Shop scheduling method and system for large-sized complex product, and device and medium

Abstract

The present invention relates to the technical field of job shop scheduling, and particularly relates to a shop scheduling method and system for a large-sized complex product, and a device and a medium. The method comprises: firstly, determining a scheduling decision problem of assemble, adjusting and debugging shops of a large-sized complex product, and a precondition hypothesis, so as to obtain a maximum workpiece completion time; then, taking the maximum workpiece completion time of the large-sized complex product being as small as possible as an optimization objective, and establishing an assemble, adjusting and debugging shop scheduling optimization model of the large-sized complex product; and finally, using a multi-objective genetic algorithm to perform optimization solving, so as to obtain a target scheduling decision and a target completion time of the assemble, adjusting and debugging shops of the large-sized complex product. Therefore, an assemble, adjusting and debugging process highly matching an actual production process is ensured, and the precision and convergence speed of model solving are improved.

Description

A method, system, equipment and medium for scheduling large and complex product workshops Technical Field

[0001] The present invention relates to the technical field of job shop scheduling, and in particular to a method, system, equipment and medium for scheduling a large-scale complex product shop. Background Art

[0002] In the production process of complex products, the main method of connecting components is assembly, and system debugging after assembly is an indispensable processing link, which accounts for a high proportion of working hours. It can be seen that improving the organizational and operational capabilities of the assembly and debugging workshop is the key to ensuring product quality and production efficiency and improving the overall economic benefits of the enterprise. Therefore, it is of great significance to large-scale complex product manufacturing companies.

[0003] At present, few scholars have studied the multi-resource constrained workshop problem that takes into account both assembly and debugging equipment and tool resources and assembly and debugging workers. However, the scheduling problem studied is only applicable to simple flow workshops, resulting in a large difference between the established mathematical model and the actual model. If traditional scheduling algorithms are still used to handle the scheduling of large and complex products with assembly process structures, the inherent parallel relationship of the process tree in the manufacturing process will inevitably be split, resulting in an extension of the production cycle.

[0004] In the assembly production of large, complex products, each process requires corresponding equipment and tools due to the bulk of the structural components. Furthermore, during assembly and system debugging, workers with appropriate skills must be dispatched to the corresponding workstations around the objects or equipment being manipulated. Furthermore, workshop teams have a limited number of workers. Failure to consider personnel resource constraints will inevitably lead to production scheduling plans that do not align with actual production capacity, rendering the plan infeasible. Due to the complexity of the problem, few research results have been reported. Therefore, research on workshop scheduling for assembly and debugging of large, complex products is of great significance.

[0005] Summary of the Invention

[0006] In response to the problems of insufficient solution accuracy and convergence speed in existing large-scale complex workpiece scheduling methods, the present invention proposes a large-scale complex product workshop scheduling method, system, equipment and medium. The method first determines the scheduling decision problem and premise assumptions of the large-scale complex product assembly and debugging workshop to obtain the maximum completion time of the workpiece, and then takes minimizing the maximum completion time of the large-scale complex product workpiece as the optimization goal to establish a large-scale complex product assembly and debugging workshop scheduling optimization model; finally, a multi-objective genetic algorithm is used to optimize and solve, and the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop are obtained, which ensures a high degree of matching between the assembly and debugging process and the actual production process, and improves the accuracy and convergence speed of the model solution.

[0007] The specific implementation contents of the present invention are as follows:

[0008] A scheduling method for a large-scale complex product workshop comprises the following steps: firstly, determining the scheduling decision problem and premise assumptions of a large-scale complex product assembly and debugging workshop to obtain the maximum completion time of a workpiece; then, taking minimizing the maximum completion time of a large-scale complex product workpiece as the optimization goal, establishing a scheduling optimization model for the large-scale complex product assembly and debugging workshop; finally, adopting a multi-objective genetic algorithm to optimize and solve the scheduling optimization model for the large-scale complex product assembly and debugging workshop to obtain a target scheduling decision and a target completion time for the large-scale complex product assembly and debugging workshop.

[0009] In order to better implement the present invention, the large-scale complex product workshop scheduling method further specifically includes the following steps:

[0010] Step 1: Determine the scheduling decision problem and assumptions for the assembly and commissioning workshop of large and complex products, and obtain the maximum completion time of the workpiece;

[0011] Step 2: Based on the maximum completion time of the workpiece, with minimizing the maximum completion time of the workpiece and minimizing the total delay of the task as the optimization goal, set the priority constraints between the processes of large and complex products, the assembly and debugging equipment and tool resource constraints, and the assembly and debugging worker resource constraints, and establish a large and complex product assembly and debugging workshop scheduling optimization model;

[0012] Step 3: Using heuristic rules to improve the initialization operation of the multi-objective genetic algorithm to obtain an improved multi-objective genetic algorithm;

[0013] Step 4: Use an improved multi-objective genetic algorithm to optimize and solve the scheduling optimization model of the large-scale complex product assembly and debugging workshop to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

[0014] In order to better implement the present invention, further, the step 1 specifically includes the following steps:

[0015] Step 11: Determine the scheduling decision problem and assumptions for large-scale complex product assembly and commissioning workshops;

[0016] Step 12: When the processes of large and complex products meet the priority constraints, determine the assembly order of the workpieces, select the tools or equipment for the current process during the scheduling process, and allocate the corresponding number of workers to obtain the longest completion time for the workpiece.

[0017] In order to better implement the present invention, further, the step 2 specifically includes the following steps:

[0018] Step 21: Obtaining a maximum completion time objective function of the workpiece according to the maximum completion time of the workpiece;

[0019] Step 22: Obtaining a total task delay objective function based on the longest completion time of the workpiece, the delivery period of the workpiece, and the delay coefficient of the workpiece;

[0020] Step 23: Based on the objective function of the longest completion time of the workpiece and the objective function of the total delay of the task, set the priority constraints between the processes of large and complex products, the assembly and debugging equipment and tool resource constraints, and the assembly and debugging worker resource constraints, and establish a scheduling optimization model for the assembly and debugging workshop of large and complex products.

[0021] In order to better implement the present invention, further, the specific operation of step 3 is: using multiple initialization methods to improve the initialization operation of the multi-objective genetic algorithm, and the multiple initialization methods include a random initialization method, an initialization method according to the priority of the delivery period rule, and an initialization method according to the priority of the remaining processing time.

[0022] In order to better implement the present invention, further, the step 4 specifically includes the following operations:

[0023] Step 41: Create a workpiece process structure tree;

[0024] Step 42: Expand the workpiece process structure tree to obtain workpiece process structure tree information;

[0025] Step 43: Perform chromosome encoding based on the workpiece process structure tree information to obtain a legal gene structure that satisfies the constraints of the preceding and following processing sequences between workpieces in the assembly and debugging process;

[0026] Step 44: Decode the legal gene structure to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

[0027] In order to better implement the present invention, further, step 44 specifically includes the following steps:

[0028] Step 441: Obtain a scheduling task and traverse the artifacts in the chromosome sorting according to the legal gene structure;

[0029] Step 442: Retrieve the workpiece type according to the workpiece number;

[0030] Step 443: Read the process of the workpiece, and obtain the platform information of the process according to the process;

[0031] Step 444: Select a corresponding tool according to the workpiece type, determine the remaining usage time of the tool, determine whether the number of workers meets the set conditions based on the remaining usage time, and calculate the current completion time;

[0032] Step 445: Determine whether all processes have been traversed. If not, return to step 443. If all processes have been traversed, determine whether all workpieces have been traversed. If not, return to step 441. If all workpieces have been traversed, use the current completion time as the target completion time and output the target scheduling decision for the large-scale complex product assembly and debugging workshop.

[0033] Based on the above-mentioned large-scale complex product workshop scheduling method, in order to better implement the present invention, a large-scale complex product workshop scheduling system is further proposed, including an initialization unit, a model building unit, and a calculation unit;

[0034] The initialization unit is used to determine the scheduling decision problem and premise assumptions of the large-scale complex product assembly and debugging workshop to obtain the maximum completion time of the workpiece;

[0035] The model building unit is used to establish a scheduling optimization model for large-scale complex product assembly and debugging workshops by taking minimization of the longest completion time of large-scale complex product workpieces as the optimization goal;

[0036] The computing unit is used to optimize and solve the scheduling optimization model of the large-scale complex product assembly and debugging workshop by using a multi-objective genetic algorithm to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

[0037] Based on the above-mentioned large-scale complex product workshop scheduling method, in order to better realize the present invention, further, an electronic device is proposed, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned large-scale complex product workshop scheduling method is realized.

[0038] Based on the above-mentioned large-scale complex product workshop scheduling method, in order to better realize the present invention, a computer-readable storage medium is further proposed, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned large-scale complex product workshop scheduling method is realized.

[0039] The present invention has the following beneficial effects:

[0040] (1) The present invention adopts a combination of multiple initialization methods to improve the initialization operation of the multi-objective genetic algorithm. The resulting multi-objective genetic algorithm has higher solution accuracy and can converge to the approximate optimal solution faster and more stably within a limited search time, thereby accelerating the speed of model convergence.

[0041] (2) The present invention comprehensively considers the resource upper limit of assembly workers and debugging workers, solves the model through a multi-objective genetic algorithm to obtain the target maximum completion time and the total order delay, thereby shortening the production cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG1 is a schematic diagram of a multi-objective genetic algorithm flow chart provided by an embodiment of the present invention.

[0043] FIG2 is a schematic diagram of a process structure tree provided by an embodiment of the present invention.

[0044] FIG3 is a schematic diagram of a process tree expansion structure according to an embodiment of the present invention.

[0045] FIG4 is a schematic block diagram of an example encoding structure provided by an embodiment of the present invention.

[0046] FIG5 is a schematic diagram of a decoding process provided by an embodiment of the present invention.

[0047] FIG6 is a schematic block diagram of a tool / equipment selection coding structure for a component corresponding to a process according to an embodiment of the present invention.

[0048] FIG7 is a schematic diagram of the information structure of the process tree of a production task product provided by an embodiment of the present invention.

[0049] FIG8 is a schematic diagram of the code structure of parts of a production task product provided by an embodiment of the present invention.

[0050] FIG9 is a schematic diagram of iteration curves of different algorithms for solving assembly debugging scheduling examples provided by an embodiment of the present invention.

[0051] FIG10 is a schematic block diagram of the process of a large-scale complex product workshop scheduling method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.

[0053] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; direct connections, indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0054] This patent relates to the field of assembly and commissioning workshop optimization technology, specifically a method for optimizing assembly and commissioning scheduling for large, complex products that requires considering both the constraints of assembly and commissioning equipment and tool resources and worker resources. This includes both mathematical modeling for workshop scheduling and the specific process of solving this mathematical model using a multi-objective genetic algorithm.

[0055] The English full names of the professional terms involved in the embodiments of the present invention are as follows:

[0056] Multi-Objective Genetic Algorithm: Multi-Objective Genetic Algorithm, MOGA.

[0057] Assembly shop scheduling: Assemble and Adjusting Shop Scheduling Problem, AASSP.

[0058] Example 1:

[0059] This embodiment proposes a scheduling method for a large-scale complex product workshop, as shown in Figure 10. First, the scheduling decision problem and premise assumptions of the large-scale complex product assembly and debugging workshop are determined to obtain the maximum completion time of the workpiece. Then, minimizing the maximum completion time of the large-scale complex product workpiece is used as the optimization goal, and a scheduling optimization model for the large-scale complex product assembly and debugging workshop is established. Finally, a multi-objective genetic algorithm is used to optimize and solve the large-scale complex product assembly and debugging workshop scheduling optimization model to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

[0060] The large-scale complex product workshop scheduling method specifically includes the following steps.

[0061] Step 1: Determine the scheduling decision problem and assumptions for the assembly and commissioning workshop of large and complex products, and obtain the maximum completion time of the workpiece.

[0062] The step 1 specifically includes the following steps:

[0063] Step 11: Determine the scheduling decision problem and assumptions for large-scale complex product assembly and commissioning workshops;

[0064] Step 12: When the processes of large and complex products meet the priority constraints, determine the assembly order of the workpieces, select the tools or equipment for the current process during the scheduling process, and allocate the corresponding number of workers to obtain the longest completion time for the workpiece.

[0065] Step 2: Based on the maximum completion time of the workpiece, with minimizing the maximum completion time of the workpiece and minimizing the total delay of the task as the optimization goal, set the priority constraints between the processes of large and complex products, assembly and debugging equipment and tool resource constraints, and assembly and debugging worker resource constraints, and establish a scheduling optimization model for the assembly and debugging workshop of large and complex products.

[0066] The step 2 specifically includes the following steps:

[0067] Step 21: Obtaining a maximum completion time objective function of the workpiece according to the maximum completion time of the workpiece;

[0068] Step 22: Obtaining a total task delay objective function based on the longest completion time of the workpiece, the delivery period of the workpiece, and the delay coefficient of the workpiece;

[0069] Step 23: Based on the objective function of the longest completion time of the workpiece and the objective function of the total delay of the task, set the priority constraints between the processes of large and complex products, the assembly and debugging equipment and tool resource constraints, and the assembly and debugging worker resource constraints, and establish a scheduling optimization model for the assembly and debugging workshop of large and complex products.

[0070] Step 3: Use heuristic rules to improve the initialization operation of the multi-objective genetic algorithm to obtain an improved multi-objective genetic algorithm.

[0071] The specific operation of step 3 is: using multiple initialization methods to improve the initialization operation of the multi-objective genetic algorithm, and the multiple initialization methods include random initialization, initialization according to delivery period priority, and initialization according to remaining processing time priority.

[0072] Step 4: Use an improved multi-objective genetic algorithm to optimize and solve the scheduling optimization model of the large-scale complex product assembly and debugging workshop to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

[0073] The step 4 specifically includes the following operations:

[0074] Step 41: Create a workpiece process structure tree;

[0075] Step 42: Expand the workpiece process structure tree to obtain workpiece process structure tree information;

[0076] Step 43: Perform chromosome encoding based on the workpiece process structure tree information to obtain a legal gene structure that satisfies the constraints of the preceding and following processing sequences between workpieces in the assembly and debugging process;

[0077] Step 44: Decode the legal gene structure to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

[0078] The step 44 specifically includes the following steps:

[0079] Step 441: Obtain a scheduling task and traverse the artifacts in the chromosome sorting according to the legal gene structure;

[0080] Step 442: Retrieve the workpiece type according to the workpiece number;

[0081] Step 443: Read the process of the workpiece, and obtain the platform information of the process according to the process;

[0082] Step 444: Select a corresponding tool according to the workpiece type, determine the remaining usage time of the tool, determine whether the number of workers meets the set conditions based on the remaining usage time, and calculate the current completion time;

[0083] Step 445: Determine whether all processes have been traversed. If not, return to step 443. If all processes have been traversed, determine whether all workpieces have been traversed. If not, return to step 441. If all workpieces have been traversed, use the current completion time as the target completion time and output the target scheduling decision for the large-scale complex product assembly and debugging workshop.

[0084] Working Principle: This embodiment first transforms the problem of scheduling decisions for the assembly and debugging of large and complex products into a combinatorial optimization mathematical model problem. Secondly, it takes the minimum maximum completion time of complex products as the optimization goal, and considers the priority constraints between the processes of large and complex products, assembly and debugging equipment and tool resources, assembly and debugging worker resources and other constraints to construct a scheduling decision model for the assembly and debugging workshop of large and complex products. Finally, a multi-objective genetic algorithm is used for solution. During the solution process, an allocation scheduling algorithm is designed to meet the requirements of the assembly and debugging scheduling problem for worker resources, ensuring that the assembly and debugging process can be highly matched with the actual production process. The scheduling solution obtained by this embodiment can effectively solve the scheduling problem of assembly and debugging of large and complex products with multiple resource requirements.

[0085] Example 2:

[0086] Based on the above embodiment 1, this embodiment, as shown in Figures 1, 2, 3, 4, 5, and 6, describes in detail the specific operations of steps 1 to 4 using a specific embodiment.

[0087] Step 1: Determine the description and related assumptions of the assembly debugging scheduling decision problem.

[0088] The Assembly Shop Scheduling Problem (AASSP) is a multi-resource scheduling problem that considers assembly equipment, tools, and workers during the assembly and commissioning process of large, complex products. The assembly process for large, complex products first requires a process structure with a prioritized order between each process step. The scheduling process then requires selecting the appropriate assembly equipment and tools for each step. Furthermore, the required assembly workers are scheduled based on process information, ultimately completing resource scheduling for all steps in the large, complex product.

[0089] The premise assumptions for the assembly shop scheduling problem AASSP include:

[0090] (1) Each equipment tool resource can only allow one process to be assembled or debugged at a time.

[0091] (2) During the operation, the process will not be interrupted unless quality problems occur.

[0092] (3) The number of each type of worker resource in the workshop is limited. When meeting the requirements of the process for equipment and tool resources, it is also necessary to consider whether there are enough workers on that day. If the conditions for starting work are not met, the start of work will be delayed.

[0093] (4) Workers who have been assigned to a certain process cannot be assigned to other processes before the process is completed, so as to avoid interruption of the process.

[0094] (5) Logistics time is negligible. Because transportation time is relatively small compared to operation time and actual workshops usually have sufficient transportation capacity, transportation time is negligible.

[0095] (6) The priority between operations is determined according to the process tree, that is, all processes of the subsequent operation must wait until all processes of the predecessor operation are completed before they can begin.

[0096] (7) All tools / equipment and workers are available for occupation and dispatch at time zero.

[0097] The Assembly Shop Scheduling Problem (AASSP) is described as follows: In a shop, there are m types of assembly equipment / tools, k types of workers with different skills, and n large, complex workpieces to be assembled. The workpieces have a process hierarchy based on the BOM, with precedence and priority relationships between them. Each workpiece may have a different number of steps, all of which are known, and at least one tool / equipment can be selected for each step. Considering that the steps of a large, complex product meet priority constraints, a reasonable assembly sequence is determined for all workpieces. During the scheduling process, an appropriate tool / equipment is selected for each step, and a reasonable number of workers are allocated to minimize the maximum completion time for all workpieces.

[0098] J represents the set of workpieces {1, 2, 3…, j}, where j represents the index; Inf represents a maximum value; i∈O j Indicates that workpiece j contains process i; Indicates that the tool / equipment M is used in the i-th process of workpiece j p Processing; M i,j represents the set of optional tools / equipment for the i-th process of workpiece j; represents the start time of the i-th process of workpiece j using tool / equipment p; It is expressed as the completion time of the i-th process of workpiece j using tool / equipment p; t j,i F represents the processing time required for the i-th process of workpiece j; j represents the set of predecessors of workpiece j {1, 2, 3…, j'}, where j' represents the index of the predecessor; i'∈O j’ Indicates that the predecessor job j' contains process i'; represents the number of workers of type k available at time a, where a represents the time index; H k Indicates the upper limit of the k-th resource; The number of workers of type k occupied by the i-th process of workpiece j at time a (hour); Q i,j represents the set of predecessor processes {1, 2, 3…, i'} of the i-th process of workpiece j; Decision variables, process O of workpiece j j,i Select Tool / Equipment M p For processing, otherwise Decision variables, if the process O of workpiece j j,i When using tools / equipment M p The processing sequence of workpiece j' is better than process O j’,i’ Use tools / equipment M p If the processing is done early, otherwise

[0099] Step 2: Establish a mathematical model for assembly workshop scheduling optimization.

[0100] Objective function: Minimize the maximum completion time and the total delay of the task. min f1=max{C j |1≤j≤n} (1-1)

[0101] In formula (1-1): f1 is the maximum completion time of the product to be manufactured, C j represents the completion time of job j.

[0102] In formula (1-2), f2 is the total delay of the task, d j represents the delivery time of component j, w j represents the delay coefficient of component j, C j represents the completion time of job j.

[0103] The constraints are as follows:

[0104] In formula (3), It is expressed as the completion time of the i-th process of workpiece j on platform p; It represents the start time of the i-th process of workpiece j on platform p.

[0105] In formula (5), t j,i represents the processing time of the i-th process of workpiece j.

[0106] Formula (6) C j，i′ is the completion time of the previous process of the i-th process of workpiece j. S j，i , C j，i ≥0, j∈J, i∈O j (10)

[0107] Among them: constraint (2) means that each process of a workpiece can only occupy one tool or equipment; constraints (3) and constraints (4) mean that the start time of a process occupying a tool / equipment is earlier than or equal to the completion time of the process using the equipment; constraint (5) means that the start time of a workpiece must be later than or equal to the completion time of all processes of all its predecessor workpieces; constraint (6) means that the completion time of all previous processes of the same workpiece is earlier than or equal to the start time of the next process; constraints (7) and constraints (8) mean that when processes of different workpieces can occupy the same tool / equipment, they cannot work at the same time, that is, at any time, no more than one workpiece can be used by the same tool / equipment; constraint (9) means that the number of calls for each type of worker at any time cannot exceed the upper limit of the number of workers of that type in the actual workshop at the current time; constraints (10) to constraints (12) mean that the decision variables are 0-1 variables.

[0108] Step 3: Improve the multi-objective genetic algorithm.

[0109] Population initialization is a crucial step in solving the assembly shop integrated scheduling problem using a genetic algorithm. It not only determines the quality of the population generated during the initialization phase but also significantly impacts the algorithm's search efficiency and convergence speed in the later stages. Given that the objective function of the mathematical model of the present invention includes the maximum completion time and the total amount of task delays, and the complexity of the solution space for multi-objective search increases the difficulty of the algorithm search, it is necessary to improve the initialization operation of the genetic algorithm to achieve higher efficiency and better results when solving multi-objective problems. The specific improvements are as follows:

[0110] In order to control the global upper bound of the genetic algorithm when solving the problem and make the initialized population as close to the global optimal solution as possible, so as to improve the efficiency of the algorithm in multi-objective search, the existing heuristic rules are used to improve the initialization operation of the multi-objective genetic algorithm. Three initialization operations are adopted, namely random initialization, priority initialization according to the delivery period rule, and priority obtained by subtracting the remaining processing time of the component production cycle from the delivery period.

[0111] Using the initial solution generated with delivery period priority as a reference, the gene individual X2 generated based on the delivery period priority sorting initialization rule and the gene individual X3 generated based on the remaining processing time priority sorting rule are more likely to be distributed near the solution space with the total task delay as the target, while the initial population generated by random initialization is scattered throughout the solution space without much regularity.

[0112] The solution generated by initializing with the latter two heuristic rules can provide a direction for some gene individuals to explode in the delivery priority, and the distance d2 and d3 between the gene individuals X2 and X3 obtained by rule initialization and the multi-objective approximate optimal solution including the maximum completion time and the total task delay may be shorter than d1, which can save the algorithm a lot of time to search for a high-quality solution that satisfies the minimum total task delay. In order to introduce rule initialization operations without affecting the diversity of the fireworks population, the three initialization methods set in this embodiment account for 40% of random initialization, 30% of initialization based on delivery priority sorting, and 30% of initialization based on remaining processing time priority. Based on the above initialization improvement scheme, the present invention proposes a multi-objective genetic algorithm, the process of which is shown in Figure 1.

[0113] Step 4: Solve by combining encoding and decoding methods.

[0114] (1) Coding design

[0115] Encoding and decoding are key issues that affect the performance of the algorithm when solving this special problem. Unlike ordinary workshop scheduling problems, the encoding of the scheduling decision problem of assembly and debugging needs to take into account the constraints of the previous and next processing sequences between workpieces in the assembly and debugging process. In order to generate a legal gene structure that meets this constraint, this embodiment designs an encoding method suitable for solving comprehensive scheduling problems based on a structural process tree related to the product BOM. Take the simple product process tree shown in Figure 2 as an example. The processing time of each workpiece in each process and the types of workers and their requirements are shown in Table 1. According to the information in Table 1, the process tree shown in Figure 2 is expanded to obtain the detailed process process tree information shown in Figure 3. The direction of the arrow in Figure 3 represents the order of the process. The pointed process needs to be scheduled after all the processes of the predecessor workpiece are completed. When there is no arrow input, it means that this process has no predecessor workpiece. For example, the first schedulable workpiece set is {J1, J2, J4, J5, J6, J8, J9, J10, J11, J12, J13, J14, J15, J16, J17, J18, J19, J20, J21, J22, J23, J24, J25, J26, J27, J28, J29, J30, J31, J4 10}, and its corresponding schedulable process set is {O 1,1 ,O 2,1 ,O 4,1 ,O 5,1 ,O 6,1 ,O 8,1 ,O 9,1 ,O 10,1}.

[0116] Table 1 Worker requirements and processing time for each component process

[0117] Based on the process tree shown in Figure 2, individual encoding can be performed according to the following steps to generate the legal gene structure shown in Figure 4:

[0118] Step S1: Enter the process tree and the corresponding tool / equipment and worker information, set the candidate component set Can, initialize the current dimension z = 1, and the total dimension is Z, that is, the total number of components;

[0119] Step S2: Scan all parts in the process tree, put the parts with in-degree 0 into the candidate parts set Can, and update the candidate parts set;

[0120] Step S3: Generate a random number in the candidate component set to determine the component j to be scheduled this time, and place the component j in the dimension z of the firework;

[0121] Step S4: Search the process tree for the successor component of the currently scheduled component, reduce the in-degree of the successor component by 1, and increase the current dimension z by 1;

[0122] Step S5: Determine whether all components have been scheduled, that is, whether the current dimension z exceeds the total dimension Z. If so, go to step S6; otherwise, go to step S2.

[0123] Step S6: Output individual fireworks.

[0124] After following the above process, the result shown in Figure 6 can be obtained. For example, for component J3 located at the seventh position of the chromosome, combined with Table 1, its process 1 uses tool / equipment M5 belonging to L3, which occupies 2 assembly workers; process 2 uses tool / equipment M5 belonging to L3 type, which occupies 4 debugging workers; process 3 uses tool / equipment M1 belonging to L1 type, which occupies 2 polishing workers.

[0125] After following the process shown in Figure 5, the results shown in Figure 5 can be obtained. For example, for component J3 located at the seventh position of the chromosome, combined with Table 1, its process 1 uses tools or equipment M5 belonging to L3, which occupies 2 assembly workers; process 2 uses tools / equipment M5 belonging to L3 type, which occupies 4 debugging workers; process 3 uses tools / equipment M1 belonging to L1 type, which occupies 2 polishing workers.

[0126] The rest of this embodiment is the same as that of the above-mentioned embodiment 1, and therefore will not be described in detail.

[0127] Example 3:

[0128] This embodiment is based on any one of the above embodiments 1-2. Taking a branch workshop of a large equipment manufacturing enterprise producing complex products as an example, the main tool / equipment information of the workshop is as follows: wire harness laying tool L1 = {M1, M2}, the quantity is 2; wire harness installation tool L2 = {M3, M4, M5, M6, M7, M8}; pipeline installation tool L3 = {M9, M10}; pipeline installation tool L4 = {M11, M12}; pipeline installation tool L5 = {M12, M13}; pipeline installation tool L6 = {M13, M14}; pipeline installation tool L7 = {M14, M15}; pipeline installation tool L8 = {M15, M16}; pipeline installation tool L9 = {M16, M17}; pipeline installation tool L1 = {M17, M18}; pipeline installation tool L1 = {M17, M19}; pipeline installation tool L2 = {M18, M19}; pipeline installation tool L3 = {M19, M11}; pipeline installation tool L4 = {M19, M11}; pipeline installation tool L5 = {M19, M11}; pipeline installation tool L6 = {M19, M11}; pipeline installation tool L7 = {M19, M11}; pipeline installation tool L8 = {M19, M11}; pipeline installation tool L8 = {M19, M11}; pipeline installation tool L9 = {M19, M11}; pipeline installation tool L1 10 ,M 11 ,M12 ,M 13 ,M 14}, the number is 6; conduction detection equipment L4 = {M 15}, the quantity is 1; airtightness detection equipment L5 = {M 16}, the quantity is 1; grinding tool L6 = {M 17 ,M 18 ,M 19 ,M 20}, the number is 4; fastening tool L7 = {M 21 ,M 22 ,M 23 ,M 24}, the quantity is 4; glue tool L8 = {M 25}, quantity is 1; ground resistance detection tool L9 = {M 26 ,M 27 ,M 28 ,M 29}, the quantity is 4. The detailed information of each tool / equipment type and the corresponding process steps are shown in Table 2.

[0129] Table 2 Tool / Equipment Details

[0130] As shown in Table 3, the workshop consists of three worker teams: debugging, assembly, polishing, and wiring. The assembly team has 6 workers, the welding team has 26 workers, the polishing team has 4 workers, and the wiring team has 2 workers. Each worker type can perform a specific process based on their skill set.

[0131] Table 3 Worker information

[0132] 1. Production task analysis

[0133] (1) Existing product process information

[0134] The actual production times and production cycles for each process are shown in Table 4. According to research, the original workshop scheduling plan was determined by schedulers based on past production experience and the product's production cycle and delivery date. This lacked overall control over the workshop's equipment, tool, and worker resources. This often resulted in overtime and uneven scheduling of equipment, tools, and workers to meet product delivery targets. Furthermore, the production process was plagued by disruptive factors such as urgent work orders, interruptions caused by quality issues during assembly and commissioning, and rework due to detected quality issues. These common dynamic disturbances in the workshop significantly complicated production planning decisions. Frequent disruptions resulted in a significant delay in the completion of tasks beyond the agreed delivery date, resulting in a loss of efficiency.

[0135] Table 4 Product component process assembly time (unit: hours)

[0136] Each process needs to occupy the corresponding equipment and tools, and the number of each equipment and tool is at least 1. As shown in Table 5, the equipment and tool type required for process 1 of product category 1 is L9. The optional equipment and tools of this type read from Table 5 are {M 26 ,M 27 ,M 28 ,M 29}.

[0137] Table 5 Product component process tool type requirements

[0138] The various types of worker resources in the workshop are limited. In the processing of some processes, it is necessary to consider the margin of these workers that can be dispatched on the same day. Generally, the type of worker required in the assembly process is H1, the type of worker required in the welding process is H2, the type of worker required in the grinding process is H3, and the type of worker required in the painting process is H4. The specific number of workers required for each type of product is shown in Table 6.

[0139] Table 6 Product component process worker requirements

[0140] Based on the above different types of product parts assembly requirements, in actual production, a certain task plan is represented by a process tree, and the process sequence constraints between parts are shown in Figure 7. The figure simplifies multiple batches of the same type of products. For example, the first rectangular box in the first row of the process tree from left to right, "wire harness 2×4", represents that the demand for this type of product is 4. The original planned total assembly cycle for this task was 160 hours. The parts of the process tree shown in Figure 7 are numbered, and the numbered result is shown in Figure 8, where J in the first rectangular box in the first row from left to right is 39 -J 42 The assembly process of the component type of wiring harness 2 includes {J 39 ,J 40 ,J 41 ,J 42 The first box J1 in the last row, from left to right, represents the assembly process for oxygen pipeline 1, including {J1}. The corresponding installation completion deadlines for all tasks are shown in Table 1. The corresponding installation completion deadline for J1 is the 50th hour of production.

[0141] Table 7 Delivery period information

[0142] 2. Problem solving and analysis

[0143] Analysis of the above workshop example reveals that in actual production scheduling, it is necessary to consider the upper limits of tool / equipment and worker resources, as well as the impact of complex process constraints, during the planning phase. This is the basis for the assembly and commissioning scheduling modeling and solution proposed in this paper. Therefore, applying the comprehensive scheduling problem model to this problem can solve the workshop's initial scheduling problem. This section uses the Python 3.7-based PyCharm software tool for simulation optimization and solution. The multi-objective genetic algorithm parameters are set as follows:

[0144] The initialization population parameters are set according to relevant experience: the number of fireworks population K is 50, the number of explosions IT is 100, and the Gaussian probability Pm is 0.25; the three initialization allocation schemes are random initialization (40%), initialization by delivery date (30%), and initialization by priority based on the difference between the delivery date and the production cycle (30%).

[0145] 3. Engineering example scheduling solution

[0146] First, the multi-objective genetic algorithm MOGA proposed in this embodiment is used to solve the initial scheduling problem model. Considering the constraints of the above-mentioned equipment and tool resources, worker resources and complex processes, 10 consecutive calculations are performed. The calculation results are shown in Table 8. In order to verify the effectiveness of the multi-objective genetic algorithm proposed in this invention in solving the problem, the three comparative algorithms proposed above, namely the genetic algorithm GA, the discrete fireworks algorithm DFWA, and the independent framework fireworks algorithm CoFFA, are used to solve this example respectively. The number of experiments is set to 10. Finally, the average value and optimal value of the 10 solution results of each algorithm are taken for comparison. The comparison results are shown in Table 8.

[0147] The comparison results in Table 8 show that the multi-objective genetic algorithm proposed in this invention can be used to solve the comprehensive workshop scheduling problem with equipment, tool resources, and worker resources constraints for complex product production. When compared with the workshop's original production cycle, it obtains an approximately optimal value for the maximum completion time, which is 9.9% earlier than the original delivery date. In comparison with other intelligent algorithms, the minimum and average values ​​obtained for the maximum completion time and total order delays as the targets are both better than those of several other algorithms. The experimental results of 10 consecutive solutions show that the completion time and the average value of total delays solved by MOGA are optimized by 1.29%, 1.43%, and 2.15%, respectively, compared with FA, GA, and CoFFA. This shows that the multi-objective genetic algorithm has higher solution accuracy.

[0148] The search iteration curves of the four algorithms for the optimal solution are shown in FIG9 . It can be seen that the multi-objective genetic algorithm of the present invention can converge to the approximate optimal solution faster and more stably within a limited search time.

[0149] Because the processing process requires consideration not only of equipment and tool availability but also of the resource limits of assembly and commissioning workers, when all parts need to be assembled, assembly workers become a bottleneck resource. This can prevent some parts from being assembled, leaving some equipment and tools idle. This example uses a production example from a large, complex product manufacturing plant as the input, applying the model and algorithm proposed in this example to solve a production task with 50 parts. The resulting static example yields a maximum completion time of 142 hours and a total order delay of 4, saving 9.9% of the planned production cycle.

[0150] The rest of this embodiment is the same as any one of the above-mentioned embodiments 1 and 2, and thus will not be described in detail.

[0151] Example 4:

[0152] This embodiment, based on any one of the above embodiments 1 to 3, proposes a large-scale complex product workshop scheduling system, including an initialization unit, a model building unit, and a calculation unit;

[0153] The initialization unit is used to determine the scheduling decision problem and premise assumptions of the large-scale complex product assembly and debugging workshop to obtain the maximum completion time of the workpiece;

[0154] The model building unit is used to establish a scheduling optimization model for large-scale complex product assembly and debugging workshops by taking minimization of the longest completion time of large-scale complex product workpieces as the optimization goal;

[0155] The computing unit is used to optimize and solve the scheduling optimization model of the large-scale complex product assembly and debugging workshop by using a multi-objective genetic algorithm to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

[0156] This embodiment also proposes an electronic device, including a memory and a processor; the memory stores a computer program; when the computer program is executed on the processor, the above-mentioned large-scale complex product workshop scheduling method is implemented.

[0157] This embodiment further proposes a computer-readable storage medium, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned large-scale complex product workshop scheduling method is implemented.

[0158] The rest of this embodiment is the same as any one of the above-mentioned embodiments 1 to 3, and thus will not be described in detail.

[0159] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the scope of protection of the present invention.

Claims

1. A large-scale complex product workshop scheduling method, characterized in that: Firstly, the scheduling decision problem and premise assumptions of large-scale complex product assembly and debugging workshop are determined to obtain the longest completion time of the workpiece. Then, minimizing the longest completion time of large-scale complex product workpieces is taken as the optimization goal, and a scheduling optimization model for large-scale complex product assembly and debugging workshop is established. Finally, a multi-objective genetic algorithm is used to optimize and solve the scheduling optimization model for large-scale complex product assembly and debugging workshop to obtain the target scheduling decision and target completion time of large-scale complex product assembly and debugging workshop.

2. A large-scale complex product workshop scheduling method according to claim 1, characterized in that: The large-scale complex product workshop scheduling method specifically comprises the following steps: Step 1: Determine the scheduling decision problem and premise assumptions of the large-scale complex product assembly and debugging workshop, and obtain the longest completion time of the workpiece; Step 2: According to the longest completion time of the workpiece, with minimizing the longest completion time of the workpiece and minimizing the total delay of the task as the optimization goal, set the priority constraints between the processes of large and complex products, the assembly and debugging equipment and tool resource constraints, and the assembly and debugging worker resource constraints, and establish a large and complex product assembly and debugging workshop scheduling optimization model; Step 3: Using heuristic rules to improve the initialization operation of the multi-objective genetic algorithm to obtain an improved multi-objective genetic algorithm; Step 4: Use an improved multi-objective genetic algorithm to optimize and solve the scheduling optimization model of the large-scale complex product assembly and debugging workshop to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

3. A large-scale complex product workshop scheduling method according to claim 2, characterized in that: The step 1 specifically comprises the following steps: Step 11: Determine the scheduling decision problems and assumptions of large and complex product assembly and commissioning workshops; Step 12: When the processes of large and complex products meet the priority constraints, determine the assembly order of the workpieces, select the tools or equipment for the current process during the scheduling process, and allocate the corresponding number of workers to obtain the longest completion time of the workpiece.

4. A large-scale complex product workshop scheduling method according to claim 2, characterized in that: The step 2 specifically includes the following steps: Step 21: Obtaining a maximum completion time objective function of the workpiece according to the maximum completion time of the workpiece; Step 22: Obtaining a total task delay objective function according to the longest completion time of the workpiece, the delivery period of the workpiece, and the delay coefficient of the workpiece; Step 23: According to the objective function of the longest completion time of the workpiece and the objective function of the total delay of the task, set the priority constraints between the processes of large and complex products, the assembly and debugging equipment and tool resource constraints, and the assembly and debugging worker resource constraints, and establish a scheduling optimization model for the assembly and debugging workshop of large and complex products.

5. A large-scale complex product workshop scheduling method according to claim 2, characterized in that: The specific operation of step 3 is: using multiple initialization methods to improve the initialization operation of the multi-objective genetic algorithm, and the multiple initialization methods include a random initialization method, an initialization method based on the priority of the delivery period rule, and an initialization method based on the priority of the remaining processing time.

6. A large-scale complex product workshop scheduling method according to claim 2, characterized in that: The step 4 specifically includes the following operations: Step 41: Establish a workpiece process structure tree; Step 42: Expand the workpiece process structure tree to obtain workpiece process structure tree information; Step 43: Perform chromosome encoding according to the workpiece process structure tree information to obtain a legal gene structure that satisfies the constraint condition of the front and back processing sequence between the workpieces in the assembly and debugging process; Step 44: Decode according to the legal gene structure to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop of the workpiece.

7. A large-scale complex product workshop scheduling method according to claim 6, characterized in that: The step 44 specifically includes the following steps: Step 441: Obtain a scheduling task, and traverse the artifacts in the chromosome sorting according to the legal gene structure; Step 442: Retrieve the artifact type according to the artifact number; Step 443: reading the process of the workpiece, and acquiring platform information of the process according to the process; Step 444: selecting a corresponding tool according to the workpiece type, determining the remaining use time of the tool, determining whether the number of workers meets the set conditions according to the remaining use time, and calculating the current completion time; Step 445: Determine whether all processes have been traversed. If not, return to step 443. If all processes have been traversed, determine whether all workpieces have been traversed. If not, return to step 441. If all workpieces have been traversed, use the current completion time as the target completion time and output the target scheduling decision for large and complex product assembly and debugging workshops.

8. A large-scale complex product workshop scheduling system, characterized in that: It includes an initialization unit, a model building unit, and a calculation unit; The initialization unit is used to determine the scheduling decision problem and premise assumptions of the large-scale complex product assembly and debugging workshop to obtain the longest completion time of the workpiece; The model building unit is used to establish a scheduling optimization model for large-scale complex product assembly and debugging workshops by taking minimization of the longest completion time of large-scale complex product workpieces as an optimization goal; The computing unit is used to optimize and solve the scheduling optimization model of the large-scale complex product assembly and debugging workshop by using a multi-objective genetic algorithm to obtain the target scheduling decision and target completion time of the large-scale complex product assembly and debugging workshop.

9. An electronic device, characterized in that: It comprises a memory and a processor; a computer program is stored in the memory; when the computer program is executed on the processor, the large-scale complex product workshop scheduling method as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions; when the computer instructions are executed on the electronic device as described in claim 9, the large-scale complex product workshop scheduling method as described in any one of claims 1-7 is implemented.

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