated and marked as a joint complete set. If no feasible combined kit solution exists, then based on the material substitution relationship, query whether there is a substitute material solution. If a feasible combination of substitute materials exists, mark it as a substitute kit. If the material inventory data cannot meet the material requirements of the target order, it will be marked as a material shortage, and a material shortage list and replenishment suggestions will be output.

[0010] Preferably, a global order priority sequence is constructed, including: Extract the attribute information of each order to be shipped; the attribute information includes order amount, delivery time window, delivery address and material availability status; The weighting factors corresponding to each attribute of the order to be shipped were determined using the analytic hierarchy process. Calculate the priority score for each order based on the attribute information and the weighting factors corresponding to each attribute. The orders are sorted in descending order based on their priority scores to obtain the global order priority sequence.

[0011] Preferably, a multi-objective optimization algorithm is used to generate cross-factory order combination schemes, including: For each order in the global order priority sequence, with the objectives of minimizing total allocation and production costs, minimizing average order delivery delay time, and maximizing order merging rate, the non-dominated sorting genetic algorithm NSGA-Ⅲ is used to solve the problem and output the order combination scheme in the Pareto optimal solution set.

[0012] Preferably, generating a target shipment scheduling plan includes: For each combination scheme in the cross-factory order combination scheme, with the objectives of minimizing total transportation cost, minimizing average order delivery delay time, and maximizing transportation resource utilization, the non-dominated sorting genetic algorithm NSGA-Ⅲ is used to solve the problem and output the shipping scheduling plan in the Pareto optimal solution set.

[0013] Preferably, the method further includes: during the delivery scheduling process of the target order based on the target delivery scheduling plan, real-time monitoring of abnormal events during transportation, including vehicle malfunctions, road congestion, material damage, and temporary address changes by the recipient; when an abnormal event is detected, the route planning and capacity allocation algorithm is re-invoked based on the remaining transportation route, current capacity status, and order information to be delivered, to generate an emergency scheduling plan.

[0014] Preferably, it also includes forecasting the availability of materials for planned but not yet placed production orders and outputting risk warning information; The process of predicting material availability for planned but not yet placed production orders and outputting risk warning information includes: Obtain long-term order plan data; Based on historical production plan execution data, material procurement cycle data, and inventory consumption rate, a homogeneity prediction model is constructed using a Long Short-Term Memory (LSTM) network. Features are extracted from planned but not yet issued production orders in long-term order planning data. The extracted features are input into the completeness prediction model to predict the probability of completeness within a preset time node and output risk warning information. The risk warning information includes the name of the material that may be in short supply, the probability of material shortage, and suggestions for countermeasures.

[0015] Preferably, it also includes real-time statistics of production order information in each factory, displaying in real-time charts the material order factory completeness rate, substitution completeness rate, joint completeness rate, order scheduling progress, transportation resource usage status and delivery delay rate of each factory; and supports data filtering and statistical analysis by factory, order type and time period, generating multi-dimensional operation reports and displaying them visually.

[0016] Preferably, the emergency dispatch plan includes a backup vehicle dispatch strategy, route replanning, and order delivery time updates; the emergency dispatch plan is synchronized to the production system of the relevant factory through a message queue and the recipient is notified by email.

[0017] To achieve the above objectives, a second aspect of the present invention proposes a material kitting verification and collaborative shipment scheduling system for multiple factories, comprising: The acquisition module is used to acquire material inventory data, material attribute data, production plan data, and order demand data from each factory in real time, and to build a multi-dimensional dataset; and to establish a unified material coding system and dynamic data mapping relationship for multiple factories based on the multi-dimensional dataset; The verification module is used to obtain the bill of materials for the target order, perform material completeness verification on the bill of materials based on the multi-dimensional dataset, and output a verification report. The first generation module is used to integrate the order information of each factory to be shipped based on the verification report, construct a global order priority sequence, and generate a cross-factory order combination scheme through a multi-objective optimization algorithm; The second generation module is used to access transportation resource pool data in real time based on the cross-factory order combination scheme, and generate a target delivery scheduling plan through path planning and capacity allocation algorithms; The execution module is used to execute the shipment scheduling of the target order based on the target shipment scheduling plan.

[0018] This invention provides a method and system for material completeness verification and collaborative shipment scheduling across multiple factories. It acquires various types of data in real time to construct a multidimensional dataset, establishes a unified material coding system and dynamic data mapping relationships, breaks down data barriers between factories, and achieves efficient data sharing and interaction, laying a solid foundation for subsequent accurate decision-making. Based on multidimensional data and coding mapping relationships, it performs completeness verification on the target order's material list, outputting accurate verification reports. This allows for timely detection of material shortages or redundancies, avoiding production stoppages due to incomplete material sets and improving production efficiency. By integrating pending order information to construct a global priority sequence, it uses multi-objective optimization algorithms to generate cross-factory combination schemes, achieving scientific allocation of orders across multiple factories. Combined with transportation resource pool data, it uses route planning and capacity allocation algorithms to generate scheduling plans, reducing overall logistics costs and shortening order delivery cycles. The entire process emphasizes real-time data access and application, enabling timely adjustments to shipment scheduling based on dynamic changes in material inventory, production plans, and transportation resources at each factory, enhancing the flexibility and responsiveness of the supply chain.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a method for material kitting verification and collaborative shipment scheduling for multiple factories according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the construction of a global order priority sequence according to an embodiment of the present invention; Figure 3 This is a block diagram of a material kitting verification and collaborative delivery scheduling system for multiple factories according to an embodiment of the present invention. Detailed Implementation

[0022] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0023] Example 1: As Figure 1 As shown, a method for material kitting verification and collaborative shipment scheduling for multiple factories includes steps S1-S5: S1: Real-time acquisition of material inventory data, material attribute data, production plan data, and order demand data from each factory to construct a multi-dimensional dataset; and based on the multi-dimensional dataset, establish a unified material coding system and dynamic data mapping relationship for multiple factories; S2: Obtain the bill of materials for the target order, perform material completeness verification on the bill of materials based on the multi-dimensional dataset, material coding system and dynamic data mapping relationship, and output a verification report; S3: Based on the verification report, integrate the order information to be shipped from each factory, construct a global order priority sequence, and generate a cross-factory order combination scheme through a multi-objective optimization algorithm; S4: Based on the cross-factory order combination scheme, access the transportation resource pool data in real time, and generate a target delivery scheduling plan through path planning and capacity allocation algorithms; S5: Execute the shipment scheduling of the target order based on the target shipment scheduling plan.

[0024] In this embodiment, the material attribute data includes material substitution relationships, batch information, expiration date, and scarcity level.

[0025] In this embodiment, the inspection report includes the completeness result and a list of missing materials.

[0026] In this embodiment, the cross-factory order combination scheme includes order merging strategies, factory delivery division of labor, and material allocation paths.

[0027] In this embodiment, the transportation resource pool data includes the status of owned vehicles, third-party logistics capacity, and real-time road condition information.

[0028] In this embodiment, the target delivery scheduling plan includes vehicle allocation, delivery route optimization, and delivery time estimation.

[0029] The working principle and beneficial effects of the above technical solution are as follows: Real-time acquisition of multiple types of data to construct a multidimensional dataset, establishing a unified material coding system and dynamic data mapping relationships, breaking down data barriers between factories, and achieving efficient data sharing and interaction, laying a solid foundation for subsequent accurate decision-making; Based on multidimensional data and coding mapping relationships, the completeness verification of the target order's material list is performed, outputting accurate verification reports, enabling timely detection of material shortages or redundancies, avoiding production stoppages due to incomplete material sets, and improving production efficiency; By integrating pending order information to construct a global priority sequence, multi-objective optimization algorithms are used to generate cross-factory combination schemes, achieving scientific allocation of orders among multiple factories; Combining transportation resource pool data, route planning and capacity allocation algorithms are used to generate scheduling plans, reducing overall logistics costs and shortening order delivery cycles; The entire process emphasizes the access and application of real-time data, enabling timely adjustments to delivery scheduling based on dynamic changes in material inventory, production plans, and transportation resources at each factory, enhancing the flexibility and responsiveness of the supply chain.

[0030] Example 2: Based on the multi-dimensional dataset, material coding system, and dynamic data mapping relationship, perform material completeness verification on the bill of materials, and output a verification report, including: The bill of materials for the target order is parsed to extract the quantity requirements, specifications, and delivery time requirements for each material. Based on a unified material coding system and dynamic data mapping, the material inventory data of each factory is matched to screen out candidate materials that meet the specifications. The candidate materials are combined with their batch information and expiration dates to eliminate materials that have exceeded their expiration dates and to calculate the effective inventory quantity. Based on the effective inventory quantity, it is determined whether the effective inventory of a single factory meets the material quantity requirements. If it does, it is marked as a single factory complete set. If it does not, the feasibility of multi-factory joint complete set is calculated based on the cross-factory transfer cycle and transfer cost, and a cross-factory material transfer list is generated and marked as a joint complete set. If no feasible combined kit solution exists, then based on the material substitution relationship, query whether there is a substitute material solution. If a feasible combination of substitute materials exists, mark it as a substitute kit. If the material inventory data cannot meet the material requirements of the target order, it will be marked as a material shortage, and a material shortage list and replenishment suggestions will be output.

[0031] In this embodiment, by parsing the bill of materials to extract key information and combining it with a unified material coding system and dynamic data mapping relationships to filter candidate materials, materials that meet the specifications can be accurately located, improving matching efficiency and accuracy. Considering material batches and expiration dates, expired materials are eliminated and effective inventory is calculated to ensure that usable materials are used, avoiding resource waste. Differentiating between single-factory complete sets, joint complete sets, alternative complete sets, and material shortages, targeted solutions are provided for different scenarios, enhancing flexibility in dealing with material shortages. When joint complete sets are used, allocation cycles and costs are considered; when alternative complete sets are used, the most cost-effective solution is selected, helping to reduce overall operating costs. For material shortages, detailed outputs and replenishment suggestions are provided, facilitating timely measures by enterprises to ensure smooth order execution.

[0032] Example 3: As Figure 2 As shown, the global order priority sequence is constructed, including steps S31-S34: S31: Extract the attribute information of each order to be shipped; the attribute information includes order amount, delivery time window, delivery address and material availability status; S32: Use the analytic hierarchy process to determine the weight factors corresponding to each attribute of the order to be shipped; S33: Calculate the priority score for each order based on the attribute information and the weighting factors corresponding to each attribute; S34: Sort each order in descending order according to its priority score to obtain the global order priority sequence.

[0033] In this embodiment, the priority score for each order is calculated based on the attribute information and the weight factors corresponding to each attribute, including: ; in, Score the order priority; , , These are weighting coefficients, all greater than 0 and summing to 1; , representing the non-negative delay time; This is a preset maximum tolerable latency threshold; The shipping distance between the order's delivery address and the nearest factory; This represents the maximum shipping distance for all orders in the current pending order set. This refers to the order amount. This represents the maximum value of all order amounts in the current pending order set.

[0034] The working principle and beneficial effects of the above technical solution are as follows: comprehensively consider multi-attribute information to evaluate orders, avoiding the one-sidedness of single-factor decision-making; objectively determine weights using the analytic hierarchy process, aligning with actual business needs; quantify priority scores, intuitively and accurately compare order priorities; allocate resources according to priority, improve utilization efficiency, and meet the needs of important customers; clarify priority sequences, reduce confusion and delays, and accelerate processing and delivery speed.

[0035] Example 4: Generating cross-factory order combination schemes using a multi-objective optimization algorithm, including: For each order in the global order priority sequence, with the objectives of minimizing total allocation and production costs, minimizing average order delivery delay time, and maximizing order merging rate, the non-dominated sorting genetic algorithm NSGA-Ⅲ is used to solve the problem and output the order combination scheme in the Pareto optimal solution set.

[0036] In this embodiment, the multi-objective optimization algorithm is a non-dominated sorting genetic algorithm.

[0037] In this embodiment, the total allocation and production cost includes the cost of material transfer across factories and the cost of production adjustment.

[0038] In this embodiment, the delivery delay time is the difference between the actual delivery time of the order and the agreed delivery time.

[0039] In this embodiment, the order consolidation rate is the proportion of the number of orders that can be consolidated for shipment to the total number of orders.

[0040] The working principle and beneficial effects of the above technical solution are: minimizing total allocation and production costs, reducing enterprise operating costs, and improving economic efficiency; minimizing the average order delivery delay time, improving the on-time delivery rate of orders, and enhancing customer satisfaction; maximizing the order consolidation rate, optimizing resource allocation, and improving production and logistics efficiency; and NSGA-Ⅲ outputs Pareto optimal solution sets, providing a variety of order combination schemes for enterprises to choose flexibly.

[0041] Example 5: Generating a target shipment scheduling plan, including: For each combination scheme in the cross-factory order combination scheme, with the objectives of minimizing total transportation cost, minimizing average order delivery delay time, and maximizing transportation resource utilization, the non-dominated sorting genetic algorithm NSGA-Ⅲ is used to solve the problem and output the shipping scheduling plan in the Pareto optimal solution set.

[0042] In this embodiment, the total transportation cost includes vehicle usage cost, fuel cost, and third-party logistics service fee.

[0043] In this embodiment, the transportation resource utilization rate is the average ratio of the actual loading volume to the vehicle's rated loading volume.

[0044] The working principle and beneficial effects of the above technical solutions are as follows: By minimizing total transportation costs, enterprises can reduce logistics expenses and increase profit margins. Optimized shipment scheduling plans can select more economical transportation routes and factory combinations, avoiding unnecessary transportation costs; minimizing the average order delivery delay time can ensure on-time order delivery and improve customer satisfaction. Timely delivery can enhance customer trust in the enterprise and promote long-term cooperation; maximizing the utilization rate of transportation resources can fully utilize the efficiency of transportation equipment and manpower, reducing resource waste; improving the utilization rate of transportation resources can reduce the enterprise's operating costs and improve overall efficiency; the Pareto optimal solution set provides multiple different shipment scheduling schemes, which enterprises can choose according to their own actual situation and preferences; these schemes can help enterprises weigh costs, delivery time, and resource utilization, and make more reasonable decisions.

[0045] Example 6: Further includes: during the shipment scheduling process of the target order based on the target shipment scheduling plan, real-time monitoring of abnormal events during transportation, including vehicle malfunctions, road congestion, material damage, and temporary address changes by the recipient; when an abnormal event is detected, based on the remaining transportation route, current capacity status, and order information to be delivered, the route planning and capacity allocation algorithm is re-invoked to generate an emergency scheduling plan; the emergency scheduling plan includes a backup vehicle scheduling strategy, route replanning, and order delivery time updates; the emergency scheduling plan is synchronized to the production system of the relevant factory through a message queue and the recipient is notified via email.

[0046] The working principle and beneficial effects of the above technical solution are as follows: Real-time monitoring of transportation anomalies enables the immediate detection of vehicle malfunctions, road congestion, and other issues, allowing for timely countermeasures to ensure smooth transportation, reduce the risk of order delivery failures due to unforeseen circumstances, and make cargo transportation more reliable; When an anomaly is detected, it quickly replans based on remaining transportation routes, capacity status, and pending order information, generating emergency dispatch plans such as backup vehicle dispatch and route replanning, enabling flexible responses to emergencies and reducing the impact of anomalies on transportation; By updating order delivery times through emergency dispatch plans, it minimizes delays caused by anomalies, ensuring orders are delivered on time or close to the original schedule as much as possible, maintaining the cooperative relationship between the company and its customers; The emergency dispatch plan is synchronized to relevant factory production systems via message queues, ensuring that factories can adjust production arrangements in a timely manner; Notifications to recipients via email allow them to stay informed about the transportation status, improving information transparency and communication efficiency.

[0047] Example 7: It also includes forecasting the availability of materials for planned but not yet placed production orders and outputting risk warning information; The process of predicting material availability for planned but not yet placed production orders and outputting risk warning information includes: Obtain long-term order plan data; Based on historical production plan execution data, material procurement cycle data, and inventory consumption rate, a homogeneity prediction model is constructed using a Long Short-Term Memory (LSTM) network. Features are extracted from planned but not yet issued production orders in long-term order planning data. The extracted features are input into the completeness prediction model to predict the probability of completeness within a preset time node and output risk warning information. The risk warning information includes the name of the material that may be in short supply, the probability of material shortage, and suggestions for countermeasures.

[0048] The working principle and beneficial effects of the above technical solution are as follows: By predicting the availability of materials for planned but not yet issued production orders, potential material shortage risks can be identified in advance. Enterprises can take measures before problems occur to avoid production stoppages due to material shortages and ensure production continuity. The solution outputs risk warning information including the names of materials that may be in short supply, the probability of shortage, and suggested countermeasures, providing enterprises with detailed and targeted references. This enables enterprises to accurately manage and schedule materials and rationally allocate production resources. By using a Long Short-Term Memory (LSTM) network combined with historical production plan execution data, material procurement cycle data, and inventory consumption rates to build a predictive model, the temporal characteristics of the data are fully considered, enabling more accurate prediction of material availability probabilities and reducing production losses due to prediction errors. Based on accurate risk warnings, enterprises can optimize material procurement plans, adjust inventory levels, avoid over-purchasing or inventory backlog, achieve efficient resource allocation, and reduce production costs.

[0049] Example 8: It also includes real-time statistics of production order information in each factory, and displays the material order factory completeness rate, substitution completeness rate, joint completeness rate, order scheduling progress, transportation resource usage status and delivery delay rate of each factory in the form of charts in real time; it supports data filtering and statistical analysis by factory, order type and time period, generates multi-dimensional operation reports and displays them visually.

[0050] The working principle and beneficial effects of the above technical solution are as follows: it presents multiple key indicators of each factory in real time with charts, helping managers to quickly grasp the overall picture of production; it supports multi-dimensional data filtering and analysis, generates reports for visual display, and assists in accurate decision-making; it promptly identifies and optimizes production bottlenecks, improving overall operational efficiency; it facilitates data sharing between departments and factories, enhancing collaboration; and it monitors key indicators to identify and address potential risks in advance.

[0051] Example 9: The emergency dispatch plan includes a backup vehicle dispatch strategy, route replanning, and order delivery time updates; the emergency dispatch plan is synchronized to the production system of the relevant factory through a message queue and the recipient is notified by email.

[0052] The working principle and beneficial effects of the above technical solution are as follows: Real-time monitoring of transportation anomalies enables the immediate detection of vehicle malfunctions, road congestion, and other issues, allowing for timely countermeasures to ensure smooth transportation, reduce the risk of order delivery failures due to unforeseen circumstances, and make cargo transportation more reliable; When an anomaly is detected, it quickly replans based on remaining transportation routes, capacity status, and pending order information, generating emergency dispatch plans such as backup vehicle dispatch and route replanning, enabling flexible responses to emergencies and reducing the impact of anomalies on transportation; By updating order delivery times through emergency dispatch plans, it minimizes delays caused by anomalies, ensuring orders are delivered on time or close to the original schedule as much as possible, maintaining the cooperative relationship between the company and its customers; The emergency dispatch plan is synchronized to relevant factory production systems via message queues, ensuring that factories can adjust production arrangements in a timely manner; Notifications to recipients via email allow them to stay informed about the transportation status, improving information transparency and communication efficiency.

[0053] To achieve the above objectives, a second aspect of the present invention proposes a material kitting verification and collaborative shipment scheduling system for multiple factories, comprising: The acquisition module is used to acquire material inventory data, material attribute data, production plan data, and order demand data from each factory in real time, and to build a multi-dimensional dataset; and to establish a unified material coding system and dynamic data mapping relationship for multiple factories based on the multi-dimensional dataset; The verification module is used to obtain the bill of materials for the target order, perform material completeness verification on the bill of materials based on the multi-dimensional dataset, and output a verification report. The first generation module is used to integrate the order information of each factory to be shipped based on the verification report, construct a global order priority sequence, and generate a cross-factory order combination scheme through a multi-objective optimization algorithm; The second generation module is used to access transportation resource pool data in real time based on the cross-factory order combination scheme, and generate a target delivery scheduling plan through path planning and capacity allocation algorithms; The execution module is used to execute the shipment scheduling of the target order based on the target shipment scheduling plan.

[0054] The working principle and beneficial effects of the above technical solution are as follows: Real-time acquisition of multiple types of data to construct a multidimensional dataset, establishing a unified material coding system and dynamic data mapping relationships, breaking down data barriers between factories, and achieving efficient data sharing and interaction, laying a solid foundation for subsequent accurate decision-making; Based on multidimensional data and coding mapping relationships, the completeness verification of the target order's material list is performed, outputting accurate verification reports, enabling timely detection of material shortages or redundancies, avoiding production stoppages due to incomplete material sets, and improving production efficiency; By integrating pending order information to construct a global priority sequence, multi-objective optimization algorithms are used to generate cross-factory combination schemes, achieving scientific allocation of orders among multiple factories; Combining transportation resource pool data, route planning and capacity allocation algorithms are used to generate scheduling plans, reducing overall logistics costs and shortening order delivery cycles; The entire process emphasizes the access and application of real-time data, enabling timely adjustments to delivery scheduling based on dynamic changes in material inventory, production plans, and transportation resources at each factory, enhancing the flexibility and responsiveness of the supply chain.

[0055] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for material kitting verification and collaborative shipment scheduling across multiple factories, characterized in that, include: Real-time acquisition of material inventory data, material attribute data, production plan data, and order demand data from each factory to construct a multi-dimensional dataset; Furthermore, a unified material coding system and dynamic data mapping relationship were established for multiple factories based on multi-dimensional datasets; Obtain the bill of materials for the target order, perform material completeness verification on the bill of materials based on the multi-dimensional dataset, material coding system and dynamic data mapping relationship, and output a verification report; Based on the verification report, the order information of pending shipment from each factory is integrated to construct a global order priority sequence, and a cross-factory order combination scheme is generated through a multi-objective optimization algorithm. Based on the cross-factory order combination scheme, real-time access to transportation resource pool data is used to generate a target delivery scheduling plan; The target order's shipment is scheduled based on the target shipment scheduling plan.

2. The material kitting verification and collaborative shipment scheduling method for multiple factories as described in claim 1, characterized in that, Based on the aforementioned multi-dimensional dataset, material coding system, and dynamic data mapping relationship, the bill of materials is subjected to material completeness verification, and a verification report is output, including: The bill of materials for the target order is parsed to extract the quantity requirements, specifications, and delivery time requirements for each material. Based on a unified material coding system and dynamic data mapping, the material inventory data of each factory is matched to screen out candidate materials that meet the specifications. The candidate materials are combined with their batch information and expiration dates to eliminate materials that have exceeded their expiration dates and to calculate the effective inventory quantity. Based on the effective inventory quantity, it is determined whether the effective inventory of a single factory meets the material quantity requirements. If it does, it is marked as a single factory complete set. If it does not, the feasibility of multi-factory joint complete set is calculated based on the cross-factory transfer cycle and transfer cost, and a cross-factory material transfer list is generated and marked as a joint complete set. If no feasible combined kit solution exists, then based on the material substitution relationship, query whether there is a substitute material solution. If a feasible combination of substitute materials exists, mark it as a substitute kit. If the material inventory data cannot meet the material requirements of the target order, it will be marked as a material shortage, and a material shortage list and replenishment suggestions will be output.

3. The material kitting verification and collaborative shipment scheduling method for multiple factories as described in claim 1, characterized in that, Construct a global order priority sequence, including: Extract the attribute information of each order to be shipped; the attribute information includes order amount, delivery time window, delivery address and material availability status; The weighting factors corresponding to each attribute of the order to be shipped were determined using the analytic hierarchy process. Calculate the priority score for each order based on the attribute information and the weighting factors corresponding to each attribute. The orders are sorted in descending order based on their priority scores to obtain the global order priority sequence.

4. The material completeness verification and collaborative delivery scheduling method for multiple factories as described in claim 1, characterized in that, Generate cross-factory order combination solutions using a multi-objective optimization algorithm, including: For each order in the global order priority sequence, with the objectives of minimizing total allocation and production costs, minimizing average order delivery delay time, and maximizing order merging rate, the non-dominated sorting genetic algorithm NSGA-Ⅲ is used to solve the problem and output the order combination scheme in the Pareto optimal solution set.

5. The material kitting verification and collaborative shipment scheduling method for multiple factories as described in claim 1, characterized in that, Generate the target shipment scheduling plan, including: For each combination scheme in the cross-factory order combination scheme, with the objectives of minimizing total transportation cost, minimizing average order delivery delay time, and maximizing transportation resource utilization, the non-dominated sorting genetic algorithm NSGA-Ⅲ is used to solve the problem and output the shipping scheduling plan in the Pareto optimal solution set.

6. The material kitting verification and collaborative shipment scheduling method for multiple factories as described in claim 1, characterized in that, Also includes: During the delivery scheduling process of the target order based on the target delivery scheduling plan, abnormal events in the transportation process are monitored in real time. These abnormal events include vehicle malfunctions, road congestion, material damage, and temporary changes in the recipient's address. When an abnormal event is detected, the route planning and capacity allocation algorithm is re-invoked based on the remaining transportation route, the current capacity status, and the order information to be delivered, to generate an emergency scheduling plan.

7. The material kitting verification and collaborative shipment scheduling method for multiple factories as described in claim 1, characterized in that, This also includes forecasting the availability of materials for planned but not yet placed production orders and outputting risk warning information; The process of predicting material availability for planned but not yet placed production orders and outputting risk warning information includes: Obtain long-term order plan data; Based on historical production plan execution data, material procurement cycle data, and inventory consumption rate, a homogeneity prediction model is constructed using a Long Short-Term Memory (LSTM) network. Features are extracted from planned but not yet issued production orders in long-term order planning data. The extracted features are input into the completeness prediction model to predict the probability of completeness within a preset time node and output risk warning information. The risk warning information includes the name of the material that may be in short supply, the probability of material shortage, and suggestions for countermeasures.

8. The material kitting verification and collaborative shipment scheduling method for multiple factories as described in claim 1, characterized in that, It also includes real-time statistics on production order information in each factory, displaying in chart form the material order factory completeness rate, substitution completeness rate, joint completeness rate, order scheduling progress, transportation resource usage status and delivery delay rate of each factory; it supports data filtering and statistical analysis by factory, order type and time period, generating multi-dimensional operation reports and displaying them visually.

9. The material kitting verification and collaborative shipment scheduling method for multiple factories as described in claim 6, characterized in that, The emergency dispatch plan includes a backup vehicle dispatch strategy, route replanning, and order delivery time updates; the emergency dispatch plan is synchronized to the production system of the relevant factory through a message queue and the recipient is notified by email.

10. A system applying the material kitting verification and collaborative shipment scheduling method for multiple plants as described in any one of claims 1-9, characterized in that, include: The acquisition module is used to acquire material inventory data, material attribute data, production plan data, and order demand data from each factory in real time, and to build a multi-dimensional dataset. Furthermore, a unified material coding system and dynamic data mapping relationship were established for multiple factories based on multi-dimensional datasets; The verification module is used to obtain the bill of materials for the target order, perform material completeness verification on the bill of materials based on the multi-dimensional dataset, and output a verification report. The first generation module is used to integrate the order information of each factory to be shipped based on the verification report, construct a global order priority sequence, and generate a cross-factory order combination scheme through a multi-objective optimization algorithm; The second generation module is used to access transportation resource pool data in real time based on the cross-factory order combination scheme, and generate a target delivery scheduling plan through path planning and capacity allocation algorithms; The execution module is used to execute the shipment scheduling of the target order based on the target shipment scheduling plan.

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