A dynamic programming-based lubricating oil filling scheduling method, system, device and storage medium

Abstract

The application discloses a kind of based on dynamic programming's lubricating oil filling production scheduling method, system, equipment and storage medium, it is related to production production scheduling optimization technical field, including response production scheduling request, obtain packaging oil order set and production line constraint parameter;According to material specification adaptation attribute for each order, filter matching production line and calculate available remaining duration, the production line that meets demand is included in candidate set to realize solution space pruning;Under the constraint of candidate set, with the goal of minimizing global production cost, the state transition cost under different production line distribution is calculated using dynamic programming, the cost;The method disclosed in the application optimizes the allocation of production line tasks by introducing dynamic programming algorithm and multi-dimensional weighted calculation, reduces the waste caused by top line cleaning and grade reduction, improves production efficiency and resource utilization, and ensures efficient scheduling and accurate execution of production tasks.

Description

Technical Field

[0001] This invention relates to the field of production scheduling optimization technology, specifically to a method, system, equipment, and storage medium for lubricating oil filling scheduling based on dynamic programming. Background Technology

[0002] Production scheduling for lubricant filling faces several challenges, especially when multiple production lines are working together. Traditional scheduling methods often struggle to effectively address the complexity of production scheduling. Filling lines need to connect to multiple unloading pumps in upstream processes to perform filling operations. This multi-point connection mode increases the difficulty of production planning. Furthermore, the capacity and material compatibility of different production lines are not entirely consistent, leading to unnecessary delays and resource waste during task allocation. To ensure smooth production, production scheduling must consider multiple factors, including material compatibility of production lines, order priority, production line load, and equipment status. However, existing methods often fail to take into account these complex factors, resulting in low production efficiency.

[0003] In lubricant filling production, the operating time and resource utilization efficiency of the filling line also face serious problems. When filling different specifications of oil, the production line usually needs to perform a cleaning operation, a process known as top-line cleaning. Each top-line cleaning consumes a large amount of lubricant and causes production line downtime, affecting overall production efficiency. Switching between different specifications of oil also leads to downgrading issues; that is, when switching from a high-specification product to a low-specification product, additional cleaning and adjustments are required, increasing waste in the production process. Therefore, in traditional production scheduling, top-line cleaning and downgrading switching occur frequently, resulting in unreasonable production planning and low production efficiency.

[0004] In addition, filling line operations are often limited by the coordination between the number of shifts and working hours. Since the number of production lines is usually far greater than the number of available shifts, the impact of the number of shifts on production scheduling during fixed working hours cannot be ignored. Traditional scheduling methods usually rely on manual judgment and experience, and have not fully considered the matching between production lines and shifts and the workload problem, resulting in greater uncertainty in the scheduling process, which further affects the coordination of production and the utilization rate of resources.

[0005] In current production scheduling systems, many methods lack flexibility and cannot be adjusted in real time according to changes that occur during the production process. Traditional scheduling methods rely too much on manual operation and pre-set fixed rules, making it difficult to cope with the frequent changes in actual production situations, such as equipment failures and changes in order demand. The shortcomings of existing technologies limit the accuracy and adaptability of production scheduling in complex production environments, resulting in low coordination efficiency between production lines, serious waste of resources, and production delays, which prevent production efficiency from reaching its optimal state. Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is that existing lubricating oil filling production scheduling methods suffer from problems such as complex production line scheduling, serious resource waste, frequent top-line cleaning and downgrading leading to low production efficiency, and inability to flexibly adapt to changes in demand during the production process. By introducing a production scheduling algorithm based on dynamic programming and group trimming, the matching between the production line and orders is optimized, production scheduling efficiency is improved, resource waste is reduced, and the production scheduling strategy can be dynamically adjusted according to real-time production demand, thus solving the problems of inflexible production scheduling, low resource utilization, and uncoordinated production process in traditional methods.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a lubricating oil filling scheduling method based on dynamic programming, comprising: responding to a scheduling request, acquiring a set of packaged oil orders to be solved and associated production line constraint parameters; for any order in the packaged oil order set, selecting a matching target production line from the entire set of production lines based on the material specification adaptation attribute, and calculating the available remaining time of the target production line within the order cycle; if the available remaining time meets the order requirements, storing the target production line in the candidate production line subset corresponding to the order to achieve grouping and pruning of the dynamic programming solution space; using minimizing the global production cost as the objective function, within the constraint range of the candidate production line subset, iteratively calculating the state transition cost of each order on different candidate production lines using a dynamic programming algorithm, wherein the state transition cost is at least positively correlated with the top line cleaning time during production line switching; and outputting a filling scheduling scheme including production line identifier, task order, and start and end times based on the calculated optimal path.

[0009] As a preferred embodiment of the dynamic programming-based lubricating oil filling scheduling method of the present invention, the packaged oil order set is generated in the following way: obtaining the original order flow and sorting the packaged oil orders according to a preset sorting rule; the sorting rule includes order type, order priority and order requirement date; based on the sorting, orders with the same material attributes and packaging specifications are dynamically aggregated to generate a single production node, so as to reduce the initial dimension of the dynamic programming algorithm.

[0010] As a preferred embodiment of the dynamic programming-based lubricating oil filling scheduling method of the present invention, the production line constraint parameters are obtained based on the production line model, which includes the adaptation attributes of different production lines to material specifications, available working hours, and equipment operating status; the production line constraint parameters also include concurrent shift quotas; in the dynamic iterative calculation step, the number of production lines in operation at any given time slot is limited by the concurrent shift quota.

[0011] As a preferred embodiment of the dynamic programming-based lubricating oil filling scheduling method of the present invention, the state transition cost is obtained by multi-dimensional weighted calculation, and the multi-dimensional weighting is defined by the scheduling strategy, including: a first weight: the difference in material packaging specifications before and after production line switching, used to characterize the top line cleaning loss; a second weight: the relationship between the material grade before and after production line switching, used to characterize the downgrade production loss; wherein, the first weight is higher than the second weight.

[0012] As a preferred embodiment of the lubricating oil filling scheduling method based on dynamic programming described in this invention, the calculation logic of the first weight is as follows: when the packaging specification of a continuous scheduling task switches from a low specification to a high specification, a first-generation value is allocated; when it switches from a high specification to a low specification, a second-generation value is allocated; wherein, the second-generation value is greater than the first-generation value.

[0013] As a preferred embodiment of the lubricating oil filling scheduling method based on dynamic programming described in this invention, the dynamic programming algorithm includes: , in, This indicates the collection of orders. Assigned to the front The minimum completion time on a production line, during state transition, is determined by changing the set. Selecting a subset of orders This makes the subset The corresponding filling production order was assigned to the first One production line processes the orders, while the remaining orders... From the beginning Processing on one production line, Indicates the preceding One production line completes the remaining orders Minimum required completion time Represents a subset of orders in front Total processing time of the production line Indicates from set The removed order subset is compared. and When the smaller of the two values ​​is used as the overall completion time for the current combination, this is achieved by iterating through all possible combinations. To determine the order set, obtain the minimum overall completion time across the entire combination. in front The optimal allocation scheme on the production line.

[0014] As a preferred embodiment of the dynamic programming-based lubricating oil filling scheduling method described in this invention, the multi-dimensional weighted calculation includes: sorting orders according to their demand dates, prioritizing orders with earlier demand dates in the scheduling sequence; and further sorting orders based on their production cycles for orders with the same demand dates. During scheduling, when different scheduling strategies are applicable simultaneously, scheduling rules are executed according to a preset priority order, which includes satisfying the order demand date, minimizing the production cycle, minimizing the number of top-line runs, and minimizing the number of downgrade runs. When minimizing the number of top-line runs, lower-specification products are prioritized in the scheduling sequence; when minimizing the number of downgrade runs, higher-specification products are prioritized. During scheduling, the task distribution of each production line is monitored in real time based on the production line load information recorded by the production line model, and task allocation is adjusted according to the load level of each production line. When a production line becomes unavailable due to equipment failure or maintenance plans, the scheduling strategy is readjusted according to the above priority order, and unfinished tasks are reassigned to other production lines that meet material compatibility and available working time requirements.

[0015] Another objective of this invention is to provide a dynamic programming-based lubricating oil filling scheduling system, which can solve the problems of complex production line scheduling, serious resource waste, low production efficiency caused by frequent top-line cleaning and downgrading, and lack of flexible and adaptable scheduling strategies in current lubricating oil filling scheduling technologies by sorting and merging packaged oil orders, constructing production line models, and optimizing task allocation.

[0016] As a preferred embodiment of the dynamic programming-based lubricating oil filling scheduling system of the present invention, it includes: an order sorting module, a constraint modeling module, and a scheduling optimization module; the order sorting module is used to obtain the original order flow, sort the packaged oil orders according to order type, order priority, and order requirement date, and dynamically aggregate orders with the same material attributes and packaging specifications to generate a packaged oil order set, which serves as input data for the dynamic programming algorithm; the constraint modeling module is used to analyze the material adaptability, available working time, and equipment operating status of each production line based on the production line model. The system generates production line constraint parameters and constructs resource constraints in the dynamic programming state transition process by combining concurrent shift quotas. Simultaneously, it filters target production lines and constructs a subset of candidate production lines based on the order-production line compatibility relationship, grouping and pruning the solution space. The production scheduling optimization module, within the constraints of the candidate production line subset, aims to minimize the global production cost by using a dynamic programming algorithm to solve the state transition problem for the order set. It calculates the state transition cost based on the multi-dimensional weighted rules defined by the production scheduling strategy and outputs a filling production scheduling plan containing production line identifiers, task order, and start and end times based on the optimal state transition path.

[0017] Another object of the present invention is to provide a lubricating oil filling and scheduling device based on dynamic programming, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the lubricating oil filling and scheduling method based on dynamic programming.

[0018] Another object of the present invention is to provide a dynamic programming-based lubricating oil filling and scheduling storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the dynamic programming-based lubricating oil filling and scheduling method.

[0019] The beneficial effects of this invention are as follows: The lubricating oil filling scheduling method based on dynamic programming provided by this invention optimizes the order processing sequence by sorting and merging packaged oil orders to generate a packaged oil order set, ensuring the orderly arrangement of production tasks; by introducing grouping and trimming, it optimizes the matching between production lines and orders, reduces computational complexity, and improves scheduling accuracy; by combining dynamic programming algorithms and multi-dimensional weighted calculations, it flexibly adjusts the priority of production tasks, realizes the optimal scheduling scheme, and ultimately improves production scheduling, priority adjustment, and production efficiency; this invention achieves better results in order sorting and allocation, production line matching, resource utilization, and scheduling accuracy. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is an overall flowchart of a lubricating oil filling and scheduling method based on dynamic programming, provided in Embodiment 1 of the present invention.

[0022] Figure 2 This is a production scheduling strategy diagram for a lubricating oil filling scheduling method based on dynamic programming, provided in Embodiment 1 of the present invention.

[0023] Figure 3 This is a production scheduling result diagram of a lubricating oil filling scheduling method based on dynamic programming provided in Embodiment 1 of the present invention.

[0024] Figure 4 The Gantt chart shows the scheduling results of a dynamic programming-based lubricating oil filling scheduling method provided in Embodiment 1 of the present invention.

[0025] Figure 5 This is a schematic diagram of the multi-dimensional weighted calculation priority of a lubricating oil filling scheduling method based on dynamic programming provided in Embodiment 1 of the present invention.

[0026] Figure 6 This is an overall schematic diagram of a lubricating oil filling and scheduling system based on dynamic programming, provided in Embodiment 2 of the present invention.

[0027] Figure labels: 100, Packaged oil order set; 101, Production line model; 200, Grouping and trimming; 300, Dynamic programming algorithm; 301, Multi-dimensional weighting; N1, Order requirement date; N2, Shortest production cycle; N3, Minimum number of top-line steps principle; N4, Minimum number of downgrade steps principle; M, Production order; 10, Order sorting module; 11, Sorting rule execution submodule; 12, Product merging and original problem set generation submodule; 20, Constraint modeling module; 21, Capacity and available time modeling submodule; 22, Material adaptability modeling submodule; 30, Production scheduling optimization module; 31, Production line screening and trimming submodule; 32, Dynamic programming optimal production scheduling calculation submodule. Detailed Implementation

[0028] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0029] Example 1, referring to Figures 1-5 As an embodiment of the present invention, a lubricating oil filling scheduling method based on dynamic programming is provided, comprising: S1: In response to the production scheduling request, obtain the set of packaged oil orders 100 to be solved and the associated production line constraint parameters.

[0030] The packaged oil order set 100 is generated in the following way: the original order flow is obtained, and the packaged oil orders are sorted according to the preset sorting rules; the sorting rules include order type, order priority and order demand date; based on the sorting, orders with the same material attributes and packaging specifications are dynamically aggregated to generate a single production node, so as to reduce the initial dimension of the dynamic programming algorithm 300.

[0031] The production line constraint parameters are obtained based on the production line model 101, which includes the adaptation attributes of different production lines to material specifications, available working hours and equipment operating status. The production line constraint parameters also include the concurrent shift limit. In the dynamic iterative solution step, the number of production lines in operation at any time slot is limited by the concurrent shift limit.

[0032] Order sorting can be achieved through database sorting or a rules engine, and order merging can be achieved through group statistics; production line model 101 can be built through the MES system, equipment ledger, or historical production data.

[0033] It should be noted that packaged oil orders within the planned outlook period (a fixed period for production enterprises, such as within the next two weeks) are sorted according to a custom sorting rule. First, orders are divided into forecast orders and inventory orders based on order type. Forecast orders are sorted with priority over inventory orders. All forecast orders are placed before inventory orders. For orders of the same type, they are sorted according to order priority, with higher priority orders placed first. Priority data is usually specified by the customer or assessed based on the urgency of the order. For orders with the same priority, they are sorted according to the order's demand date, with orders with earlier demand dates being processed first.

[0034] After sorting is complete, the order merging step is performed. For the same product, multiple orders for the same product are merged into a single order. The system checks all orders to see if there are any orders for the same product. The merged order will contain the total demand for all the same products.

[0035] The merged order information will form a packaged oil order set of 100. Based on the merged order data, basic information for each order will be generated, including order type, priority, required date, product specifications, and required quantity. The original problem set will be used as the basic data input for subsequent production scheduling to ensure that the subsequent dynamic planning process can accurately handle the production scheduling of each order.

[0036] It should also be noted that by collecting basic information about each production line, assessing the maximum processing capacity of each production line, and determining the number and type of orders that each production line can handle within a specific time period based on the equipment configuration and operating time of the production line, for example, one production line can handle 150 orders per hour, while another can handle 100 orders per hour. The material adaptability of each production line is defined, with one production line suitable for small bottles of lubricating oil and another specifically handling large drums of oil. It is also necessary to select the appropriate production line based on the order specifications.

[0037] Considering the equipment model, equipment status, and maintenance cycle of the production line, the operating status of the production line equipment (such as normal operation, pending repair, and under maintenance) as well as the equipment's regular maintenance cycle and downtime, the configuration of the production line is optimized by combining the material flow path and resource availability of the production line, generating production line model 101, which provides accurate support for subsequent production scheduling and task allocation.

[0038] S2: For any order in the packaged oil order set 100, select the matching target production line from the entire production line set according to the material specification adaptation attribute, and calculate the available remaining time of the target production line within the order cycle; if the available remaining time meets the order requirements, store the target production line in the candidate production line subset corresponding to the order, so as to realize the grouping and pruning of the dynamic programming solution space 200.

[0039] Retrieve all orders from the packaged oil order set 100, and break down each packaged oil order into specific filling production orders. Each filling production order represents an independent production task, including the product type and packaging specifications of each order, the quantity to be produced, and production scheduling based on factors such as the order's required date, priority, and market demand.

[0040] The screening process can be based on rule matching, database query, or a preset adaptation matrix; the production time can be calculated based on the production line's unit capacity and order demand.

[0041] The algorithm iterates through all orders using a grouping and trimming method (200 iterations). Based on the material compatibility parameters recorded in production line model 101, it compares the lubricant specifications, packaging types, and production line material compatibility information for each order to determine which production line can handle the lubricant type and packaging specifications required by the current order. Based on the current idle time window of the production line, it compares the task time of the order with the available working time of the production line to determine which production line has enough available time to cover the task requirements of the current order. It also combines the status information of the production line to determine which production lines are in an available operating state (non-stop, non-maintenance, and no scheduled maintenance). Production lines that do not meet any of the above compatibility conditions are trimmed and removed from the candidate production line set. For the trimmed matching production line set, the task allocation order is further optimized based on order priority, current production line load, and priority adjustment rules set by the scheduling strategy. In the final determined matching production line set, based on task time and production line capacity parameters, a dynamic programming algorithm (300) generates the scheduling results corresponding to feasible combinations, and selects the production line combination scheme that best meets the scheduling strategy constraints.

[0042] Because the grouping and trimming 200 needs to completely match all combinations of orders and production lines during the recursive calculation process, when scheduling order T, the trimming and grouping results in a set of production lines that can fit the current order, list{list⊆K}, where K represents all production lines. In the overall calculation, the grouping and trimming operation is simplified from T combined with K to T combined with list.

[0043] Specifically, for each production line, during the scheduling process, it is first determined whether the preceding task has occupied the working time of the production line. If the preceding task has not been completed, the start time of the new production task must be delayed until the preceding task is completed before scheduling can begin. The working time of the preceding task needs to be recorded in advance. The preceding task information of the current production line is obtained, the completion time of the preceding task is calculated, the available time window of the production line is determined, the time occupied by the preceding task is recorded, and the start time of the current task is adjusted.

[0044] In addition to the working hours of the preliminary tasks, the factory's production calendar and equipment downtime plans also need to be considered. During the production process, equipment maintenance and factory downtime will affect the overall production schedule. The factory's calendar information (such as rest days, shutdown days, etc.) and equipment downtime plans (such as regular maintenance, downtime due to malfunctions, etc.) must be included in the calculation of production time. Obtain the factory's calendar information, collect equipment downtime plan information, determine whether the equipment will be shut down during production, and incorporate the plan information into the calculation of production time.

[0045] It should also be noted that during the production scheduling process, the working time of the current task is calculated and recorded. The working time of each task is determined by its production demand, the processing capacity of the production line, and the required working time. The working time of the current task will be combined with the time of the preceding task, equipment status, and other information during the production scheduling process to calculate the production time required for the current task. Based on the processing capacity of the production line and the workload, the time is estimated, the working time of the current task is recorded, and the available remaining working time of the current task is calculated and output.

[0046] S3: With minimizing the global production cost as the objective function, within the constraints of the candidate production line subset, use dynamic programming algorithm 300 to iteratively calculate the state transition cost of each order on different candidate production lines. The state transition cost is at least positively correlated with the top line cleaning time during production line switching. Based on the calculated optimal path, output a filling production scheduling plan that includes production line identification, task order and start and end time.

[0047] Global production cost refers to the total time consumption and changeover losses of the entire production scheduling plan across all production lines: The total production time required for each production line to complete its assigned orders.

[0048] Additional time due to order switching (top line cleaning time + time affected by product degradation).

[0049] State transition cost refers to the additional time cost introduced by the change in production sequence when an order is transferred from one production state to the next.

[0050] Based on the candidate production line subsets obtained by grouping and cutting 200, the packaged oil order set 100 is recursively decomposed into multiple order subsets and assigned to different production lines for execution, thereby forming multiple production scheduling path combinations.

[0051] During each state transition, the system calculates two types of time: one is the completion time of the assigned order on the preceding production line, and the other is the processing time of the current order on the selected production line. The larger of the two values ​​is taken as the completion time under the current allocation method. At the same time, a state transition cost is introduced for this allocation method to characterize the additional time impact caused by the change in production sequence.

[0052] The state transition cost is calculated using a multi-dimensional weighted 301 method, which is defined by the production scheduling strategy. The multi-dimensional weighted 301 method includes: a first weight: the difference in material packaging specifications before and after the production line switch, used to characterize the top line cleaning loss; and a second weight: the relationship between the material grades before and after the production line switch, used to characterize the downgrade production loss. The first weight is higher than the second weight.

[0053] The calculation logic for the first weight is as follows: when the packaging specification of a continuous production task is switched from a low specification to a high specification, the system recognizes that the switching process requires the execution of a top-line cleaning operation and assigns the first generation value; when switching from a high specification to a low specification, the second generation value is assigned; where the second generation value is greater than the first generation value.

[0054] The first weight represents the topline weight, used to measure the importance of the impact of topline cleaning caused by changes in packaging specifications; the second weight represents the downgrade weight, used to measure the production impact when switching from high specifications to low specifications.

[0055] The first-generation value represents the smaller switching cost when switching from a lower specification to a higher specification; the second-generation value represents the larger switching cost when switching from a higher specification to a lower specification.

[0056] If the remaining time is available to meet the order requirements, the target production line is stored in the candidate production line subset corresponding to the order, so as to achieve grouping and pruning of the dynamic programming solution space.

[0057] First, for each order, calculate the overall completion time of the current production line. Let t1 be the completion time when the current order is scheduled to production line k, and t2 be the completion time when it is scheduled to production line j. By comparing t1 and t2, select the optimal scheduling scheme. After producing a low-specification product, the production line needs to be cleaned before the next high-specification product can be produced. If the scheduled order requires the production of a high-specification product, and there are already low-specification products produced, the time of the top line operation needs to be considered, and the time and lubricant waste caused by the operation need to be calculated. By dynamically adjusting the top line and de-product strategies, the production line usage in the scheduling process is optimized.

[0058] It should be noted that the dynamic programming algorithm 300 includes, , in, This indicates the collection of orders. Assigned to the front The minimum completion time on a production line, during state transition, is determined by changing the set. Selecting a subset of orders This makes the subset The corresponding filling production order was assigned to the first One production line processes the orders, while the remaining orders... From the beginning Processing on one production line, Indicates the preceding One production line completes the remaining orders Minimum required completion time Represents a subset of orders in front Total processing time of the production line Indicates from set The removed order subset is compared. and When the smaller of the two values ​​is used as the overall completion time for the current combination, this is achieved by iterating through all possible combinations. To determine the order set, obtain the minimum overall completion time across the entire combination. in front The optimal allocation scheme on the production line.

[0059] Reference Figure 2 , Figure 5As shown, the multi-dimensional weighted 301 calculation includes: sorting orders according to their demand dates, prioritizing orders with earlier demand dates in the production scheduling sequence; and further sorting orders with the same demand date based on their production cycle. During the scheduling process, when the applicable conditions of different scheduling strategies exist simultaneously, the scheduling rules are executed according to a preset priority order, which includes, in order, satisfying the order demand date N1, orders with the shortest production cycle N2, minimizing the number of top-line runs N3, and minimizing the number of downgrades N4. When executing the principle of minimizing the number of top-line runs, lower-specification products are prioritized to enter the production sequence to reduce the impact of batch cuts. The system triggers the top-line cleaning requirement; when implementing the principle of minimizing the number of downgrades, it prioritizes scheduling high-specification products to reduce the scheduling risk of switching to low-specification production after the preceding high-specification production; during the scheduling process, it monitors the task distribution of each production line in real time based on the production line load information recorded by the production line model, and adjusts the task allocation according to the load level of each production line to keep the tasks evenly distributed among available production lines; when a production line becomes unavailable due to equipment failure or maintenance plan during the production process, the scheduling strategy is readjusted according to the above priority order, and the unfinished tasks are redistributed to other production lines that meet the material adaptability and available working time requirements.

[0060] It should also be noted that, referring to Figures 3-4 As shown, the configuration of production scheduling parameters is provided, including the earliest scheduling date for the order, the shortest production cycle, the fewest top-line times, and the fewest downgrade times. Based on the optimal production scheduling scheme, i.e., the production scheduling strategy constraints, the first sub-problem is obtained through the basic configuration of factory modeling, i.e., the first order is arranged to the first production line to obtain the first filling production order. It is determined whether the filling production order meets the preset production scheduling strategy constraints. If it does, the current production scheduling result is sorted out. At this time, there are still N-1 orders and 1 filling production order already arranged on production line 1. The N-1 orders are used as a new set of original problems to enter a new round of recursive decomposition until all N orders are completed. Finally, all filling production orders M on X production lines are formed, and the optimal production scheduling result and the corresponding Gantt chart are output.

[0061] Example 2, refer to Figure 6 As an embodiment of the present invention, a lubricating oil filling scheduling system based on dynamic programming is provided, including an order sorting module 10, a constraint modeling module 20, and a scheduling optimization module 30. The order sorting module 10 includes a sorting rule execution submodule 11 and a product merging and original problem set generation submodule 12.

[0062] The sorting rule execution submodule 11 is used to sort packaged oil orders according to order type, order priority, and order demand date. The order type is sorted according to the principle that forecasted orders take precedence over inventory orders, so that forecasted demand has a higher processing priority in production scheduling. When the order types are the same, they are further sorted according to the priority parameters contained in the order. For orders with the same priority, the final sorting is performed according to the demand date, so that the order with earlier demand enters the priority position of the production scheduling logic.

[0063] The product merging and original problem set generation submodule 12 is used to identify all order entries with the same lubricant specifications, packaging type and production parameters, merge them and accumulate the demand. The merged order structure serves as the basic unit of the original set of dynamic programming, enabling dynamic programming to be performed in an optimized set space when selecting order subsets, state transitions and production line allocation.

[0064] The constraint modeling module 20 includes a capacity and availability modeling submodule 21 and a material adaptability modeling submodule 22.

[0065] The capacity and available time modeling submodule 21 is used to calculate the effective working time that the production line can be scheduled within the current production cycle based on information such as the maximum processing capacity of the production line, equipment operating status, duration of pre-tasks, factory calendar and maintenance plan, and at the same time record the last end time of the pre-tasks to determine the remaining working window that the current task can use.

[0066] The material adaptability modeling submodule 22 is used to encode the material parameters such as oil categories, packaging specifications, and filling methods that each production line can support, and establish an adaptability matrix that can be directly called by the production scheduling optimization module. It can quickly determine which production line each filling production order can be executed on, providing a feasibility basis for sub-problem decomposition and grouping.

[0067] The production scheduling optimization module 30 includes a production line screening and trimming sub-module 31 and a dynamic programming optimal production scheduling calculation sub-module 32.

[0068] The production line screening and trimming submodule 31 is used to match the lubricant specifications and packaging types of the order using the material adaptation matrix in the production line model, and initially screen the production lines that can handle the order. At the same time, it filters the candidate production lines based on the available time window of the production line, the current task occupancy time, and the equipment operating status (including shutdown, maintenance, and repair). Production lines that cannot meet the production needs of the order are removed from the set, which is used for dynamic planning of the effective set of production lines for state transition.

[0069] The dynamic programming optimal scheduling calculation submodule 32 is used under the multi-dimensional weighted 301 calculation constraints: , in, This indicates the collection of orders. Assigned to the front The minimum completion time on a production line, during state transition, is determined by changing the set. Selecting a subset of orders This makes the subset The corresponding filling production order was assigned to the first One production line processes the orders, while the remaining orders... From the beginning Processing on one production line, Indicates the preceding One production line completes the remaining orders Minimum required completion time Represents a subset of orders in front Total processing time of the production line Indicates from set The removed order subset is compared. and When the smaller of the two values ​​is used as the overall completion time for the current combination, this is achieved by iterating through all possible combinations. To determine the order set, obtain the minimum overall completion time across the entire combination. in front The optimal allocation scheme on the production line.

[0070] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a dynamic programming-based lubricating oil filling and scheduling method as proposed in the above embodiment.

[0071] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements a dynamic programming-based lubricating oil filling and scheduling method as proposed in the above embodiment.

[0072] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0074] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0075] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A lubricating oil filling scheduling method based on dynamic programming, characterized in that, include: In response to the production scheduling request, obtain the set of packaged oil orders to be solved and the associated production line constraint parameters; The packaged oil order set is generated in the following way: Obtain the original order flow and sort the packaged oil orders according to the preset sorting rules; The sorting rules include order type, order priority, and order demand date; Based on the sorting, orders with the same material attributes and packaging specifications are dynamically aggregated to generate a single production node, thereby reducing the initial dimensionality of the dynamic programming algorithm. The production line constraint parameters are obtained based on the production line model, which includes the adaptation attributes of different production lines to material specifications, available working time, and equipment operating status. The production line constraint parameters also include concurrent shift limits; In the dynamic iterative solution step, the number of production lines in operation at any given time slot is limited by the concurrent shift limit. For any order in the packaged oil order set, a matching target production line is selected from the entire production line set based on the material specification compatibility attribute, and the remaining available time of the target production line within the order cycle is calculated. If the available remaining time meets the order requirements, the target production line is stored in the candidate production line subset corresponding to the order, so as to realize the grouping and pruning of the dynamic programming solution space; With minimizing the global production cost as the objective function, within the constraints of the candidate production line subset, the dynamic programming algorithm is used to iteratively calculate the state transition cost of each order on different candidate production lines. The state transition cost is at least positively correlated with the top line cleaning time during production line switching. Based on the calculated optimal path, output a filling production scheduling plan that includes production line identification, task sequence, and start and end times; The state transition cost is obtained through multi-dimensional weighted calculation, whereby the multi-dimensional weighting is defined by the scheduling strategy and includes, First weight: Difference in material packaging specifications before and after production line switchover, used to characterize top line cleaning losses; Second weight: The relationship between the grade of materials before and after the production line switchover, used to characterize the production loss due to downgrades; Wherein, the first weight is higher than the second weight; The calculation logic for the first weight is as follows: When the packaging specifications of a continuous production task are switched from low to high, the first generation of value is allocated. When switching from a high-specification to a low-specification, allocate the second-generation value; The value of the second generation is greater than that of the first generation.

2. The lubricating oil filling scheduling method based on dynamic programming as described in claim 1, characterized in that: The dynamic programming algorithm includes, ， in, This indicates the collection of orders. Assigned to the front The minimum completion time on a production line, during state transition, is determined by changing the set. Selecting a subset of orders This makes the subset The corresponding filling production order was assigned to the first One production line processes the orders, while the remaining orders... From the beginning Processing on one production line, Indicates the preceding One production line completes the remaining orders Minimum required completion time Represents a subset of orders in front Total processing time of the production line Indicates from set The removed order subset is compared. and When the smaller of the two values ​​is used as the overall completion time for the current combination, this is achieved by iterating through all possible combinations. To determine the order set, obtain the minimum overall completion time across the entire combination. in front The optimal allocation scheme on the production line.

3. The lubricating oil filling scheduling method based on dynamic programming as described in claim 1, characterized in that: The multi-dimensional weighted calculation includes, Orders are sorted according to their demand dates, with orders with earlier demand dates given priority in the production scheduling sequence. Orders with the same demand date are then sorted further according to their production cycle. During the production scheduling process, when the applicable conditions of different production scheduling strategies exist simultaneously, the production scheduling rules are executed according to the preset priority order. The priority order includes, in order, meeting the order of order demand dates, the shortest production cycle, minimizing the number of times the production line is completed, and minimizing the number of times the product is degraded. When implementing the principle of minimizing the number of top-line production runs, priority should be given to arranging lower-specification products into the production scheduling sequence; When implementing the principle of minimizing the number of downgrades, priority should be given to scheduling high-specification products; During the production scheduling process, the task distribution of each production line is monitored in real time based on the production line load information recorded by the production line model, and the task allocation is adjusted according to the load level of each production line. When a production line becomes unavailable due to equipment failure or maintenance schedule during the production process, the production scheduling strategy is readjusted according to the above priority order, and the unfinished tasks are redistributed to other production lines that meet the material adaptability and available working time requirements.

4. A lubricating oil filling scheduling system based on dynamic programming, employing the lubricating oil filling scheduling method based on dynamic programming as described in any one of claims 1 to 3, characterized in that: Includes an order sorting module (10), a constraint modeling module (20), and a production scheduling optimization module (30); The order sorting module (10) is used to obtain the original order flow, sort the packaged oil orders according to order type, order priority and order demand date, and dynamically aggregate orders with the same material attributes and packaging specifications to generate a packaged oil order set as input data for the dynamic programming algorithm. The constraint modeling module (20) is used to analyze the material adaptability, available working time and equipment operating status of each production line based on the production line model, generate production line constraint parameters, and construct resource constraint conditions in the dynamic programming state transition process in combination with concurrent shift quota. At the same time, it filters target production lines and constructs a subset of candidate production lines based on the order and production line adaptability relationship, and performs grouping and pruning of the solution space. The production scheduling optimization module (30) is used to solve the state transition of the order set by using dynamic programming algorithm within the constraint range of the candidate production line subset with the goal of minimizing the global production cost. It calculates the state transition cost according to the multi-dimensional weighted rules defined by the production scheduling strategy, and outputs a filling production scheduling plan containing production line identifier, task order and start and end time according to the optimal state transition path.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the lubricating oil filling and scheduling method based on dynamic programming as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the lubricating oil filling and scheduling method based on dynamic programming as described in any one of claims 1 to 3.

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