Process manufacturing industry-oriented production workshop production scheduling method

Abstract

The invention provides a process manufacturing industry-oriented production workshop production scheduling method. The process manufacturing industry-oriented production workshop production scheduling method comprises the steps of setting an evaluation index system, an evaluation method, an optimization target and a production scheduling scene of a production scheduling scheme; based on the production scheduling optimization target, constructing a production workshop production scheduling optimization model; reading total data of a to-be-scheduled production workshop, and determining an actual scheduling constraint condition of the production workshop scheduling optimization model; performing simplified solution on the production workshop production scheduling optimization model to obtain an initial solution of a production scheduling scheme; on the basis of the initial solution of the production scheduling scheme, under the actual constraint condition, carrying out personalized solution on the production workshop production scheduling optimization model according to a production scheduling scene to obtain a personalized solution result; and repeating simplified solution and personalized solution, and outputting an optimal personalized solution result as an optimal production scheduling scheme. According to the method, the quality of the production scheduling scheme is ensured while the production scheduling speed is increased, and personalized optimization schemes can be obtained for various production scheduling scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of workshop production scheduling in process manufacturing industry, and in particular to a production workshop production scheduling method for process manufacturing industry. Background Art

[0002] At present, the scheduling methods that are more widely used in process manufacturing industries mainly include three categories: manual experience scheduling method, heuristic rule scheduling method, and operations research model scheduling method.

[0003] Manual experience-based production scheduling usually relies on the accumulated experience of production planners and uses software tools such as Excel to prepare workshop production scheduling plans. As the number of brands scheduled increases, manual scheduling takes a long time and the feasibility of scheduling plans is lacking.

[0004] The heuristic rule scheduling method usually extracts and solidifies manual experience into simple rules, and compiles workshop production scheduling plans according to fixed processes through computer programming and other methods. This scheduling method is not flexible enough when facing different production needs. After completing a version of the production scheduling plan, it takes a long time for manual work to adjust the plan according to the actual production environment and requirements, and the optimization space of this plan is small, resulting in a certain proportion of production resource waste and increased production costs.

[0005] The operations research model scheduling method usually uses mathematical modeling to model various constraints and goals in the production process into an operations research optimization model, and then solves it through the corresponding operations research optimization method. This scheduling method has obvious disadvantages when solving large-scale scheduling problems, that is, it takes a long time to solve and is difficult to adapt to the real-time changing actual production environment. In addition, this method has relatively high requirements for the accuracy and integrity of engineering data, and it is difficult to apply and implement for enterprises with immature information and automation. Summary of the invention

[0006] The present invention provides a two-stage production workshop scheduling method for process manufacturing industry, which is used to solve the defects of the prior art scheduling method, such as poor optimization level, low solution efficiency and insufficient scheduling flexibility, and improve the efficiency, quality and scientific rationality of the production workshop scheduling method.

[0007] The present invention provides a production workshop scheduling method for process manufacturing industry, comprising the following steps.

[0008] S1. Set the evaluation index system, evaluation method, optimization target and production scheduling scenario of the production scheduling plan; S2. Reading the full data of the production workshop to be scheduled, and determining the actual production scheduling constraints based on the full data; S3. Building a production workshop production scheduling optimization model based on the production scheduling optimization target and the actual production scheduling constraints; S4, simplifying and solving the production workshop scheduling optimization model to obtain an initial solution for the production scheduling plan; S5. Based on the initial solution of the production scheduling plan, the production workshop production scheduling optimization model is personalized solved according to the production scheduling scenario to obtain a personalized solution result; S6. Repeat S4-S5, record the obtained personalized solution results, until the preset optimization time is reached, and output the optimal solution of each personalized solution result as the optimal production scheduling plan.

[0009] According to a production workshop scheduling method for process manufacturing industry provided by the present invention, the evaluation index system includes: supply management index, production control management index, production continuity management index, production efficiency management index and production manpower management index.

[0010] According to a production workshop scheduling method for process manufacturing industry provided by the present invention, the full data of the production workshop to be scheduled includes: monthly production plan, weekly supply demand plan, machine capacity information, machine grouping information, brand machine correspondence, machine monthly maintenance plan, average machine replacement time, workshop work calendar, raw material production batch requirements, process constraints, raw and auxiliary material monthly supply plan, initial supply inventory, initial machine start-up status, and initial brand shortage.

[0011] According to a production workshop scheduling method for process manufacturing provided by the present invention, the optimization objectives include logistics delivery timeliness rate, brand change times, number of production working days, and machine production balance; The logistics delivery timeliness rate is characterized by the out-of-stock cost of each cycle, and the calculation formula of the out-of-stock cost of each cycle is:

[0012] in, represents the unit out-of-stock penalty cost, Indicates production cycle t The out-of-stock quantity at Represents the set of all brands, Represents the set of all production cycles; The number of brand changes is represented by the production cost caused by the brand change. The calculation formula of the production cost caused by the brand change is:

[0013] in, Represents the cost of a single card exchange, Representation cycle t Machine l Whether a card swap has occurred, if so , otherwise 0, Represents the set of all machines; The number of production working days is represented by the total start-up cost of the machine, and the calculation formula of the total start-up cost of the machine is:

[0014] in, Indicates production cycle Machine The start-up cost increases with the cycle t increases linearly with the increase of Representation cycle Machine Produced brand J, It means it is not produced. Represents the set of production correspondences between brands and machines; The balance of machine production is characterized by the cost of production imbalance, and the calculation formula of the cost of production imbalance is:

[0015] in, Indicates the production area i The production imbalance cost, represents the number of production areas, Indicates the production area i The total number of machines that can be started, Indicates the machine in the production cycle l The number of working cycles.

[0016] According to a production workshop scheduling method for process manufacturing industry provided by the present invention, the scheduling scenario includes one or more of a scheme with the least number of card changes, a scheme with the least number of working days, and a scheme with the highest production balance; Among them, the scheme with the least number of card changes is to output the scheme with the least total number of card changes for all machines within the production scheduling cycle, under the premise of optimizing the logistics delivery timeliness rate; The plan with the least number of working days is based on the premise of optimizing the on-time delivery rate of logistics, and outputs the plan with the least number of working days in the production scheduling cycle; The highest production balance plan is based on the premise that the logistics delivery timeliness rate is optimized, and outputs the highest machine production balance plan within the production scheduling cycle.

[0017] According to a production workshop scheduling method for process manufacturing provided by the present invention, S2, based on the production scheduling optimization target and the actual production scheduling constraints, constructing a production workshop scheduling optimization model, specifically comprising: Based on the production scheduling optimization target and the actual production scheduling constraints, a production workshop scheduling optimization model is constructed with the goal of minimizing the total scheduling cost, including: The first objective function:

[0018] The first constraint is: Constraints 1. Production and sales balance constraints:

[0019] in, Representation cycle t Machine l Production brand j the number of Representation cycle t brand j Out of stock quantity; Representation cycle t brand j Inventory volume; Representation cycle t brand j Quantity of demand; Constraint 2: Machine capacity constraints:

[0020] in, Representation cycle t Machine l Production brand j time; Display machine l In cycle t Total available production time; is a 0-1 variable, Representation cycle t Machine l Production brand specifications j , otherwise the opposite is true; Constraint 3: Machine capacity and brand change constraints:

[0021] in, Display machine l From the brand Switch to Brand The time consumed; It is a 0-1 variable. Time represents cycle t Machine l From the brand Switch to Brand , 0 means no switching; Represents the possible card exchange combinations of all machines; Constraint 4: Machine startup status:

[0022] Constraint 5: Determine whether the machine changes cards:

[0023] in, is a 0-1 variable, Representation cycle t Machine l A card swap was made, otherwise the opposite was true; Constraint 6: Machine brand change judgment:

[0024] Constraint 7: Constraints on the relationship between production time and output:

[0025] in, Display machine l Production brand j production capacity; Constraint 8: Constraints on the supply and consumption of raw and auxiliary materials:

[0026] in, Indicates raw materials i In cycle t Inventory volume; Indicates the brand of the unit j Consumption of raw materials i the number of Representation cycle t Raw materials i the supply of Indicates all brands j Required raw materials i A collection of; Constraint 9: Machine grouping constraints:

[0027] in, It represents a collection of machine groups, requiring that machines in the same group can only produce the same brand and specifications in the same cycle; Constraint 10: Constraint on the number of work shifts:

[0028] in, Display machine lThe number of production cycles within the planning period; Constraint 11: Production batch quantity constraint:

[0029] in, Indicates the minimum production batch of brand specification j; N is an integer value; Constraint 12, non-negative and integer constraints:

[0030] .

[0031] According to a production workshop scheduling method for process manufacturing provided by the present invention, S4, simplifying and solving the production workshop scheduling optimization model to obtain an initial solution of the scheduling plan, specifically includes: S41, removing the nonlinear constraint condition, the first integer constraint condition and the nonlinear optimization target in the optimization target in the actual production scheduling constraint condition, and relaxing the second integer constraint; wherein the removed nonlinear constraint includes constraint condition 3 and constraint condition 5, the first integer constraint includes constraint condition 6, the removed nonlinear optimization target is machine production balance; the second integer constraint is constraint condition 11; S42, adjusting the first objective function, setting the logistics delivery timeliness rate as the first stage optimization target, and establishing a simplified model of the production workshop scheduling optimization model, including: The second objective function:

[0032] Second constraint:

[0033] ; S43. Solve the simplified model to obtain an initial solution for the production scheduling plan.

[0034] According to a production workshop scheduling method for process manufacturing provided by the present invention, S5, based on the initial solution of the production scheduling plan, the production workshop scheduling optimization model is personalized solved according to the production scheduling scenario to obtain a personalized solution result, specifically including: S51. Set the second-stage optimization goals for different production scheduling scenarios; S52. Optimize the production sequence of brands on the unit to improve the timeliness of logistics delivery and reduce the time consumed by brand replacement; S53, repairing the integer constraint of the production batch quantity, so that the infeasible initial solution of the production scheduling plan becomes a feasible solution; S54, merging production tasks and optimizing the second stage optimization target; S55. Optimize overall balance.

[0035] According to a production workshop scheduling method for process manufacturing provided by the present invention, S51, setting a second-stage optimization target for different scheduling scenarios specifically includes: Establish a production scheduling optimization target array, defined as: [A, B, C, D], where AD are all 0-1 variables, where A represents the logistics delivery timeliness rate, A=1 represents the highest logistics delivery timeliness rate, and A=0 represents no requirement for this target; B represents the number of card changes, B=1 represents the minimum number of card changes, and B=0 represents no requirement for this target; C represents the number of working days, C=1 represents the minimum number of working days, and C=0 represents no requirement for this target; D represents balance, D=1 represents the highest balance, and D=0 represents no requirement for this target; Determine the target production schedule to be acquired based on business needs; According to the target production scheduling plan, A, B, C, and D in the production scheduling optimization target array [A, B, C, D] are set to 0 or 1 to set the second-stage optimization target.

[0036] According to a production workshop scheduling method for process manufacturing provided by the present invention, S52, optimizing the production sequence of brands on the unit to improve the logistics delivery timeliness rate and reduce the time consumed by brand replacement, specifically includes: Traverse the sequence combinations of brands to be produced in the same unit, and determine the sequence combination of brands that maximizes the logistics delivery timeliness rate or minimizes the total brand change consumption time as the production order of the brands on the unit.

[0037] According to a production workshop scheduling method for process manufacturing provided by the present invention, S53, repairing the integer constraint of the production batch quantity so as to make the infeasible initial solution of the scheduling plan become a feasible solution, specifically comprising: Assume the initial solution of the production scheduling plan g Production brand j The number of , according to the preset minimum batch requirements, increase the corresponding production volume to meet the minimum batch of the unit: ,

[0038] in, It is a brand j Minimum batch production volume, It's the crew g Production brand j The minimum number of batches.

[0039] According to a production workshop scheduling method for process manufacturing provided by the present invention, S54, merging production tasks, and optimizing the second-stage optimization target specifically includes: For any brand j , traverse all units producing this brand in pairs. If there are multiple units in the pairwise traversal process , , …, , moving the production of different units; If you optimize the target array during the move , if there is an indicator corresponding to 1 that rises, and other indicators that are 1 do not decrease, the move is executed; otherwise, the move is not executed.

[0040] According to a production workshop scheduling method for process manufacturing industry provided by the present invention, S55, performing overall balance optimization, specifically includes: Under the premise of not exceeding the upper limit of safety stock and not violating the batch quantity, increase the output of some units with lower production volume to optimize the overall balance of the production scheduling plan.

[0041] According to a production workshop scheduling method for process manufacturing provided by the present invention, S4-S5 are repeated, and the obtained personalized solution results are recorded until a preset optimization time is reached, and the optimal solution of each personalized solution result is output as the optimal scheduling plan, which specifically includes: Determine whether the current running time of each step exceeds the preset optimization time; If the preset optimization time is not exceeded, the current personalized solution results are recorded, and the simplified solution and personalized solution are repeated; If the preset optimization time is exceeded, the personalized solution results in all records are output; Each of the recorded personalized solution results is evaluated using the evaluation index system and the evaluation method, and the best personalized solution result is output as the best production scheduling plan.

[0042] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements any of the above-described production workshop scheduling methods for process manufacturing industries.

[0043] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements any of the above-mentioned production workshop scheduling methods for process manufacturing industries.

[0044] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned production workshop scheduling methods for process manufacturing industries.

[0045] The production workshop scheduling method for process manufacturing provided by the present invention includes: S1, setting an evaluation index system, evaluation method, optimization target and scheduling scenario of a scheduling plan; S2, reading the full amount of data of the production workshop to be scheduled, and determining the actual scheduling constraints based on the full amount of data; S3, constructing a production workshop scheduling optimization model based on the scheduling optimization target and the actual scheduling constraints; S4, simplifying and solving the production workshop scheduling optimization model to obtain an initial solution of the scheduling plan; S5. Based on the initial solution of the production scheduling plan, the production workshop scheduling optimization model is solved individually according to the production scheduling scenario to obtain personalized solution results; S6. S4-S5 are repeated, and the personalized solution results are recorded until the preset optimization time is reached, and the optimal solution of each personalized solution result is output as the optimal production scheduling plan; the present invention adopts a two-stage production scheduling optimization method. In the first stage, a high-quality model initial solution is obtained by simplifying the model, which speeds up the model solving speed. In the second stage, complex constraints of the model are repaired by heuristic rules, and personalized optimization is achieved for different production scheduling scenarios, meeting the needs of process manufacturing enterprises for intelligent production scheduling methods. Compared with the prior art, the present invention can speed up the production scheduling while ensuring the quality of the production scheduling plan, and can obtain personalized optimization plans for a variety of production scheduling scenarios, which is more suitable for complex production scheduling scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 This is one of the flow diagrams of the production workshop scheduling method for process manufacturing industry provided by the present invention.

[0048] Figure 2 This is the second flow chart of the production workshop scheduling method for process manufacturing industry provided by the present invention.

[0049] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] At present, the manual experience scheduling method, heuristic rule scheduling method and operations research model scheduling method that are widely used in my country's process manufacturing industry all have certain defects.

[0052] Among them, manual experience scheduling is the most commonly used scheduling method in my country's process manufacturing industry. This scheduling method is characterized by relying on the rich experience accumulated by production planners, and usually using software tools such as Excel to prepare workshop scheduling plans. The advantage of this scheduling method is that the rich experience of the scheduling planner can be used to flexibly adjust the production plan to cope with various changes in production needs. However, this scheduling method also has obvious disadvantages. First, with the increase in the number of scheduled brands, manual scheduling takes a lot of time to complete a version of the plan, and sometimes even a feasible plan cannot be given; second, due to the limited computing power of the human brain, there is still a certain proportion of room for optimization and improvement in the scheduled production plan. Optimizing the scheduling plan means a faster completion cycle and lower production costs.

[0053] The heuristic rule scheduling method is the most commonly used automatic scheduling method in my country's process manufacturing industry. The characteristic of this scheduling method is to extract and solidify manual experience into simple rules, and compile workshop scheduling plans according to fixed processes through computer programming and other methods. The advantage of this scheduling method is that a version of the scheduling plan can be completed quickly and automatically through the solidified simple rules, reducing the number of manual repetitive thinking and operations. The disadvantage of this scheduling method is that once the manual experience is solidified into simple rules, the scheduling plan is not flexible enough when facing different production needs, which often leads to the fact that although the computer can give a version of the scheduling plan, it still takes a long time for manual adjustment according to the actual production environment and requirements. In addition, since it is solidified into simple rules, it also reduces the space for optimization, resulting in a certain proportion of waste of production resources and increased production costs.

[0054] The scheduling method based on the operations research model is a scheduling method that has not been well implemented in my country's process manufacturing industry. The characteristic of this scheduling method is to use mathematical modeling to model various constraints and goals in the production process into an operations research optimization model, and then solve it through the corresponding operations research optimization method. The advantage of this scheduling method is that it can find the optimal scheduling solution for small-scale scheduling problems, achieving the lowest cost and maximizing efficiency. However, this method has obvious disadvantages when solving large-scale scheduling problems, that is, it takes a long time to solve. In the face of real-time changing actual production environments, the long solution time is obviously unacceptable. In addition, this method has relatively high requirements for the accuracy and integrity of engineering data, and it is difficult to apply and implement for enterprises with immature information and automation.

[0055] In view of this, the present invention provides a production workshop scheduling for process manufacturing industry, which integrates the advantages of heuristic rule scheduling method and operation model scheduling method, and is used to solve the problems of poor optimization level, low solution efficiency and insufficient scheduling flexibility in existing scheduling technologies, and further improve the efficiency, quality and scientific rationality of production workshop scheduling methods.

[0056] The present invention is described in detail below in conjunction with the accompanying drawings of the specification. The specific operating method in the method embodiment can also be applied to the device embodiment or the system embodiment. In the description of the present invention, unless otherwise specified, "at least one" includes one or more. "Multiple" refers to two or more. For example, at least one of A, B and C includes: A exists alone, B exists alone, A and B exist at the same time, A and C exist at the same time, B and C exist at the same time, and A, B and C exist at the same time. In the present invention, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a kind of association relationship that describes the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0057] In some specific embodiments of the present invention, Figure 1 As shown, this solution provides a production workshop scheduling method for process manufacturing industry, including: S1. Set the evaluation index system, evaluation method, optimization target and production scheduling scenario of the production scheduling plan; S2. Reading the full data of the production workshop to be scheduled, and determining the actual production scheduling constraints based on the full data; S3. Building a production workshop production scheduling optimization model based on the production scheduling optimization target and the actual production scheduling constraints; S4, simplifying and solving the production workshop scheduling optimization model to obtain an initial solution for the production scheduling plan; S5. Based on the initial solution of the production scheduling plan, the production workshop production scheduling optimization model is personalized solved according to the production scheduling scenario to obtain a personalized solution result; S6. Repeat S4-S5, record the obtained personalized solution results, until the preset optimization time is reached, and output the optimal solution of each personalized solution result as the optimal production scheduling plan.

[0058] It should be noted that the existing production scheduling plans have poor optimization levels, low solution efficiency, and insufficient scheduling flexibility. It is difficult to obtain personalized optimization plans in various production scheduling scenarios, and to ensure the quality of production scheduling plans while speeding up production scheduling. Therefore, it is unable to meet the personalized scheduling needs of enterprises in complex production scheduling scenarios.

[0059] Therefore, the present invention constructs a production workshop scheduling optimization model, and performs simplified solution and personalized solution on the production workshop scheduling optimization model respectively, and obtains the initial value of the production scheduling plan through the simplified solution; on the basis of the initial value, the production workshop scheduling optimization model is personalized solved, and the optimal solution of the personalized solution is output as the optimal production scheduling plan. Through the above-mentioned settings, the present invention can ensure the quality of the production scheduling plan while speeding up the production scheduling, and meet the personalized scheduling needs of the enterprise under complex production scheduling scenarios.

[0060] The above steps are described in detail below through specific embodiments.

[0061] S1. Set the evaluation index system, evaluation method, optimization target and production scheduling scenario of the production scheduling plan; In some possible implementations of the present invention, the evaluation index system includes: supply management index, production control management index, production continuity management index, production efficiency management index and production manpower management index.

[0062] Specifically, an evaluation index system is set up, and the evaluation method and scoring rules for each index are clarified; the evaluation index system includes five major indicators: supply management, production control management, production continuity management, production efficiency management, and production manpower management; the analytic hierarchy process is selected as the evaluation method, and the weight of each index is given after expert scoring. The total plan score needs to be calculated based on the scoring rules for each index and the evaluation method.

[0063] In some possible implementations of the present invention, optimization targets are set, and specific optimization targets include: logistics delivery timeliness rate, brand change times, number of production working days, and machine production balance; the calculation method of each optimization target is as follows: (1) Logistics delivery timeliness rate: It is expressed by the out-of-stock cost of each cycle and is calculated as follows:

[0064] in, represents the unit out-of-stock penalty cost, Indicates production cycle t The out-of-stock quantity at Represents the set of all brands, Represents the set of all production cycles; (2) Number of brand changes: expressed as the production cost caused by brand changes, calculated as follows:

[0065] in, Represents the cost of a single card exchange, Representation cycle t Machine l Whether a card swap has occurred, if so , otherwise it is 0. Represents the set of all machines; (3) Number of production working days: It is expressed by the total start-up cost of the machine. The more working days, the higher the start-up cost. The calculation formula is as follows:

[0066] in, Indicates production cycle Machine The start-up cost increases with the cycle t increases linearly with the increase of Representation cycle Machine Produced brand J, It means it is not produced. Represents the set of production correspondences between brands and machines; (4) Balance of machine production: It is expressed by the cost of uneven production. The more balanced the production is within the same production area, the smaller the variance is, and the smaller the cost is. The calculation formula is as follows:

[0067] in, Indicates the production area i The production imbalance cost, represents the number of production areas, Indicates the production area i The total number of machines that can be started, Indicates the machine in the production cycle l The number of working cycles.

[0068] In some possible implementations of the present invention, the production scheduling scenarios include one or more of a plan with the least number of brand changes, a plan with the least number of working days, and a plan with the highest production balance; Among them, the scheme with the least number of card changes is to output the scheme with the least total number of card changes for all machines within the production scheduling cycle, under the premise of optimizing the logistics delivery timeliness rate; The plan with the least number of working days is based on the premise of optimizing the on-time delivery rate of logistics, and outputs the plan with the least number of working days in the production scheduling cycle; The highest production balance plan is based on the premise that the logistics delivery timeliness rate is optimized, and outputs the highest machine production balance plan within the production scheduling cycle.

[0069] Specifically, the workshop production scheduling scenarios are set to meet the needs of different business scenarios; in a specific embodiment, the workshop production scheduling scenarios include but are not limited to: a plan with the least number of card changes, a plan with the least number of working days, and a plan with the highest production balance; Specifically, the plan with the least number of card changes refers to the plan with the least total number of card changes for all machines within the production scheduling cycle, with the priority given to the timely delivery of logistics. Minimum number of working days plan: refers to the plan with the minimum number of working days in the production scheduling cycle, with the priority given to the timely delivery of logistics. The plan with the highest production balance refers to the plan with the highest production balance of machines within the output scheduling cycle, under the premise of giving priority to the timely delivery of logistics, that is, it requires that machines in the same area start and end at the same time as much as possible.

[0070] S2. Reading the full amount of data of the production workshop to be scheduled, and determining the actual scheduling constraints of the production workshop scheduling optimization model based on the full amount of data; In some possible implementations of the present invention, the full amount of data required for workshop production scheduling is collected, wherein the full amount of data of the production workshop to be scheduled includes but is not limited to: monthly production plan, weekly supply demand plan, machine capacity information, machine grouping information, brand machine correspondence, machine monthly maintenance plan, average machine replacement time, workshop work calendar, raw material production batch requirements, process constraints, raw and auxiliary material monthly supply plan, initial supply inventory, initial machine start-up status, and initial brand shortage.

[0071] Specifically, the monthly production plan refers to the monthly production quantity plan for each brand issued by the company to the factory to meet market demand and inventory management requirements; for example, brand A, 3,000 pieces are produced this month; brand B, 1,500 pieces this month; Weekly supply demand plan: refers to the weekly delivery quantity plan for each brand issued by the company to the factory to meet logistics delivery needs; for example, for brand A, 1,000 pieces will be shipped in the first week of this month, 1,000 pieces will be shipped in the second week, 500 pieces will be shipped in the third week, and 500 pieces will be shipped in the fourth week; Machine capacity information: refers to the unit time capacity data of each brand produced by the machine; for example, machine 1 produces brand A at 7.8 pieces / hour; machine 1 produces brand B at 6.5 pieces / hour; Machine grouping information: refers to the information that machines are divided into different groups due to production process requirements. Machines in the same group can only produce the same brand at the same time; for example, machines 1#-3# are group 1; Brand-machine correspondence: refers to the correspondence between machines and production brands, because some brands can only be produced on specific machines; for example, machine 1 corresponds to brand 1 and brand 2; machine 2 corresponds to brand 2 and brand 3; Machine monthly maintenance plan: refers to the maintenance plan of the production machine this month. The machine cannot be arranged for production during the maintenance period. For example, machine 1, on the 4th of this month - day shift - 8:00-12:00, maintenance for 4 hours; Average machine brand change time: refers to the time it takes for a machine to switch from producing one brand to another. For example, it takes 0.5 hours for machine 1 to switch from brand 1 to brand 2. Workshop work calendar: refers to the actual working days that can be used for scheduling in the factory, to avoid scheduling production due to holidays or special events; for example, Monday to Friday of all weeks of the month can be used for production, and the rest of the week can be used for rest; Raw material production batch requirement: refers to the minimum batch requirement for raw material production to meet the scale effect. The minimum production batch can get the minimum brand production batch; for example, the minimum production batch of brand 1-raw material A is 1 batch, and then the minimum quantity of brand 1 produced by 1 batch of raw material A is 200 pieces; Process constraints: specific process requirements in the actual production process; for example, brand A and brand B cannot be produced at the same time, the number of brands produced in production area 1 is greater than 3, etc. Monthly inventory and supply plan of raw materials and auxiliary materials: refers to the initial inventory and monthly supply data of raw materials and auxiliary materials required for the production of this month's brand. Production cannot be arranged without raw materials and auxiliary materials. For example, raw material A - initial inventory 200, supply 1000 on the 5th of this month; raw material B - initial inventory 500, supply 500 on the 10th of this month; Initial stock quantity: refers to the initial stock quantity of each brand and specification, which can be directly used to meet the delivery needs; for example, the initial stock quantity of brand 1 is 200 pieces; the initial stock quantity of brand 2 is 30 pieces; Initial start-up status: refers to the state of the brand specifications of each machine at the beginning of the period. This is considered to ensure production continuity and reduce the number of brand changes. For example, machine 1 initially produces brand 2; machine 2 initially produces brand 3; Brand and specification out-of-stock quantity at the beginning of the period: refers to the out-of-stock quantity of each brand and specification at the beginning of the period. The main reason is that there was market demand in the previous period but no supply to meet it, which was delayed to the quantity that needed to be met in this period; for example, the initial out-of-stock quantity of brand 1 was 500 pieces.

[0072] It is understandable that in a specific production scenario, the above data needs to meet certain limiting conditions, or serve as limiting conditions for production scheduling, thereby constituting the actual scheduling constraints of the production scheduling plan.

[0073] S3. Building a production workshop production scheduling optimization model based on the production scheduling optimization target and the actual production scheduling constraints; In some possible implementations of the present invention, based on the production scheduling optimization target and the actual production scheduling constraints, a production workshop scheduling optimization model is constructed, which specifically includes: Based on the production scheduling optimization goal and the actual production scheduling constraints, a production workshop production scheduling optimization model is constructed with the goal of minimizing the total production scheduling cost, including constructing the first objective function of the production scheduling optimization model and constructing the first constraint condition of the production scheduling optimization model, which are respectively: The first objective function:

[0074] Specifically, the objective function minimizes the total cost of production scheduling, which includes the first out-of-stock penalty cost, the second brand replacement cost, the third working day cost, and the fourth equilibrium cost; The first constraints include: Constraints 1. Production and sales balance constraints:

[0075] in, Representation cyclet Machine l Production brand j the number of Representation cycle t brand j Out of stock quantity; Representation cycle t brand j Inventory volume; Representation cycle t brand j Quantity of demand; Constraint 2: Machine capacity constraints:

[0076] in, Representation cycle t Machine l Production brand j time; Display machine l In cycle t Total available production time; is a 0-1 variable, Representation cycle t Machine l Production brand specifications j , otherwise the opposite is true; Constraint 3: Machine capacity and brand change constraints:

[0077] in, Display machine l From the brand Switch to Brand The time consumed; It is a 0-1 variable. Time represents cycle t Machine l From the brand Switch to Brand , 0 means no switching; Represents the possible card exchange combinations of all machines; Constraint 4: Machine startup status:

[0078] Constraint 5: Determine whether the machine changes cards:

[0079] in, is a 0-1 variable, Representation cycle t Machinel A card swap was made, otherwise the opposite was true; Constraint 6: Machine brand change judgment:

[0080] Constraint 7: Constraints on the relationship between production time and output:

[0081] in, Display machine l Production brand j production capacity; Constraint 8: Constraints on the supply and consumption of raw and auxiliary materials:

[0082] in, Indicates raw materials i In cycle t Inventory volume; Indicates the brand of the unit j Consumption of raw materials i the number of Representation cycle t Raw materials i the supply of Indicates all brands j Required raw materials i A collection of; Constraint 9: Machine grouping constraints:

[0083] in, It represents a collection of machine groups, requiring that machines in the same group can only produce the same brand and specifications in the same cycle; Constraint 10: Constraint on the number of work shifts:

[0084] in, Display machine l The number of production cycles within the planning period; Constraint 11: Production batch quantity constraint:

[0085] in, Indicates the minimum production batch of brand specification j; N is an integer value; Constraint 12, non-negative and integer constraints:

[0086] .

[0087] In some possible implementations of the present invention, a two-stage optimization is performed on the production workshop scheduling optimization model constructed in the above embodiments, wherein the purpose of the first stage optimization is to simplify the production workshop scheduling optimization model to obtain an initial solution for the scheduling plan, and the purpose of the second stage optimization is to perform scenario-specific optimization and initial solution constraint repair.

[0088] In some possible implementations of the present invention, S4, simplifying and solving the production workshop scheduling optimization model to obtain an initial solution of the scheduling plan, specifically includes: S41, removing the nonlinear constraint condition in the actual production scheduling constraint condition, the first integer constraint condition and the nonlinear optimization target in the optimization target, and relaxing the second integer constraint; In this step, the nonlinear constraints removed include constraint 3 and constraint 5, the first integer constraint includes constraint 6, the nonlinear optimization target removed is the machine production balance; the second integer constraint is constraint 11; That is to say, in this step, nonlinearity, partial integer constraints and nonlinear optimization targets are temporarily not considered, and partial integer constraints are relaxed at the same time; wherein, the nonlinear constraints and partial integer constraints not considered include constraints 3, 5, and 6 in the first constraint condition, and the nonlinear optimization target not considered includes the machine production balance optimization target; the relaxed integer constraints include constraint 11 in the first constraint condition; S42, adjusting the first objective function, setting the logistics delivery timeliness rate as the first stage optimization target, and establishing a simplified model of the production workshop scheduling optimization model, that is, re-adjusting the first objective function to only consider the delivery timeliness rate, and establishing a simplified optimization model of the first stage including the second objective function and the second constraint condition, which are as follows: The second objective function:

[0089] Second constraint:

[0090] ; It is understandable that after this step is completed, the optimization solution time can also be set, for example, 300s. If the running time of the subsequent calculation step exceeds the set optimization time, the production scheduling result is directly output, otherwise continue to the next step; S43. Solve the simplified model to obtain an initial solution for the production scheduling plan.

[0091] Specifically, a mature commercial / open source solver can be used to calculate the simplified production scheduling model, and the calculation solution is used as the initial solution for the second-stage optimization.

[0092] In some possible implementations of the present invention, after the first stage optimization is completed, the calculation scheme is used as the initial solution of the second stage optimization to start the second stage optimization.

[0093] In some possible implementations of the present invention, S5, based on the initial solution of the production scheduling plan, under the actual constraints, the production workshop production scheduling optimization model is personalized according to the production scheduling scenario to obtain a personalized solution result, specifically including: S51. Set the second-stage optimization goals for different production scheduling scenarios; Specifically, the first-stage optimization model only considers the timely delivery rate of goods, and does not consider the minimum number of brand changes, the minimum number of working days, and the highest balance. These three optimization indicators correspond to three different production scheduling scenarios, each with its own focus.

[0094] In a possible embodiment, S51, setting the second-stage optimization target for different production scheduling scenarios specifically includes: Establish a production scheduling optimization target array, defined as: [A, B, C, D], where AD are all 0-1 variables, where A represents the logistics delivery timeliness rate, A=1 represents the highest logistics delivery timeliness rate, and A=0 represents no requirement for this target; B represents the number of card changes, B=1 represents the minimum number of card changes, and B=0 represents no requirement for this target; C represents the number of working days, C=1 represents the minimum number of working days, and C=0 represents no requirement for this target; D represents balance, D=1 represents the highest balance, and D=0 represents no requirement for this target; Determine the target production schedule to be acquired based on business needs; According to the target production scheduling plan, A, B, C, and D in the production scheduling optimization target array [A, B, C, D] are set to 0 or 1 to set the second-stage optimization target.

[0095] For example, the second stage considers the optimization goal setting rules and methods under the following three production scheduling scenarios: Step 1: Establish a production scheduling optimization target array, [A, B, C, D], where AD are all 0-1 variables, where A represents the on-time rate of logistics delivery, A=1 represents the highest on-time rate of logistics delivery, and A=0 represents no requirement for this target; B represents the number of card changes, B=1 represents the minimum number of card changes, and B=0 represents no requirement for this target; C represents the number of working days, C=1 represents the minimum number of working days, and C=0 represents no requirement for this target; D represents balance, D=1 represents the highest balance, and D=0 represents no requirement for this target; Step 2: Set the desired production schedule according to business needs. For example, setting the production schedule optimization target array to [1,1,0,0] indicates that you want to obtain a plan with higher logistics timeliness and accuracy and the least number of changes. This process can be looped to output multiple different production schedules at once.

[0096] 52. Optimize the production sequence of brands on the unit to improve the timeliness of logistics delivery and reduce the time consumed by brand replacement; In a possible embodiment, S52, optimizing the production sequence of brands on the unit to improve the logistics delivery timeliness rate and reduce the time consumed by brand replacement, specifically includes: Traverse the sequence combinations of brands to be produced in the same unit, and determine the sequence combination of brands that maximizes the logistics delivery timeliness rate or minimizes the total brand change consumption time as the production order of the brands on the unit.

[0097] For example, the brand production sequence is traversed and adjusted one by one in units of units; the specific adjustment method is to traverse all the sequence combinations of brands that need to be produced in the same unit, and finally find a sequence combination of brands that can maximize the logistics delivery timeliness rate or minimize the total brand change consumption time. If no combination is found, the original production sequence is maintained.

[0098] S53, repairing the integer constraint of the production batch quantity, so that the infeasible initial solution of the production scheduling plan becomes a feasible solution; In a possible embodiment, S53, repairing the integer constraint of the production batch quantity so as to make the infeasible initial solution of the production scheduling plan into a feasible solution, specifically includes: Assume the initial solution of the production scheduling plan g Production brand j The number of , according to the preset minimum batch requirements, increase the corresponding production volume to meet the minimum batch of the unit: ,

[0099] in, It is a brand j Minimum batch production volume, It's the crew g Production brand j The minimum number of batches.

[0100] Specifically, the production batch size integer constraint is repaired to make the infeasible initial solution a feasible solution. The specific operation is as follows: Assume that the unit g Production brand j The number of To meet the minimum batch requirement, some production volume needs to be increased to meet the minimum batch requirement of the unit. ,in It is a brand j Minimum batch production volume, It's the crew g Production brand j The minimum number of batches.

[0101] S54, merging production tasks and optimizing the second stage optimization target; In a possible embodiment, S54, merging production tasks and optimizing the second-stage optimization target, specifically includes: For any brand j , traverse all units producing this brand in pairs. If there are multiple units in the pairwise traversal process , , …, , moving the production of different units; If you optimize the target array during the move , if there is an indicator corresponding to 1 that rises, and other indicators that are 1 do not decrease, the move is executed; otherwise, the move is not executed.

[0102] Specifically, merge production tasks and optimize set goals; the specific operation methods are as follows: j , traverse all units producing this brand in pairs. Assume that one unit is , and the other unit is , then consider of production moved to On, or of production moved to If the target array is optimized during the move, , if there is an increase in the corresponding indicator of 1, and the other indicators of 1 do not decrease, then accept the move.

[0103] S55. Optimize overall balance.

[0104] In a possible embodiment, S55, performing overall balance optimization, specifically includes: Under the premise of not exceeding the upper limit of safety stock and not violating the batch quantity, increase the output of some units with lower production volume to optimize the overall balance of the production scheduling plan.

[0105] In other words, overall balance optimization is an adjustment that increases the output of some units with lower production volumes without exceeding the upper limit of safety stock requirements and violating batch quantities, thereby improving the overall balance.

[0106] In some possible implementations of the present invention, S4-S5 are repeated, and the obtained personalized solution results are recorded until a preset optimization time is reached, and the optimal solution of each personalized solution result is output as the optimal production scheduling plan, which specifically includes: Determine whether the current running time of each step exceeds the preset optimization time; If the preset optimization time is not exceeded, the current personalized solution results are recorded, and the simplified solution and personalized solution are repeated; If the preset optimization time is exceeded, the personalized solution results in all records are output; Each of the recorded personalized solution results is evaluated using the evaluation index system and the evaluation method, and the best personalized solution result is output as the best production scheduling plan.

[0107] That is to say, based on the above embodiment, it is determined whether the current running time of each step exceeds the set optimization time. If it exceeds, the set optimization target production scheduling plan is output. If it does not exceed, the current target values ​​of each optimal plan are recorded, and the first optimization stage is continued to recalculate; Furthermore, when the step running time exceeds the set optimization time, the optimal solution for each indicator in all records is output. Through the pre-set evaluation system and evaluation method, the output multiple scheduling solutions are evaluated in multiple dimensions and overall, and the best scheduling solution is selected based on the actual factory needs and the execution preferences of the workshop planners, and then issued to the workshop for execution to complete the final scheduling task.

[0108] In a specific embodiment, Figure 2 As shown, Figure 2 This is the second flow chart of the production workshop scheduling method for process manufacturing provided by the present invention. Taking the production scheduling process of a cigarette packing factory in the tobacco industry as an example, the specific process of the method is as follows: Step 201: Set the evaluation index system, evaluation method, optimization target and production scheduling scenario; Specifically, Table 1 is the evaluation index system and scoring rules for the production scheduling plan of the cigarette factory workshop. As shown in Table 1, the evaluation method adopts the hierarchical analysis method. After expert scoring, the weights of each indicator are determined as 0.525 for supply management, 0.28 for production control management, 0.065 for production continuity management, 0.065 for production efficiency management, and 0.065 for production manpower management. Among them, the alternative plan refers to all the plans involved in the evaluation.

[0109] Table 1

[0110] Furthermore, it is necessary to set optimization goals; as described in the above embodiment, the optimization goals include four categories, namely, logistics delivery timeliness rate, brand change times, number of production working days, and machine production balance; the calculation method of each optimization goal is as described in the above embodiment.

[0111] At the same time, there are three scenarios for determining production scheduling: the scenario with the least number of card changes, the scenario with the least number of working days, and the scenario with the highest balance. The corresponding optimization targets are represented by the optimization target array in step 5.1 of the invention content, which are [1,1,0,0], [1,0,1,0], and [1,0,0,1] respectively. The logistics delivery timeliness rate targets in the above three scenarios all need to be considered.

[0112] Step 202: Collect all data required for workshop production scheduling; The specific data collected include monthly production plan, weekly supply demand plan, machine capacity information, machine grouping information, brand-machine correspondence, machine monthly maintenance plan, average time for machine replacement, workshop work calendar, raw material production batch requirements, process constraints, monthly supply plan for raw and auxiliary materials, initial supply inventory, initial machine start-up status, and initial shortage of each brand.

[0113] Step 203: Building a production workshop scheduling optimization model; Specifically, in step one, the objective function of the production scheduling optimization model is constructed, and the specific parameter explanations are as described in the above embodiment.

[0114]

[0115] Step 2: Construct the constraints of the production scheduling optimization model. The specific parameters are explained as described in the above embodiment.

[0116]

[0117] Step 204, first stage optimization: simplify the model and obtain an initial solution for the production scheduling plan; Specifically include: Step 1: Temporarily ignore nonlinearity, partial integer constraints and nonlinear optimization objectives, and relax partial integer constraints.

[0118] Step 2: Re-adjust the objective function to only consider the on-time delivery rate and establish a simplified optimization model for the first stage, as shown below: (1) Objective function:

[0119] (2) Constraints:

[0120] Step 3: Set the optimization time to 300s. If the program running time exceeds the set time, output the result; otherwise, go to the next step to continue the calculation.

[0121] Step 4: Based on the simplified model, write a program and use mature solving software to solve the first-stage optimization model to obtain the initial solution of the production scheduling plan.

[0122] Step 205, second stage optimization: scene individuality optimization and initial solution constraint repair; Step 1: Set the second-stage optimization goals for different scenarios. The specific optimization goal array is , , , which correspond to the scenario with the least number of brand changes, the least number of working days, and the highest production balance under the premise of ensuring the timely delivery rate of logistics; Step 2: Optimize the order of unit brands according to the content of the invention; Step 3: Repair the production batch constraints according to the invention content; Step 4: According to the content of the invention, merge the production tasks to continuously optimize the indicators; Step 5: Optimize the overall balance again according to the content of the invention; Step 6: Then write a program to implement the above steps, and determine whether the program running time reaches the set time. If it does not reach the set time, go to the next step according to the invention content to continue running, and record the optimization plan output by the current system; if the program running time reaches the set time, go to the next step to output the plan; Step 206: Output, evaluation and selection of solutions; Through the above steps, a production schedule is calculated using actual monthly data of a cigarette factory and compared with the manual production schedule. Table 2 compares the model production schedule proposed in an embodiment of the present invention with the manual production schedule indicators. The comparison results are shown in Table 2. Scheme 1 is manual production scheduling, and Schemes 2, 3, and 4 are model production scheduling of the present invention respectively.

[0123] Table 2

[0124] From the results in Table 2, it can be seen that the evaluation index values ​​of each scheme in each dimension show that the production scheduling scheme index values ​​of the present invention are basically better than manual production scheduling. In addition, it can also be seen from the final scheme score that the model production scheduling has a great advantage over the existing manual production scheduling.

[0125] The production workshop scheduling method for process manufacturing provided by the embodiment of the present invention belongs to the field of enterprise production scheduling methods. The method adopts a two-stage scheduling plan. The first stage obtains a high-quality model initial solution by simplifying the model and speeds up the model solving speed. The second stage repairs the complex constraints of the model through heuristic rules and realizes personalized optimization for different scheduling scenarios, which meets the needs of process manufacturing enterprises for intelligent scheduling methods in the digital transformation. At present, this technical method has been actually used in enterprises, helping enterprises to obtain considerable economic benefits and efficiency improvements. Compared with the existing technology, this method can ensure the quality of the scheduling plan while speeding up the scheduling speed, can obtain personalized optimization plans for a variety of scheduling scenarios, can easily handle scheduling problems when facing complex scheduling scenarios, help enterprises improve the level of workshop scheduling management and support enterprises to achieve intelligent transformation of workshops.

[0126] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor 310 , a communications interface 320 , a memory 330 and a communication bus 340 , wherein the processor 310 , the communications interface 320 , and the memory 330 communicate with each other via the communication bus 340 . The processor 310 can call the logic instructions in the memory 330 to execute a production workshop scheduling method for process manufacturing, which includes: S1. Setting an evaluation index system, evaluation method, optimization goal and scheduling scenario of a scheduling plan; S2. Reading the full data of the production workshop to be scheduled, and determining the actual scheduling constraints based on the full data; S3. Constructing a production workshop scheduling optimization model based on the scheduling optimization goals and the actual scheduling constraints; S4. Simplifying and solving the production workshop scheduling optimization model to obtain an initial solution to the scheduling plan; S5. Based on the initial solution to the scheduling plan, performing personalized solution on the production workshop scheduling optimization model according to the scheduling scenario to obtain a personalized solution result; S6. Repeating S4-S5, recording the personalized solution results until the preset optimization time is reached, and outputting the optimal solution of each personalized solution result as the optimal scheduling plan.

[0127] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0128] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the production workshop scheduling method for process manufacturing provided by the above methods, which method includes: S1, setting an evaluation index system, evaluation method, optimization goal and scheduling scenario for the scheduling plan; S2, reading the full data of the production workshop to be scheduled, and determining the actual scheduling constraints based on the full data; S3, constructing a production workshop scheduling optimization model based on the scheduling optimization goal and the actual scheduling constraints; S4, simplifying and solving the production workshop scheduling optimization model to obtain an initial solution of the scheduling plan; S5, based on the initial solution of the scheduling plan, performing personalized solution on the production workshop scheduling optimization model according to the scheduling scenario to obtain a personalized solution result; S6, repeating S4-S5, recording the personalized solution results obtained, until the preset optimization time is reached, and outputting the optimal solution of each personalized solution result as the optimal scheduling plan.

[0129] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the production workshop scheduling method for process manufacturing provided by the above-mentioned methods, the method comprising: S1, setting an evaluation index system, evaluation method, optimization target and scheduling scenario of a scheduling plan; S2, reading the full data of the production workshop to be scheduled, and determining the actual scheduling constraints based on the full data; S3, constructing a production workshop scheduling optimization model based on the scheduling optimization target and the actual scheduling constraints; S4, simplifying and solving the production workshop scheduling optimization model to obtain an initial solution of the scheduling plan; S5, based on the initial solution of the scheduling plan, performing personalized solution on the production workshop scheduling optimization model according to the scheduling scenario to obtain a personalized solution result; S6, repeating S4-S5, recording the personalized solution results obtained, until the preset optimization time is reached, and outputting the optimal solution of each personalized solution result as the optimal scheduling plan.

[0130] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0131] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A production workshop scheduling method for process manufacturing industry, characterized in that: include: S1. Set the evaluation index system, evaluation method, optimization target and production scheduling scenario of the production scheduling plan; S2. Reading the full data of the production workshop to be scheduled, and determining the actual production scheduling constraints based on the full data; S3. Building a production workshop production scheduling optimization model based on the production scheduling optimization target and the actual production scheduling constraints; S4, simplifying and solving the production workshop scheduling optimization model to obtain an initial solution for the production scheduling plan; S5. Based on the initial solution of the production scheduling plan, the production workshop production scheduling optimization model is personalized solved according to the production scheduling scenario to obtain a personalized solution result; S6. Repeat S4-S5, record the obtained personalized solution results, until the preset optimization time is reached, and output the optimal solution of each personalized solution result as the optimal production scheduling plan.

2. The production workshop scheduling method for process manufacturing industry according to claim 1 is characterized in that: The evaluation index system includes: supply management index, production control management index, production continuity management index, production efficiency management index and production manpower management index.

3. The production workshop scheduling method for process manufacturing industry according to claim 1 is characterized in that: The full data of the production workshop to be scheduled includes: monthly production plan, weekly supply demand plan, machine capacity information, machine grouping information, brand-machine correspondence, machine monthly maintenance plan, average time for machine replacement, workshop work calendar, raw material production batch requirements, process constraints, raw and auxiliary materials monthly supply plan, initial supply inventory, initial machine start-up status, and initial brand shortage.

4. The production workshop scheduling method for process manufacturing industry according to claim 1 is characterized in that: The optimization objectives include logistics delivery timeliness, brand change times, number of production working days, and machine production balance; The logistics delivery timeliness rate is characterized by the out-of-stock cost of each cycle, and the calculation formula of the out-of-stock cost of each cycle is: in, represents the unit out-of-stock penalty cost, Indicates production cycle t The out-of-stock quantity at Represents the set of all brands, Represents the set of all production cycles; The number of brand changes is represented by the production cost caused by the brand change. The calculation formula of the production cost caused by the brand change is: in, Represents the cost of a single card exchange, Representation cycle t Machine l Whether a card swap has occurred, if so , otherwise 0, Represents the set of all machines; The number of production working days is represented by the total start-up cost of the machine, and the calculation formula of the total start-up cost of the machine is: in, Indicates production cycle Machine The start-up cost increases with the cycle t increases linearly with the increase of Representation cycle Machine Produced brand J, It means it is not produced. Represents the set of production correspondences between brands and machines; The balance of machine production is characterized by the cost of production imbalance, and the calculation formula of the cost of production imbalance is: in, Indicates the production area i The production imbalance cost, represents the number of production areas, Indicates the production area i The total number of machines that can be started, Indicates the number of machines in the production cycle l The number of working cycles.

5. The production workshop scheduling method for process manufacturing industry according to claim 4 is characterized in that: The production scheduling scenarios include one or more of a plan with the least number of brand changes, a plan with the least number of working days, and a plan with the highest production balance; Among them, the scheme with the least number of card changes is to output the scheme with the least total number of card changes for all machines within the production scheduling cycle, under the premise of optimizing the logistics delivery timeliness rate; The plan with the least number of working days is based on the premise of optimizing the on-time delivery rate of logistics, and outputs the plan with the least number of working days in the production scheduling cycle; The highest production balance plan is based on the premise that the logistics delivery timeliness rate is optimized, and outputs the highest machine production balance plan within the production scheduling cycle.

6. The production workshop scheduling method for process manufacturing industry according to claim 5 is characterized in that: S3. Based on the production scheduling optimization target and the actual production scheduling constraints, a production workshop production scheduling optimization model is constructed, which specifically includes: Based on the production scheduling optimization target and the actual production scheduling constraints, a production workshop scheduling optimization model is constructed with the goal of minimizing the total scheduling cost, including: The first objective function: The first constraint is: Constraint 1: Production and sales balance constraint: in, Representation cycle t Machine l Production brand j the number of Representation cycle t brand j Out of stock quantity; Representation cycle t brand j Inventory volume; Representation cycle t brand j Quantity of demand; Constraint 2: Machine capacity constraints: in, Representation cycle t Machine l Production brand j time; Display machine l In cycle t Total available production time; is a 0-1 variable, Representation cycle t Machine l Production brand specifications j , otherwise the opposite is true; Constraint 3: Machine capacity and brand change constraints: in, Display machine l From the brand Switch to Brand The time consumed; It is a 0-1 variable. Time represents cycle t Machine l From the brand Switch to Brand , 0 means no switching; Represents the possible card exchange combinations of all machines; Constraint 4: Machine startup status: Constraint 5: Determine whether the machine changes cards: in, is a 0-1 variable, Representation cycle t Machine l A card swap was made, otherwise the opposite was true; Constraint 6: Machine brand change judgment: Constraint 7: Constraints on the relationship between production time and output: in, Display machine l Production brand j production capacity; Constraint 8: Constraints on supply and consumption of raw and auxiliary materials: in, Indicates raw materials i In cycle t Inventory volume; Indicates the brand of the unit j Consumption of raw materials i the number of Representation cycle t Raw materials i the supply of Indicates all brands j Required raw materials i A collection of; Constraint 9: Machine grouping constraints: in, It represents a collection of machine groups, requiring that machines in the same group can only produce the same brand and specifications in the same cycle; Constraint 10: Constraint on the number of work shifts: in, Display machine l The number of production cycles within the planning period; Constraint 11: Production batch quantity constraint: in, Indicates the minimum production batch of brand specification j; N is an integer value; Constraint 12, non-negative and integer constraints: 。 7. The production workshop scheduling method for process manufacturing industry according to claim 6 is characterized in that: S4. Simplify and solve the production workshop scheduling optimization model to obtain an initial solution for the scheduling plan, which specifically includes: S41, removing the nonlinear constraint condition in the actual production scheduling constraint condition, the first integer constraint condition and the nonlinear optimization target in the optimization target, and relaxing the second integer constraint; wherein the first integer constraint is constraint condition 6; and the second integer constraint is constraint condition 11; S42, adjusting the first objective function, setting the logistics delivery timeliness rate as the first stage optimization target, and establishing a simplified model of the production workshop scheduling optimization model, including: The second objective function: Second constraint: ； S43. Solve the simplified model to obtain an initial solution for the production scheduling plan.

8. The production workshop scheduling method for process manufacturing industry according to claim 7 is characterized in that: S5. Based on the initial solution of the production scheduling plan, the production workshop production scheduling optimization model is solved in a personalized manner according to the production scheduling scenario to obtain a personalized solution result, which specifically includes: S51. Set the second-stage optimization goals for different production scheduling scenarios; S52. Optimize the production sequence of brands on the unit to improve the timeliness of logistics delivery and reduce the time consumed by brand replacement; S53, repairing the integer constraint of the production batch quantity, so that the infeasible initial solution of the production scheduling plan becomes a feasible solution; S54, merging production tasks and optimizing the second stage optimization target; S55. Optimize overall balance.

9. The production workshop scheduling method for process manufacturing industry according to claim 8, characterized in that: S51. Set the second-stage optimization goals for different production scheduling scenarios, including: Establish a production scheduling optimization target array, defined as: [A, B, C, D], where AD are all 0-1 variables, where A represents the logistics delivery timeliness rate, A=1 represents the highest logistics delivery timeliness rate, and A=0 represents no requirement for this target; B represents the number of card changes, B=1 represents the minimum number of card changes, and B=0 represents no requirement for this target; C represents the number of working days, C=1 represents the minimum number of working days, and C=0 represents no requirement for this target; D represents balance, D=1 represents the highest balance, and D=0 represents no requirement for this target; Determine the target production schedule to be acquired based on business needs; According to the target production scheduling plan, A, B, C, and D in the production scheduling optimization target array [A, B, C, D] are set to 0 or 1 to set the second-stage optimization target.

10. The production workshop scheduling method for process manufacturing industry according to claim 8, characterized in that: S52. Optimize the production sequence of brands on the unit to improve the timeliness of logistics delivery and reduce the time consumed by brand replacement, including: Traverse the sequence combinations of brands to be produced in the same unit, and determine the sequence combination of brands that maximizes the logistics delivery timeliness rate or minimizes the total brand change consumption time as the production order of the brands on the unit.

11. The production workshop scheduling method for process manufacturing industry according to claim 8, characterized in that: S53, repairing the integer constraint of the production batch quantity to make the infeasible initial solution of the production scheduling plan become a feasible solution, specifically including: Set the initial solution of the production scheduling plan g Production brand j The number of , according to the preset minimum batch requirements, increase the corresponding production volume to meet the minimum batch of the unit: ， in, It is a brand j Minimum batch production volume, It's the crew g Production brand j The minimum number of batches.

12. The production workshop scheduling method for process manufacturing industry according to claim 8, characterized in that: S54, merging production tasks and optimizing the second stage optimization objectives, specifically including: For any brand j , traverse all units producing this brand in pairs. If there are multiple units in the pairwise traversal process , , …, , moving the production of different units; If you optimize the target array during the move , if there is an indicator corresponding to 1 that rises, and other indicators that are 1 do not decrease, the move is executed; otherwise, the move is not executed.

13. The production workshop scheduling method for process manufacturing industry according to claim 8, characterized in that: S55. Optimize overall balance, including: Under the premise of not exceeding the upper limit of safety stock and not violating the batch quantity, increase the output of some units with lower production volume to optimize the overall balance of the production scheduling plan.

14. The production workshop scheduling method for process manufacturing industry according to any one of claims 1 to 13, characterized in that: S4-S5 are repeated, and the obtained personalized solution results are recorded until the preset optimization time is reached, and the optimal solution of each personalized solution result is output as the optimal production scheduling plan, which specifically includes: Determine whether the current running time of each step exceeds the preset optimization time; If the preset optimization time is not exceeded, the current personalized solution results are recorded, and the simplified solution and personalized solution are repeated; If the preset optimization time is exceeded, the personalized solution results in all records are output; Each of the recorded personalized solution results is evaluated using the evaluation index system and the evaluation method, and the best personalized solution result is output as the best production scheduling plan.

Images