A multi-bin collaborative dynamic optimization method suitable for multi-variety variable batch mixed-line production

Abstract

The application discloses a multi-bin collaborative dynamic optimization method suitable for multi-variety variable-batch mixed-line production, relates to the technical field of logistics distribution optimization, and comprises the following steps: multi-bin scene analysis problem modeling, analyzing the multi-bin scene; multi-bin material state correlation analysis, constructing the correlation relationship between the multi-bins based on the multi-bin scene model; multi-bin collaborative material adjustment threshold dynamic optimization, constructing an environment containing workshops, inner bins and outer bins, and solving the multi-bin collaborative optimal material adjustment threshold; production logistics collaborative mechanism, constructing a production logistics collaborative double-layer optimization model, and realizing deep collaboration between production and logistics; production logistics collaborative scheduling optimization, combining a multi-objective optimization algorithm and the production logistics collaborative mechanism, taking the minimization of the maximum completion time and production waiting time and the maximization of task benefits and the minimization of material waiting time as the target, realizing bidirectional feedback of the production and logistics states, optimizing the distribution priority and production scheduling, and evaluating the production benefits from the production and material levels.

Description

Technical Field

[0001] This invention belongs to the field of manufacturing service scheduling technology in service-oriented manufacturing systems, specifically involving a multi-warehouse collaborative dynamic optimization method adapted to mixed production lines with varying batch sizes and multiple product types. Background Technology

[0002] In the 3C manufacturing sector, the promotion and popularization of intelligent manufacturing has placed higher demands on workshop production and logistics efficiency. Currently, 3C manufacturing workshops face many pressing issues in production logistics management.

[0003] In multi-warehouse collaborative scheduling scenarios, 3C workshop products are characterized by multiple varieties, small batches, and high-frequency production changes, making traditional single-warehouse management models and fixed threshold setting methods inadequate. Lack of coordination in material allocation across multiple warehouses during workshop production, insufficient information sharing between internal and external warehouses, and unclear relationships lead to unreasonable material requisition and allocation threshold settings, easily causing production line shutdowns, production interruptions, or warehouse overload, severely impacting production efficiency.

[0004] In terms of production and logistics collaboration, most existing studies model the production and logistics processes independently, making effective collaboration between them impossible. The dynamic and interconnected nature of 3C workshop production, with significant differences in material demand across different work sections, means that traditional scheduling strategies fail to fully exploit the potential correlations between production and logistics data. This makes it difficult to cope with demand fluctuations caused by frequent production changes, often resulting in problems such as "production lines waiting for materials" or "material backlog," leading to low production efficiency and high inventory costs.

[0005] For high-frequency production change scenarios, the status of 3C workshops changes dynamically in real time. Resource constraints require some production tasks to switch production lines to improve efficiency. However, existing methods lack accurate prediction and proactive scheduling of production change needs, making it difficult to reasonably arrange production change scheduling and coordinate material transportation in a highly dynamic environment, and thus failing to meet complex production needs.

[0006] To address the above issues, a cluster detection and scheduling algorithm testing and recommendation method and system adapted to multiple scenarios is needed. This system includes multi-warehouse scenario analysis problem modeling, multi-warehouse material state correlation analysis, multi-warehouse collaborative material adjustment threshold dynamic optimization, production logistics collaboration mechanism, and production logistics collaborative scheduling optimization. By analyzing multi-warehouse collaborative scenarios, multi-warehouse collaborative material correlation is constructed, thereby realizing multi-warehouse collaborative dynamic material threshold optimization. Based on the threshold optimization of multi-warehouse collaboration, a production logistics collaboration mechanism is further constructed, and combined with a multi-objective optimization algorithm, the scheduling optimization of production logistics collaboration is achieved. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a multi-warehouse collaborative dynamic optimization method adapted to mixed-batch production lines with multiple product varieties. This invention comprises five steps: multi-warehouse scenario analysis and problem modeling, multi-warehouse material state correlation analysis, dynamic optimization of multi-warehouse collaborative material adjustment thresholds, production logistics collaboration mechanism, and production logistics collaborative scheduling optimization. This invention can analyze multi-warehouse collaborative scenarios, construct multi-warehouse collaborative material correlations, and then achieve dynamic material threshold optimization for multi-warehouse collaboration. Based on the threshold optimization of multi-warehouse collaboration, a production logistics collaboration mechanism is further constructed, and combined with a multi-objective optimization algorithm, production logistics collaborative scheduling optimization is achieved.

[0008] The technical problem solved by this invention is achieved through the following technical solution:

[0009] A multi-warehouse collaborative dynamic optimization method adapted to multi-product, variable-batch, mixed-line production includes the following steps:

[0010] Step 1. Abstract the workshop, inner warehouse, and outer warehouse into scenario models. In the workshop model, set the workshop capacity, task types and quantities, arrival and deadline times, and the demand for various materials for each task, ensuring that the remaining material quantity is non-negative, and establish a mapping relationship between task quantity and material demand. In the inner warehouse model, set the capacity, quantity of various materials, consumption and replenishment, and keep the material quantity non-negative. In the outer warehouse model, set the capacity, quantity of various materials, consumption and replenishment, and keep the material quantity non-negative. Set the material calling threshold and material adjustment threshold in real time to trigger the replenishment chain of the workshop, inner warehouse, and outer warehouse.

[0011] Step 2. Multi-warehouse material status correlation analysis: Construct transportation status quantities for workshop, inner warehouse, and outer warehouse, and calculate transportation coefficients based on distance and transportation speed; calculate the correlation coefficients between inner and outer warehouses and between outer warehouses based on the storage status of material types in each entity; integrate the transportation coefficients and correlation coefficients to calculate the transportation correlation coordination coefficient.

[0012] Step 3. Dynamic optimization of multi-warehouse collaborative seasoning threshold: Construct an environment including workshop, internal warehouse and external warehouse. Based on the evaluation of task requirement status, warehouse material status and multi-warehouse collaborative status, configure the objective function, constraints and optimization objectives of the optimization problem, and solve for the optimal seasoning threshold for multi-warehouse collaboration.

[0013] Step 4. Production logistics collaboration mechanism: Construct a two-layer optimization model for production logistics collaboration. In this model, the upper production scheduling optimization layer integrates task and work section models to generate production plans and receives constraint feedback from the collaboration layer. The collaboration layer coordinates tasks, work sections, logistics, and materials, clarifies the collaboration goals of production waiting time and task benefits, and transmits status updates. The lower logistics distribution optimization layer optimizes distribution through logistics and material models, while setting constraints on internal warehouse inventory and work section sequence. The optimization goal is to minimize total waiting time and maximize task benefits, thereby achieving deep collaboration between production and logistics.

[0014] Step 5. Production logistics collaborative scheduling optimization: Combining multi-objective optimization algorithms and production logistics collaborative mechanisms, the upper-level production scheduling optimization layer aims to minimize the maximum completion time, production waiting time and maximize task benefits, while the lower-level logistics distribution optimization layer aims to minimize material waiting time. Through the collaborative layer, bidirectional feedback between production and logistics status is achieved to optimize distribution priority and production scheduling.

[0015] The present invention has the following beneficial effects:

[0016] (1) This invention constructs a multi-warehouse collaborative multi-level dynamic model, integrates workshop and warehouse status assessments, and uses reinforcement learning algorithms to achieve adaptive adjustment of material transfer thresholds between internal and external warehouses. Unlike the traditional fixed threshold mode, this mechanism can dynamically optimize the material calling and replenishment thresholds based on real-time production status (such as material consumption rate and warehouse capacity), avoid production line material shortages or warehouse overload, and achieve collaborative scheduling of materials in multiple warehouses.

[0017] This invention proposes a two-layer collaborative optimization architecture for production and logistics: the upper layer constructs a task-workshop matching model to optimize production scheduling, while the lower layer establishes a material-task matching model for dynamic scheduling and delivery. By improving the multi-objective algorithm to achieve multi-objective Pareto optimal solution search, it overcomes the limitations of traditional independent modeling of production and logistics, and solves the collaborative failure problem of "production line waiting for materials" and "material backlog". Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings.

[0020] This invention discloses a multi-warehouse collaborative dynamic optimization method adapted to multi-variety, variable-batch mixed-line production, such as... Figure 1As shown, this method comprises five steps: multi-warehouse scenario analysis problem modeling, multi-warehouse material state correlation analysis, dynamic optimization of multi-warehouse collaborative material adjustment thresholds, production logistics collaboration mechanism, and production logistics collaborative scheduling optimization. This invention can analyze multi-warehouse collaborative scenarios, construct multi-warehouse collaborative material correlations, and then achieve dynamic material threshold optimization for multi-warehouse collaboration. Based on the threshold optimization of multi-warehouse collaboration, a production logistics collaboration mechanism is further constructed, and combined with a multi-objective optimization algorithm, the scheduling optimization of production logistics collaboration is achieved. Specific implementation methods are as follows:

[0021] Step 1. Abstract the workshop, inner warehouse, and outer warehouse into scenario models. In the workshop model, set the workshop capacity, task types and quantities, arrival and deadline times, and the demand for various materials for each task, ensuring that the remaining material quantity is non-negative, and establish a mapping relationship between task quantity and material demand. In the inner warehouse model, set the capacity, quantity of various materials, consumption and replenishment, and keep the material quantity non-negative. In the outer warehouse model, set the capacity, quantity of various materials, consumption and replenishment, and keep the material quantity non-negative. Set the material calling threshold and material adjustment threshold in real time to trigger the replenishment chain of the workshop, inner warehouse, and outer warehouse.

[0022] Step 2. Multi-warehouse material status correlation analysis: Construct transportation status quantities for workshop, inner warehouse, and outer warehouse, and calculate transportation coefficients based on distance and transportation speed; calculate the correlation coefficients between inner and outer warehouses and between outer warehouses based on the storage status of material types in each entity; integrate the transportation coefficients and correlation coefficients to calculate the transportation correlation coordination coefficient.

[0023] Step 3. Dynamic optimization of multi-warehouse collaborative seasoning threshold: Construct an environment including workshop, internal warehouse and external warehouse. Based on the evaluation of task requirement status, warehouse material status and multi-warehouse collaborative status, configure the objective function, constraints and optimization objectives of the optimization problem, and solve for the optimal seasoning threshold for multi-warehouse collaboration.

[0024] Step 4. Production logistics collaboration mechanism: Construct a two-layer optimization model for production logistics collaboration. In this model, the upper production scheduling optimization layer integrates task and work section models to generate production plans and receives constraint feedback from the collaboration layer. The collaboration layer coordinates tasks, work sections, logistics, and materials, clarifies the collaboration goals of production waiting time and task benefits, and transmits status updates. The lower logistics distribution optimization layer optimizes distribution through logistics and material models, while setting constraints on internal warehouse inventory and work section sequence. The optimization goal is to minimize total waiting time and maximize task benefits, thereby achieving deep collaboration between production and logistics.

[0025] Step 5. Production logistics collaborative scheduling optimization: Combining multi-objective optimization algorithms and production logistics collaborative mechanisms, the upper-level production scheduling optimization layer aims to minimize the maximum completion time, production waiting time and maximize task benefits, while the lower-level logistics distribution optimization layer aims to minimize material waiting time. Through the collaborative layer, bidirectional feedback between production and logistics status is achieved to optimize distribution priority and production scheduling.

[0026] The more specific implementation method for step 1 is as follows:

[0027] Step 1 specifically includes:

[0028] Multi-warehouse problem modeling. Production tasks can be issued by multiple workshops, and the materials required for production are usually replenished from the production line to the internal warehouse. The internal warehouse directly transports materials to the production line. When the internal warehouse is short of materials, it sends a material transfer signal to the external warehouse, which then replenishes the internal warehouse. A workshop typically corresponds to only one internal warehouse, but the materials in one internal warehouse can be replenished by multiple external warehouses. Multi-warehouse material distribution scenarios involve two collaboration modes: same-level collaboration between external warehouses and multi-level collaboration between internal and external warehouses. In the multi-warehouse material distribution scenario, a scenario model, a workshop model, an internal warehouse model, and an external warehouse model are constructed, and threshold parameters are set to model the workshop material shortage level, workshop material consumption rate, warehouse material shortage level, warehouse inbound / outbound rate, material request threshold, and material transfer threshold.

[0029] ①Scenario Model Construction: The multi-warehouse collaborative scenario consists of three parts: workshops, inner warehouses, and outer warehouses. The number of workshops is... The number of internal warehouses is The number of external warehouses is Typically, the number of workshops equals the number of internal warehouses, that is... The workshops are grouped as follows: ,in Indicates the first One workshop; the inner warehouse is assembled as follows: ,in Indicates the first One internal warehouse; the external warehouse is a collection of... ,in Indicates the first There are one external warehouse, and the materials required for production total... kind.

[0030] ② Workshop Model Construction: The number of required tasks in the workshop is The required tasks are as follows: Seeds, workshop The capacity is , Time Workshop middle The number of tasks is ,in For the workshop middle Number of tasks, task set is ,in Indicates the first indivual Class of tasks, For the first indivual The arrival time of the task. For the first indivual Deadlines for different task types; corresponding deadlines for each type of task. The material requirements are ,in express Class of requirements task for the first The demand for this type of material. There is a certain amount of material available in the workshop for production. Time Workshop In The quantity of this type of material is , Time Workshop In The consumption of this type of material is , Time Workshop The material replenishment amount is , Time Workshop The remaining amount of material in the container is , Time Workshop right The required quantity of this material is .

[0031] (1)

[0032] That is, the remaining amount of material at the current moment is the sum of the remaining amount of material at the previous moment and the remaining amount of material after utilization at this moment; the remaining amount of material cannot be negative.

[0033] (2)

[0034] workshop right The demand for this type of material is in the workshop. middle Number of tasks and The product of the material requirements for each type of task.

[0035] ③ Internal warehouse model construction: Internal warehouse The capacity is The materials in the internal warehouse are used to supply the production lines in the corresponding workshops. In-time warehouse In The quantity of this type of material is , In-time warehouse In The consumption of this type of material is , In-time warehouse The material replenishment amount is , In-time warehouse The remaining amount of material in the container is .

[0036] (3)

[0037] That is, the amount of material in the warehouse at the current moment is the sum of the amount of material in the warehouse at the previous moment and the net increase in the amount of material in the warehouse at this moment. The amount of material in the warehouse cannot be negative.

[0038] ④ External Warehouse Model Construction: Raw materials from various locations are periodically transported to external warehouses as material storage points. Materials in the external warehouses are used to replenish materials in the internal warehouses. Since the types of materials in the external and internal warehouses may differ, sometimes multiple external warehouses need to coordinate to send materials to the internal warehouses for replenishment. External Warehouse The capacity is , Time Warehouse In The quantity of this type of material is , Time Warehouse In The consumption of this type of material is , Time Warehouse The material replenishment amount is , Time Warehouse The remaining amount of material in the container is .

[0039] (4)

[0040] That is, the amount of material in the outer warehouse at the current moment is the sum of the amount of material in the outer warehouse at the previous moment and the net increase in the amount of material in the outer warehouse at this moment. The amount of material in the outer warehouse cannot be negative.

[0041] ⑤ Threshold parameter: The threshold for the production line to call materials into the inner warehouse at any given time is That is, when the workshop middle Material quantity Less than or equal to When the material is replenished, a material call signal is sent to the inner warehouse. The threshold for replenishing materials from the inner warehouse to the outer warehouse at any given time is That is, when the inner warehouse middle Material quantity Less than or equal to When the material replenishment signal is sent to the outer warehouse, the outer warehouse will replenish the material.

[0042] Step 2 is implemented in more detail as follows:

[0043] Step 2.1. Constructing Transportation Coefficients. Construct the state variables to determine whether there is a correlation between workshops, internal warehouses, and external warehouses in multi-warehouse collaboration. This refers to the material requisition status in the workshop and internal warehouse. Time Workshop If to the inner warehouse Order materials, then ,on the contrary ; This refers to the quantity of seasonings in the inner and outer warehouses. In-time warehouse If going to the outer warehouse Seasonings, ,on the contrary . This refers to the material loading status in the workshop. Time Workshop If materials are replenished, then ,on the contrary ; This refers to the replenishment status of materials between the inner and outer warehouses. If the time is inside the warehouse There are from external warehouses For material replenishment, ,on the contrary The distance parameters between workshops, inner warehouses, and outer warehouses in multi-warehouse collaboration are constructed, and then the multi-warehouse transportation coefficient is constructed. The closer the distance between warehouses, the larger the transportation coefficient and the lower the transportation cost. For the workshop With the inner warehouse The distance between them is such that, since there is a one-to-one correspondence between the workshop and the inner warehouse, and the workshop only receives materials from its corresponding inner warehouse, therefore:

[0044] (5)

[0045] For the inner warehouse With external warehouse The distance between the inner and outer warehouses is a many-to-many relationship, with each inner warehouse having a corresponding distance from each outer warehouse, and none of these distances are zero.

[0046] The speed of transporting materials is The material transportation time between the workshop and the inner warehouse is The material transportation time between the inner and outer warehouses is The formulas for calculating transportation time are shown in equations (6) and (7):

[0047] (6)

[0048] (7)

[0049] Inner warehouse With external warehouse The transport coefficient between them is The formula for calculating the transport coefficient is as follows:

[0050] (8)

[0051] in, This is the attenuation factor.

[0052] Step 2.2. Construction of Multi-Warehouse Correlation Coefficients. Based on the material type relationships between internal and external warehouses, and between external warehouses, multi-warehouse correlation coefficients can be constructed. These coefficients represent the degree of consistency or complementarity of transported materials. For collaboration between internal and external warehouses, the more similar the material types, the greater the synergistic advantage, and the fewer times the required material types can be replenished. For collaboration between external warehouses, the more complementary the material types, the greater the synergistic advantage; multiple complementary external warehouses supplying materials to a single internal warehouse better meets the material demand.

[0053] Construct material type status variables. Workshop Is it necessary? State quantities of similar materials, such as those in the workshop need For this type of material, ,on the contrary, ; Indicates internal warehouse Store State variables of similar materials, such as those in the inner warehouse. storage For this type of material, ,on the contrary, ; Indicates external warehouse Store State variables of similar materials, if external warehouse storage For this type of material, ,on the contrary, Construct the correlation coefficient between internal and external warehouses, and the correlation coefficient between external warehouses. , Indicates internal warehouse With external warehouse Synergistic Relationship , Indicates external warehouse With external warehouse The synergistic relationship between them and The calculation formulas are shown in equations (9) and (10):

[0054] (9)

[0055] (10)

[0056] in, , The weighting coefficient is approximately 1 when the inner and outer warehouses coordinate, in which case the similarity of materials plays a dominant role; when the outer warehouses coordinate, the weighting coefficient is approximately 0, in which case the complementarity of materials plays a dominant role.

[0057] Step 2.3. Transportation-Related Coordination Coefficient. Based on the transportation coefficient and the internal-external warehouse correlation coefficient, a coordination coefficient between the internal and external warehouses can be constructed. When replenishing the internal warehouse, the replenishment warehouse can be selected based on the coordination coefficient and the material quantity in the external warehouse. With external warehouse The synergy coefficient between them is , indicating external warehouse For the inner warehouse The suitability of the supplementary materials, The larger the warehouse, the better. For the inner warehouse The lower the cost of replenishing materials, The calculation formula is shown in equation (11):

[0058] (11)

[0059] Step 3 is implemented in more detail as follows:

[0060] Step 3.1. Multi-warehouse collaborative seasoning threshold optimization algorithm based on reinforcement learning. By combining reinforcement learning algorithms with the multi-warehouse collaborative seasoning scenario, multi-warehouse collaborative threshold optimization is achieved. In multi-warehouse collaborative optimization, the workshop, inner warehouse, and outer warehouse are considered as the environment. Reinforcement learning actions act on the environment, thereby changing the state of the workshop, inner warehouse, and outer warehouse, and thus providing an evaluation of the action and the state at the next moment. The state space includes information such as warehouse inventory level, order quantity, collaborative demand, collaborative cost, material requisition threshold, and replenishment threshold. The reinforcement learning agent, based on the state... Actions are generated through policy networks and value networks. This involves adjusting the material call threshold and the material replenishment threshold. The adjustment of the material call threshold is as follows: The adjustment of the seasoning threshold is as follows: , , Positive values ​​indicate increments, and negative values ​​indicate decrements; after an action is applied to the environment, the environment state is updated. At the same time, rewards are given to the agent based on a reward function composed of inventory costs, transportation costs, production efficiency, etc. The reward function includes a task completion reward. Inventory holding costs Transportation costs And penalties for margin calls / production shutdowns and The reward function is calculated as shown in Equation (12). The strategy is improved by means of dynamic penalty terms based on adaptive KL divergence to optimize the advantage function. This process is repeated to achieve a close combination of multi-warehouse collaborative optimization and reinforcement learning algorithm, thereby continuously improving system performance.

[0061] (12)

[0062] Step 3.2. Construction of Evaluation Indicators for Multi-Warehouse Collaboration Threshold Optimization. Indicators such as maximum task completion time, internal warehouse turnover rate, external warehouse turnover rate, and transportation cost are constructed to evaluate the performance of the optimized multi-warehouse collaboration threshold. Maximum task completion time is the completion time of the last completed task within the specified time period. Internal warehouse turnover rate is the ratio of the change in internal warehouse materials to the internal warehouse capacity per unit time. External warehouse turnover rate is the ratio of the change in external warehouse materials to the external warehouse capacity per unit time. Transportation cost refers to the transportation costs incurred during the transportation process, including manpower and vehicle costs.

[0063] Step 4 is implemented in more detail as follows:

[0064] Multi-stage Production Demand Analysis. The production of 3C products is characterized by diverse varieties, small batches, and high-frequency production changes. The production process typically includes multiple stages such as SMT placement, component insertion, assembly, testing, and packaging. Each stage differs significantly in material types and process requirements, and the process sequence is strictly defined. Warehouse material delivery requires highly accurate material loading based on production rhythm and stage material demands. Therefore, clarifying the relationship between the production characteristics and material demands of each stage provides a basis for constructing a two-layer optimization model for production logistics collaboration and a production logistics collaborative scheduling optimization method. A multi-stage production process demand analysis is conducted, analyzing the types and quantities of materials in the warehouse, the types and quantities of materials required by each stage, the material demand periods, and the material delivery methods. This provides a basis for subsequent production logistics collaboration. The 3C workshop production process typically includes core stages such as SMT placement, component insertion, assembly, testing, and packaging. Each stage has the following characteristics:

[0065] ①SMT Placement: High precision requirements, large equipment investment, many types of materials (such as resistors, capacitors, IC chips), extremely high requirements for timely material supply, and rapid switching of material trays is required when changing production.

[0066] ② Component assembly section: mainly manual / semi-automatic operation, with large materials (such as connectors and transformers), requiring batch feeding according to the process sequence, making production line balancing difficult.

[0067] ③ Assembly section: Multi-variety mixed production, complex component matching (such as shell, cable, motherboard), requires precise control of material availability to avoid downtime due to material shortage.

[0068] ④ Testing section: The equipment is highly specialized, the testing procedures vary with the product model, and it is necessary to monitor material consumption (such as test fixtures and consumables) in real time, with a high data acquisition frequency.

[0069] ⑤ Packaging Section: Orders are highly customized, with a wide variety of auxiliary materials (such as instruction manuals, packaging boxes, and damage prevention materials). Quick response to order changes is required, and packaging line switching costs are low but frequent.

[0070] Because the material demand between work sections is dynamic, interconnected, and time-sensitive, production and logistics scheduling are relatively isolated, leading to coordination failures such as "production lines waiting for materials" or "material backlog." At the same time, because the distribution strategy is not matched to the characteristics of the work sections, the first-in-first-out task execution strategy is adopted, resulting in low logistics efficiency.

[0071] Due to the rapid product updates of 3C products (e.g., the average lifespan of a mobile phone model is 6-12 months), the types and quantities of materials in each production segment change frequently with orders, making production task scheduling and planning difficult. Furthermore, the material requirements between production segments are strongly correlated; for example, connectors for the component insertion segment are only needed after SMT placement is completed. Therefore, a material-production segment correlation matrix needs to be established to describe the relationships between materials and production segments in detail.

[0072] A two-layer optimization model for production and logistics collaboration is constructed to enhance the correlation between production and logistics and improve collaborative efficiency. The architecture of this model comprises a production scheduling optimization layer, a collaboration layer, and a logistics distribution optimization layer. The production scheduling optimization layer corresponds to a production scheduling optimization model, the logistics distribution optimization layer corresponds to a logistics distribution optimization model, and the collaboration layer corresponds to the production-logistics collaborative coupling mechanism. The production scheduling optimization layer includes a task model and a work segment model. The task model includes elements such as task requirements, task time, and task status, while the work segment model includes information such as the number of work segments, work segment types, and work segment capacity. The production scheduling optimization layer generates production plans and receives constraint feedback from the collaboration layer to optimize task arrangement. The collaboration layer is the hub connecting the production scheduling optimization layer and the logistics distribution optimization layer. Based on collaboration assumptions and constraints, it coordinates tasks, work segments, logistics, and materials. The collaboration layer defines collaboration goals, such as production waiting time, task efficiency, material waiting time, and completion time, and transmits status updates to other layers while providing logistics solutions to the logistics distribution optimization layer. The logistics distribution optimization layer consists of a logistics model and a material model. The logistics model involves logistics carriers, delivery routes, and logistics status, while the material model includes material quantity, type, and update status, responsible for optimizing material distribution. The production-logistics collaborative two-tier optimization model achieves deep collaboration between production and logistics through layered cooperation, improving overall production efficiency and effectiveness.

[0073] (1) The production scheduling optimization model includes parameters related to the selection of multiple production sections and task execution. The total number of production tasks is... The number of production task types is Task set ,in Indicates the first There are [number] production tasks. The number of production section types is [number]. The work sections are set as follows ,in Indicates the first Work section. Task The corresponding processing section is ,in This is a state variable, representing the task. Do we need a work section? Processing, if Then the task No work section required Processing, or conversely, task Work section required Processing. The set of process segments is as follows: ,in In the workshop Number of work sections, total number of work sections The calculation formula is shown in equation (13):

[0074] (13)

[0075] Further construct the time status quantities related to production tasks, production tasks The arrival time is Production tasks The deadline is Production tasks At the work section The start time is This is the decision variable, the production task. At the work section The end time is Section The processing time is The relationship between production tasks and work section task execution time is shown in equations (14) and (15).

[0076] (14)

[0077] (15)

[0078] in, For production tasks Total processing time.

[0079] Based on the association between work sections and production tasks, the association between work sections and material requirements is constructed. Material quantity required for one processing , For the number of material types, Indicator Section One processing requires the first Quantity of each type of material, number of each type of material in the internal warehouse ,in The first in the inner warehouse Quantity of materials, work section The upper limit of production capacity is That is, work section The maximum number of workpieces that can be processed at the same time.

[0080] (2) In the logistics and distribution optimization model, AGVs transport materials from the inner warehouse to specific sections of the production line. Currently, the material distribution method is mostly timed delivery, without real-time delivery according to the production line's production rhythm, which can easily lead to material accumulation or production line shutdown due to material shortage, thus reducing production efficiency. Therefore, a logistics and distribution optimization model is constructed to calculate material demand in real time based on the production status and then optimize the distribution. for In the warehouse at any time Quantity of different types of materials, from warehouse to work section The distance is The AGV cart's transport speed is constant. Then from the inner warehouse to the work section Delivery time The calculation formula is shown in equation (16):

[0081] (16)

[0082] (3) In the production logistics collaborative coupling mechanism, construct the production logistics collaborative state quantity. express Production tasks at all times Is it in the work section? Upstream processing. Based on the production scheduling optimization model and the logistics distribution optimization model, the following production logistics coordination assumptions are set:

[0083] In the current scenario of joint scheduling of production logistics, only the multi-section production of a single workshop and the material distribution of the corresponding internal warehouse of a single workshop are considered; the transportation distance between sections is not considered, that is, it is assumed that the workpiece can immediately enter the next section for processing after it is processed in one section.

[0084] Assuming there are enough AGVs to transport materials to specific work sections, and without considering any AGV quantity limitations;

[0085] The materials in the inner warehouse are replenished regularly and will not exceed the maximum capacity of the inner warehouse;

[0086] The AGV transports materials at a constant speed.

[0087] Based on the above assumptions, the constraints of the production logistics collaboration scenario are extracted, and the constraint formulas are shown in equations (17)-(21):

[0088] (17)

[0089] (18)

[0090] (19)

[0091] (20)

[0092] (twenty one)

[0093] Among them, Equation (17) is the internal warehouse material inventory constraint, the material demand of the work section at the same time cannot exceed the internal warehouse material inventory; Equation (18) is the work section sequence constraint, the start time of the task in the previous work section cannot be later than the start time of the next work section; Equation (19) is the work section capacity constraint, the processing task of the work section at the same time cannot exceed the maximum capacity of the work section; Equation (20) is the process sequence constraint, the processing time of the same task in the previous period cannot be later than the processing time of the next work section, that is, it must be processed in the previous work section before it can enter the next work section for processing; Equation (21) is the work section quantity constraint, the number of work sections occupied at the same time cannot exceed the total number of such work sections.

[0094] The ultimate goal of production logistics collaboration is to improve the compatibility between the production process and the logistics transportation process, thereby increasing the production efficiency of tasks and the efficiency of logistics distribution. Therefore, the optimization objective of the production logistics collaboration bi-level optimization model is the total waiting time. Minimum and maximum production time Minimum and total logistics waiting time Minimum, and task efficiency Maximum. The total waiting time is the sum of the start time and arrival time of all production tasks. The maximum production time is the time when all production tasks are completed. The total logistics waiting time is the sum of the start time and arrival time of all logistics tasks. The task benefit is the sum of the benefits of each task (which can be regarded as having a unit of benefit for completing a task).

[0095] Step 5 is implemented in more detail as follows:

[0096] 5.1. Production Logistics Collaborative Scheduling Based on Multi-Objective Optimization Algorithms. The production scheduling optimization model aims to minimize the maximum completion time, minimize production waiting time, and maximize task revenue, optimizing the allocation and sequence of tasks across different work sections. Each work section prioritizes high-priority tasks to avoid significant losses caused by delays in urgent tasks. Dual coding of processes and machines ensures task sequence constraints and equipment exclusivity constraints. The logistics distribution optimization model aims to minimize material waiting time, dynamically adjusting the coupling relationship between material delivery time and task start time. Based on AGV transport speed and distance, the delivery time of materials from the inner warehouse to the work section is calculated to ensure timely material supply to tasks. Constraint conflicts are handled through repair operations; for example, if the material replenishment time is later than the task start time, it is forcibly brought forward and the time of subsequent tasks is adjusted. Production and logistics status are transmitted through the collaboration layer; for example, the work section load is fed back to the logistics model in real time to adjust delivery priorities; the logistics inventory status affects production task scheduling, avoiding work section idleness due to material shortages. Production status is fed back to the multi-warehouse collaborative threshold optimization module, thereby achieving real-time threshold optimization.

[0097] 5.2. Construction of Evaluation Indicators for Production Logistics Collaborative Optimization. Evaluation indicators such as material waiting time, production waiting time, maximum completion time, and production efficiency are constructed. Material waiting time is the time from when a production task is completed until all materials are available; production waiting time is the time from when a task is completed until it begins execution; maximum completion time is the time when the last production task is completed; and production efficiency is the profit obtained from production. These indicators evaluate production benefits from both the production and material perspectives.

[0098] In summary, this invention discloses a multi-warehouse collaborative dynamic optimization method adapted to multi-variety, variable-batch mixed-line production. This invention comprises five steps: multi-warehouse scenario analysis and problem modeling, multi-warehouse material state correlation analysis, dynamic optimization of multi-warehouse collaborative material adjustment thresholds, production logistics collaboration mechanism, and production logistics collaborative scheduling optimization. This invention can analyze multi-warehouse collaborative scenarios, construct multi-warehouse collaborative material correlations, and then achieve multi-warehouse collaborative dynamic material threshold optimization. Based on the threshold optimization of multi-warehouse collaboration, a production logistics collaboration mechanism is further constructed, and combined with a multi-objective optimization algorithm, production logistics collaborative scheduling optimization is achieved.

[0099] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0100] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-warehouse collaborative dynamic optimization method adapted to multi-variety, variable-batch mixed-line production, characterized in that, Includes the following steps: Step 1. Abstract the workshop, inner warehouse, and outer warehouse into scenario models. In the workshop model, set the workshop capacity, task types and quantities, arrival and deadline times, and the demand for various materials for each task, ensuring that the remaining material quantity is non-negative, and establish a mapping relationship between task quantity and material demand. In the inner warehouse model, set the capacity, quantity of various materials, consumption and replenishment, and keep the material quantity non-negative. In the outer warehouse model, set the capacity, quantity of various materials, consumption and replenishment, and keep the material quantity non-negative. Set the material calling threshold and material adjustment threshold in real time to trigger the replenishment chain of the workshop, inner warehouse, and outer warehouse. Step 2. Multi-warehouse material status correlation analysis: Construct transportation status quantities for workshop, inner warehouse, and outer warehouse, and calculate transportation coefficients based on distance and transportation speed; calculate the correlation coefficients between inner and outer warehouses and between outer warehouses based on the storage status of material types in each entity; integrate the transportation coefficients and correlation coefficients to calculate the transportation correlation coordination coefficient. Step 3. Dynamic optimization of multi-warehouse collaborative seasoning threshold: Construct an environment including workshop, internal warehouse and external warehouse. Based on the evaluation of task requirement status, warehouse material status and multi-warehouse collaborative status, configure the objective function, constraints and optimization objectives of the optimization problem, and solve for the optimal seasoning threshold for multi-warehouse collaboration. Step 4. Production logistics collaboration mechanism: Construct a two-layer optimization model for production logistics collaboration. In this model, the upper production scheduling optimization layer integrates task and work section models to generate production plans and receives constraint feedback from the collaboration layer. The collaboration layer coordinates tasks, work sections, logistics, and materials, clarifies the collaboration goals of production waiting time and task benefits, and transmits status updates. The lower logistics distribution optimization layer optimizes distribution through logistics and material models, while setting constraints on internal warehouse inventory and work section sequence. The optimization goal is to minimize total waiting time and maximize task benefits, thereby achieving deep collaboration between production and logistics. Step 5. Production logistics collaborative scheduling optimization: Combining multi-objective optimization algorithms and production logistics collaborative mechanisms, the upper production scheduling optimization layer aims to minimize the maximum completion time, production waiting time and maximize task benefits, while the lower logistics distribution optimization layer aims to minimize material waiting time. The collaborative layer enables two-way feedback between production and logistics status to optimize distribution priority and production scheduling. Step 2 specifically includes: Step 2.

1. Constructing Transportation Coefficients; Constructing the state variables to determine whether there is a correlation between workshops, internal warehouses, and external warehouses in multi-warehouse collaboration. This refers to the material requisition status in the workshop and internal warehouse. Time Workshop If to the inner warehouse Order materials, then ,on the contrary ; This refers to the quantity of seasonings in the inner and outer warehouses. In-time warehouse If to the outer warehouse Seasonings, ,on the contrary , This refers to the material loading status in the workshop. Time Workshop If materials are replenished, then ,on the contrary ; This refers to the replenishment status of materials between the inner and outer warehouses. If the time is inside the warehouse There are from external warehouses For material replenishment, ,on the contrary Construct distance parameters between workshop, inner warehouse, and outer warehouse in multi-warehouse collaboration, and then construct multi-warehouse transportation coefficients. For the workshop With the inner warehouse The distance between workshops and inner warehouses corresponds one-to-one. Each workshop only receives materials from its corresponding inner warehouse. Therefore: （5） For the inner warehouse With external warehouse The distance between the inner and outer warehouses is a many-to-many relationship, with each inner warehouse having a corresponding distance to each outer warehouse, and none of these distances are zero. The speed of transporting materials is The material transportation time between the workshop and the inner warehouse is The material transportation time between the inner and outer warehouses is The formulas for calculating transportation time are shown in equations (6) and (7): （6） （7） Inner warehouse With external warehouse The transport coefficient between them is The formula for calculating the transport coefficient is as follows: （8） in, It is the attenuation factor; Step 2.

2. Constructing multi-warehouse correlation coefficients; constructing material type status variables. Workshop Is it necessary? State quantities of similar materials, such as those in the workshop need For this type of material, ,on the contrary, ; Indicates internal warehouse Store State variables of similar materials, such as those in the inner warehouse. storage For this type of material, ,on the contrary, ; Indicates external warehouse Store State variables of similar materials, if external warehouse storage For this type of material, ,on the contrary, Construct the correlation coefficient between internal and external warehouses and the correlation coefficient between external warehouses. , Indicates internal warehouse With external warehouse Synergistic Relationship , Indicates external warehouse With external warehouse The synergistic relationship between them and The calculation formulas are shown in equations (9) and (10): （9） （10） in, , These are the weighting coefficients; Step 2.

3. Inner Warehouse With external warehouse The synergy coefficient between them is , indicating external warehouse For the inner warehouse The suitability of the supplementary materials, The larger the warehouse, the better. For the inner warehouse The lower the cost of replenishing materials, The calculation formula is shown in equation (11): （11）。 2. The multi-warehouse collaborative dynamic optimization method for adapting to multi-variety, variable-batch mixed-line production as described in claim 1, characterized in that, Step 1 specifically includes: ① The scenario model includes: The multi-warehouse collaborative scenario consists of three parts: workshops, internal warehouses, and external warehouses. The number of workshops is... The number of internal warehouses is The number of external warehouses is The workshops are grouped as ,in Indicates the first One workshop; the inner warehouse is assembled as follows: ,in Indicates the first Each internal warehouse; the internal warehouse set is as follows: ,in Indicates the first There are one external warehouse, and the materials required for production total... kind; ② Workshop Model Construction: The number of required tasks in the workshop is The required tasks are as follows: Seeds, workshop The capacity is , Time Workshop middle The number of tasks is ,in For the workshop middle Number of tasks, task set is ,in Indicates the first indivual Class of tasks, For the first indivual The arrival time of the task. For the first indivual Deadlines for different task types; corresponding deadlines for each type of task. The material requirements are ,in express Class of requirements task for the first The demand for this type of material is such that there is a certain amount of material available in the workshop for production. Time Workshop In The quantity of this type of material is , Time Workshop In The consumption of this type of material is , Time Workshop The material replenishment amount is , Time Workshop The remaining amount of material in the container is , Time Workshop right The required quantity of this material is : （1） That is, the remaining amount of material at the current moment is the sum of the remaining amount of material at the previous moment and the remaining amount of material after utilization at this moment; the remaining amount of material cannot be negative. （2） workshop right The demand for this type of material is in the workshop. middle Number of tasks and The product of the material requirements for each type of task; ③ Internal warehouse model construction: Internal warehouse The capacity is The materials in the internal warehouse are used to supply the production lines in the corresponding workshops. In-time warehouse In The quantity of this type of material is , In-time warehouse In The consumption of this type of material is , In-time warehouse The material replenishment amount is , In-time warehouse The remaining amount of material in the container is : （3） That is, the amount of material in the warehouse at the current moment is the sum of the amount of material in the warehouse at the previous moment and the net increase in the amount of material in the warehouse at this moment. The amount of material in the warehouse cannot be negative. ④ External warehouse model construction: External warehouse The capacity is , Time Warehouse In The quantity of this type of material is , Time Warehouse In The consumption of this type of material is , Time Warehouse The material replenishment amount is , Time Warehouse The remaining amount of material in the container is : （4） That is, the amount of material in the outer warehouse at the current moment is the sum of the amount of material in the outer warehouse at the previous moment and the net increase of material in the outer warehouse at this moment; the amount of material in the outer warehouse cannot be negative. ⑤ Threshold parameter: The threshold for the production line to call materials into the inner warehouse at any given time is That is, when the workshop middle Material quantity Less than or equal to When the material is replenished, a material call signal is sent to the inner warehouse. The threshold for replenishing materials from the inner warehouse to the outer warehouse at any given time is That is, when the inner warehouse middle Material quantity Less than or equal to When the material replenishment signal is sent to the outer warehouse, the outer warehouse will replenish the material.

3. The multi-warehouse collaborative dynamic optimization method for adapting to multi-variety, variable-batch mixed-line production as described in claim 2, characterized in that, Step 3 specifically includes: Step 3.

1. In multi-warehouse collaborative optimization, the workshop, internal warehouse, and external warehouse are considered as the environment. Actions affect the environment, thereby changing the state of the workshop, internal warehouse, and external warehouse, and thus providing an evaluation of the action and the state at the next moment. The state space includes warehouse inventory level, order quantity, collaborative requirements, collaborative costs, material requisition threshold, and material replenishment threshold information, based on the state. Generate Actions This involves adjusting the material call threshold and the material replenishment threshold. The adjustment of the material call threshold is as follows: The adjustment of the seasoning threshold is as follows: , , Positive values ​​represent increments, and negative values ​​represent decrements; after an action is applied to the environment, the environment state is updated. The reward function that is formed at the same time gives a reward. The reward function includes a task completion reward. Inventory holding costs Transportation costs And penalties for margin calls / production shutdowns and The formula for calculating the reward function is shown in equation (12): （12） Step 3.

2. Construction of evaluation indicators for multi-warehouse collaboration threshold optimization; construct indicators such as maximum task completion time, internal warehouse turnover rate, external warehouse turnover rate, and transportation cost to evaluate the merits of the results after multi-warehouse collaboration threshold optimization, and solve for the optimal seasoning threshold for multi-warehouse collaboration.

4. The multi-warehouse collaborative dynamic optimization method for adapting to multi-variety, variable-batch mixed-line production as described in claim 3, characterized in that, Step 4 specifically includes: The production logistics collaboration two-layer optimization model is constructed. The architecture of the production logistics collaboration two-layer optimization model includes a production scheduling optimization layer, a collaboration layer and a logistics distribution optimization layer. The production scheduling optimization layer corresponds to the production scheduling optimization model, the logistics distribution optimization layer corresponds to the logistics distribution optimization model, and the collaboration layer corresponds to the production logistics collaboration coupling mechanism. The production scheduling optimization model includes parameters related to the selection of multiple production stages and task execution. The total number of production tasks is... The number of production task types is Task set ,in Indicates the first There are [number] production tasks, and the number of production sections is [number]. The work sections are set as follows ,in Indicates the first Work section, task The corresponding processing section is ,in This is a state variable, representing the task. Do we need a work section? Processing, if Then the task No work section required Processing, or conversely, task Work section required The set of processing sections is as follows ,in In the workshop Number of work sections, total number of work sections The calculation formula is shown in equation (13): （13） Further construct the time status quantities related to production tasks, production tasks The arrival time is Production tasks The deadline is Production tasks At the work section The start time is This is the decision variable, the production task. At the work section The end time is Section The processing time is The relationship between the production task and the section task execution time is shown in equations (14) and (15): （14） （15） in, For production tasks Total processing time; Based on the association between work sections and production tasks, the association between work sections and material requirements is constructed. Material quantity required for one processing , For the number of material types, Indicator Section One processing requires the first Quantity of each type of material, number of each type of material in the internal warehouse ,in The first in the inner warehouse Quantity of materials, work section The upper limit of production capacity is That is, work section The maximum number of workpieces that can be processed at the same time; In the logistics and distribution optimization model, for In the warehouse at any time Quantity of different types of materials, from warehouse to work section The distance is The AGV cart's transport speed is constant. Then from the inner warehouse to the work section Delivery time The calculation formula is shown in equation (16): （16） In the production logistics collaborative coupling mechanism, production logistics collaborative state variables are constructed. express Production tasks at all times Is it in the work section? For the upper processing, based on the production scheduling optimization model and the logistics distribution optimization model, the constraints of the production logistics collaboration scenario are extracted, and the constraint formulas are shown in equations (17)-(21): （17） （18） （19） （20） （21） Among them, Equation (17) is the internal warehouse material inventory constraint, the material demand of the work section at the same time cannot exceed the internal warehouse material inventory; Equation (18) is the work section sequence constraint, the start time of the task in the previous work section cannot be later than the start time of the next work section; Equation (19) is the work section capacity constraint, the processing task of the work section at the same time cannot exceed the maximum capacity of the work section; Equation (20) is the process sequence constraint, the processing time of the same task in the previous period cannot be later than the processing time of the next work section, that is, it must be processed in the previous work section before it can enter the next work section for processing; Equation (21) is the work section quantity constraint, the number of work sections occupied at the same time cannot exceed the total number of such work sections.

5. The multi-warehouse collaborative dynamic optimization method for adapting to multi-variety, variable-batch mixed-line production as described in claim 4, characterized in that, The optimization objectives of the production logistics collaborative two-layer optimization model are to minimize the total waiting time, minimize the maximum production time, minimize the total logistics waiting time, and maximize the task benefits.

6. The multi-warehouse collaborative dynamic optimization method for adapting to multi-variety, variable-batch mixed-line production as described in claim 3, characterized in that, The maximum task completion time is the completion time of the last task completed within that time period. The internal warehouse turnover rate is the ratio of the change in internal warehouse materials to the internal warehouse capacity per unit time. The external warehouse turnover rate is the ratio of the change in external warehouse materials to the external warehouse capacity per unit time. The transportation cost is the cost of manpower and vehicles consumed during the transportation process.

7. The multi-warehouse collaborative dynamic optimization method for adapting to multi-variety, variable-batch mixed-line production as described in claim 2, characterized in that, The number of workshops is equal to the number of internal warehouses, that is... .

8. The multi-warehouse collaborative dynamic optimization method for adapting to multi-variety, variable-batch mixed-line production as described in claim 5, characterized in that, The total waiting time is the sum of the start time and arrival time of all production tasks. The maximum production time is the time when all production tasks are completed. The total logistics waiting time is the sum of the start time and arrival time of all logistics tasks. The task benefits are the sum of the revenue from each task.

9. The multi-warehouse collaborative dynamic optimization method for adapting to multi-variety, variable-batch mixed-line production as described in claim 5, characterized in that, Extract constraints from the production logistics collaboration scenario, and construct the scenario based on the following assumptions: Once a workpiece is finished in one section, it can immediately proceed to the next section for processing. There are enough AGV carts to transport materials to specific work sections, and the number of AGV carts is not a concern. The materials in the inner warehouse are replenished regularly and will not exceed the maximum capacity of the inner warehouse; The AGV transports materials at a constant speed.

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