Integrated Optimization Methods for Flexible Workshop Process Routes and Production Scheduling

Abstract

This invention relates to a method for integrated optimization of process routing and production scheduling in a flexible workshop, comprising the following steps: determining the decision variables for integrated optimization of process routing and production scheduling in a flexible workshop; establishing a multi-objective integrated optimization model for process routing and production scheduling in a flexible workshop, with the objectives of minimizing total processing time, total processing energy consumption, and tool wear cost; randomly generating an initial scheduling solution based on the processing characteristics of each part, under the premise of satisfying constraints; generating a corresponding initial population according to the initial population size set in the algorithm's initial data; performing hierarchical crossover and mutation operations to form a new population; iterating to complete the process and outputting the optimal solution set to obtain the optimal process routing scheme. This invention, targeting the characteristics of flexible workshops, combines a genetic algorithm to integrate and optimize the process routing and workshop scheduling of parts, improving production efficiency, increasing equipment utilization, and reducing production costs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of workshop production, in particular to a flexible workshop process route and production scheduling comprehensive optimization method. BACKGROUND

[0002] With the continuous development of manufacturing industry, the degree of digitization and intelligence in the supply chain of traditional manufacturing enterprises gradually rises. The optimization of part process route and workshop scheduling problem has an important influence on the production efficiency, resource utilization rate and cost control of manufacturing enterprises. In the actual production process of the production workshop, the determination of the part process route not only selects and sequences the machining process, but also determines the machining equipment and machining tools of different processes, and is also affected by the process priority relationship between processes. Therefore, it is necessary to optimize the process route of the part according to the specific production requirements of the production workshop.

[0003] The scheduling problem refers to reasonably arranging the resources occupied by each task, the start time and finish time and the processing order under certain resource constraints, so that a certain or multiple performance indicators are optimized. The workshop scheduling problem has many complexities, such as modeling difficulties, multiple constraints, discrete and continuous coexistence and other factors. Efficient workshop scheduling technology research has become a frontier research direction in the field of scheduling technology.

[0004] In traditional research, process route optimization and workshop scheduling are two independent subsystems, and process route is formulated before workshop scheduling. This method not only is not conducive to improving the production efficiency and equipment utilization rate of the production workshop, but also causes a series of conflict problems, such as the maximum finish time of workshop scheduling is not the shortest when the machining time of each part is the shortest. Process planning and workshop scheduling have different optimization objects and optimization targets, the optimization object of process planning is the processing sequence of a single part and the optional machining resource, and the workshop scheduling needs to determine the start time and finish time of different processes of multiple parts on the machining machine. Since the optimization and selection of the process route only consider the processing sequence of a single part and the machining resource, resource conflict problems are easily caused in the process of workshop scheduling. Therefore, the integrated research of process planning and workshop scheduling can effectively overcome the above conflict problems, thereby improving the efficiency of the manufacturing system. SUMMARY

[0005] The purpose of the present application is to solve the problem that the process route and production scheduling are optimized separately in the prior art, which results in limited production efficiency and equipment utilization rate of the production workshop, and causes resource conflict problems, and to provide a flexible workshop process route and production scheduling comprehensive optimization method.

[0006] To solve the above problems, the present application adopts the following technical solutions:

[0007] A flexible workshop process route and production scheduling comprehensive optimization method, comprising the following steps:

[0008] Step one: determine the decision variables of the process route and production scheduling comprehensive optimization of the flexible workshop, the decision variables including the processing sequence of each process, the processing tool selected for each process, the processing equipment selected for each process, and the processing sequence of each part;

[0009] Step two: establish a multi-objective integrated optimization model of the process route and production scheduling of the flexible workshop, with the shortest total processing time, the smallest total processing energy consumption, and the smallest processing tool wear cost as the target, the total processing time being the sum of the part processing time, the part disassembly time, the part clamping time, and the tool changing time of the equipment, the total processing energy consumption being the sum of the equipment processing energy consumption, the standby energy consumption, and the tool changing energy consumption, and the processing tool wear cost being the sum of the wear costs of all tools;

[0010] Step three: generate a scheduling initial solution randomly based on the processing features of each part under the premise of meeting the constraint conditions;

[0011] Step four: repeat step three to generate the corresponding initial population according to the initial population number set in the algorithm initial data;

[0012] Step five: perform non-dominated sorting on the initial population, select the top-ranked population in the initial population, and perform hierarchical crossover and mutation operations to form a new population;

[0013] Step six: determine whether the algorithm iteration is complete, if yes, execute step seven, if not, return to step five;

[0014] Step seven: output the optimal solution set result to obtain the optimal process route scheme.

[0015] Compared with the prior art, the present application has the following beneficial effects: the flexible workshop process route and production scheduling comprehensive optimization method proposed in the present application integrates the process route and workshop scheduling of parts by combining genetic algorithm with the characteristics of flexible workshops, which can effectively overcome the resource conflict problem in the optimization process, improve the production efficiency of the production workshop, improve the equipment utilization rate, and reduce the production cost. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A schematic diagram of a processing process route with multiple flexibilities;

[0017] Figure 2A flow chart of the flexible job shop process route and production scheduling comprehensive optimization method in the embodiment of the present application;

[0018] Figure 3 An array structure of process sequence constraint data and equipment selection constraint data in the embodiment of the present application;

[0019] Figure 4 A structural schematic diagram of a part 1;

[0020] Figure 5 A structural schematic diagram of a part 2;

[0021] Figure 6 A solution set space diagram of the optimal solution set result in the embodiment of the present application. DETAILED DESCRIPTION

[0022] The present application will be further described below in conjunction with the drawings and embodiments, but should not be understood as limiting the above-mentioned subject matter of the present application to the following embodiments. Various substitutions and modifications can be made according to ordinary technical knowledge and conventional means in the art without departing from the above-mentioned technical idea of the present application, and all of them should be included in the protection scope of the present application.

[0023] The machining process route of a part represents a series of machining process procedures from raw materials to finished products. Since the machining features of a part are complex, each part usually has multiple machining feature units, and therefore the planning of the machining process route of a part involves not only multiple machining features, but also multiple machining processes, multiple machining resources (machining machine tools M and tools T), and the production tasks of a workshop are often the simultaneous production of multiple parts, so when arranging the production scheduling of the workshop, the machining processes of each part can be interleaved. This makes the machining process route have multiple flexibilities, such as Figure 1 as shown.

[0024] The process route and production scheduling comprehensive optimization problem can be described as follows: based on each machining feature unit, determine the corresponding machining process, select the required machining machine tool and tool for each process, and randomly arrange the machining processes of all parts, so that the selected scheduling scheme and machining process route achieve coordinated optimization in the three targets of total machining time, total machining energy consumption, and machining tool wear cost.

[0025] The assumption conditions of the process route and production scheduling comprehensive optimization problem of the flexible job shop are described as follows:

[0026] (1) All processes of the same part must comply with certain process sequence constraints, such as reference constraints, process structure constraints, etc. Different parts do not need to follow the process sequence constraints.

[0027] (2) If the machine tools of two adjacent processes are different, the workpiece needs to be re-clamped; if the cutting tools of two adjacent processes are different, a tool change operation is required.

[0028] This invention provides a comprehensive optimization method for flexible workshop process routing and production scheduling. This method establishes a multi-objective integrated optimization model for flexible workshop process routing and production scheduling, aiming to minimize total processing time, total processing energy consumption, and tool wear cost. Furthermore, it proposes an optimization model based on a multi-objective genetic algorithm, specifically, as follows: Figure 2 As shown, the method includes the following steps:

[0029] Step 1: Determine the decision variables for the comprehensive optimization of the process route and production scheduling in the flexible workshop.

[0030] The decision variables for the integrated optimization problem of process route and production scheduling in this invention include: 1) the processing sequence of each process; 2) the processing tools selected for each process; 3) the processing equipment selected for each process; and 4) the processing sequence of each part.

[0031] like Figure 2 As shown, before running the multi-objective genetic algorithm, the basic data is first determined through the following steps: dividing each part into processing units; determining the processing method for each processing unit; determining the available machine tools and cutting tools for each feasible processing method; and determining the constraints between each process. After determining the basic data through the above steps, the decision variables for the comprehensive optimization problem of the above-mentioned process route and production scheduling can be finally determined.

[0032] Step 2: With the goals of minimizing total processing time, total processing energy consumption, and tool wear cost, establish a multi-objective integrated optimization model for the process route and production scheduling of the flexible workshop. The total processing time is the sum of the processing time, disassembly time, clamping time, and tool change time of each process. The total processing energy consumption is the sum of the processing energy consumption, standby energy consumption, and tool change energy consumption of the equipment. The tool wear cost is the sum of the wear costs of all tools.

[0033] 1) Objective function for total processing time

[0034] In this invention, the processing time for each part consists of four parts: the processing time t for each process. 加 Disassembly time t 拆 clamping time t 装 and the tool change time t of the equipment 换 Therefore, the objective function for the total processing time is as follows:

[0035]

[0036] Wherein, r represents the rth process, R represents the total number of processes.

[0037] 2) Total energy consumption target function of processing

[0038] In the present application, the total energy consumption W of the workshop is 总 composed of three parts: equipment processing energy consumption w 加 , standby energy consumption w 待 and tool changing energy consumption w 换 , and the calculation formula is: W 总 = w 加 + w 待 + w 换 .

[0039] The calculation formula of equipment processing energy consumption w 加 is as follows:

[0040]

[0041] In the formula, P ci represents the processing power of the equipment numbered i, t 加ij represents the processing time of the jth part on the equipment i, n represents the total number of equipment, and k represents the total number of parts processed on the equipment numbered i.

[0042] The calculation formula of standby energy consumption w 待 is as follows:

[0043]

[0044]

[0045] In the formula, P ist represents the standby power of the equipment numbered i, t 待i represents the standby time of the equipment numbered i, t 加i represents the processing time of the equipment numbered i, and t 换i represents the tool changing time of the equipment numbered i.

[0046] The calculation formula of tool changing energy consumption w 换 is as follows:

[0047]

[0048] In the formula, P ifeed represents the no-load power of the equipment numbered i, and t 换i is the tool changing time of the equipment numbered i.

[0049] 3) Processing tool wear cost target function

[0050] The wear of machining tools in a processing workshop is also a very important component of processing cost, and thus the wear cost V of machining tools in the workshop needs to be optimized to reduce the production cost.

[0051]

[0052] In the formula, h m represents the wear cost of the tool numbered m per unit time, t m represents the total processing time of the tool numbered m, and M represents the total number of tools.

[0053] Step three: based on the processing features of each part, an initial solution of scheduling is randomly generated under the premise of meeting the constraint conditions.

[0054] Specifically, the constraint conditions of the present application are described as follows:

[0055] (1) The processing procedures of each part must follow certain order relationship constraints, such as reference constraints, positioning and clamping constraints, etc. An array structure is defined as shown in Figure 3 (a), which is the arrangement of the preceding procedures of the procedures. The row number in the table represents the current procedure number, for example, the first row represents the first procedure of the current part. If the value in a procedure is -1, it represents that the procedure must be completed first. If the content of the second row is [3, 5], it represents that the second procedure must be completed before the third procedure and the fifth procedure. Similarly, a post-procedure array is established.

[0056] (2) The machine tool selection and tool selection of each procedure must meet the specific requirements of the processing technology, such as the processing of a plane, which can select a milling machine or a machining center, but cannot select a drilling machine. Therefore, in the present application, an array structure is established as shown in Figure 3 (b). The row number in the table represents the number of the current processing procedure, and each row has an array, for example, the number 3, 4, 5, 6, 8 in the first row represents that the first procedure can select five devices numbered 3, 4, 5, 6, and 8.

[0057] In step three, the process of randomly generating an initial solution of scheduling specifically includes the following steps:

[0058] Step a): a matrix A is randomly generated according to the number of existing parts and the number of procedures required for each part. The column number of the matrix A is equal to the total number of procedures of the parts, and the row number is 4 rows, of which the first row is the scheduling number, the second row is the procedure number, the third row is the equipment number, and the fourth row is the tool number. The initial code is generated according to the number of parts and the number of procedures;

[0059] Step b): the procedure numbers in the procedure set of each part are randomly arranged;

[0060] Step c): Place the randomly arranged process numbers below the corresponding columns;

[0061] Step d): In the third row of matrix A, randomly select a machine tool from the available equipment for each process;

[0062] Step e): In the fourth row of matrix A, randomly select a tool from the available tools for each operation;

[0063] Step f): Randomly arrange the first row of matrix A;

[0064] Step g): Calculate the corresponding objective function value for matrix A based on step one to obtain an initial scheduling solution.

[0065] Step 4: Based on the initial population size set in the algorithm's initial data, repeat step 3 to generate the corresponding initial population.

[0066] Step 5: Selecting individuals in the bidding process involves performing a non-dominated sort on the initial population, selecting the top-ranked individuals from the initial population, and then performing stratified crossover and mutation operations on them to form a new population.

[0067] Step Six: Determine if the algorithm has completed its iteration. If yes, proceed to Step Seven; otherwise, return to Step Five and restart the bidding process to select individuals.

[0068] Step 7: Output the frontier solution set as the optimal solution set to obtain the optimal process route scheme.

[0069] The flexible workshop process route and production scheduling integrated optimization method proposed in this embodiment, taking into account the characteristics of flexible workshops, combines genetic algorithms to integrate and optimize the process route and workshop scheduling of parts. Since both workshop scheduling and process route are considered at the same time, it can effectively overcome the resource conflict problem in the optimization process, improve the production efficiency of the production workshop, increase equipment utilization, and reduce production costs.

[0070] The technical solution and effects of the present invention will be described below with reference to specific examples.

[0071] This example uses the machine tool's energy efficiency monitoring system to measure the machine tool's real-time power. The power information and tool change time of each machining machine are shown in Table 1. The machining tool information is shown in Table 2.

[0072] This example uses Figure 4 Part 1 and shown Figure 5 Part 2 shown is used as the object for verification, based on Figure 4 and Figure 5 The machining characteristics of the two parts were analyzed to obtain feasible machining processes, machine tools, cutting tools, and process constraints, as shown in Table 3.

[0073] Table 1 power information of machining tools, tool changing time

[0074]

[0075] Table 2 machining tool information

[0076] Tool number Tool type Tool life (min) Tool price T1 Milling cutter 1 60 22 T2 Milling cutter 2 23 15 T3 Milling cutter 3 100 13 T4 Tap 120 17 T5 Milling cutter 4 110 20 T6 Milling cutter 5 200 25 T7 Turning tool 1 120 16 T8 Turning tool 2 80 12

[0077] Table 3 machining process, machine tool, machining time, tool and process constraint information of parts

[0078]

[0079] Next, a scheduling initial solution is randomly generated:

[0080] Step a): a matrix A is randomly generated according to the number of existing parts and the number of processes required for each part, the column number of the matrix A is equal to the total number of processes of the parts, and the row number is 4 rows. The first row is the scheduling number, the second row is the process number, the third row is the equipment number, and the fourth row is the tool number. The initial code is generated according to the number of parts and the number of processes. For example, there are two parts, the first part has 4 processes, and the second part has 2 processes, then the first row of matrix A a={1,1,1,1,2,2}, the second row b={1,2,3,5,1,2}.

[0081] Step b): randomly arrange the process numbers in the process set of each part. For example, after randomly arranging b1={1,2,3,5}, b1={1,3,5,2} is obtained.

[0082] Step c): place the randomly arranged process numbers under the corresponding columns, for example, the randomly arranged process set of part 1 b1={1,3,5,2}, the process set of part 2 b2={1,2}, and the total process set b={1,3,5,2,1,2}.

[0083] Step d): randomly select a machine tool for each process in the available equipment in the third row of matrix A.

[0084] Step e): randomly select a tool for each process in the available tools in the fourth row of matrix A.

[0085] Step f): randomly arrange the first row a of matrix A, such as a={1,1,1,1,2,2}, after arrangement a={1,2,1,1,2}, which represents the scheduling order of the parts, that is, the first 1 represents the first process of part 1, and the second 2 represents the second process of part 1.

[0086] Step g): Calculate the corresponding objective function value for matrix A based on step one to obtain an initial scheduling solution.

[0087] Based on the initial population size set in the algorithm's initial data, the operation of randomly generating the initial scheduling solution in the previous step is repeated to generate the corresponding initial population. If the initial population size is 10, then 10 matrices identical to matrix A are generated to form the algorithm's initial data.

[0088] The initial solution set is sorted non-dominated. The solution set with the highest ranking in the initial solution set is selected and subjected to stratified crossover and mutation operations to form a new population.

[0089] After performing the above operations a certain number of iterations, a better population will be obtained. This better population will be sorted using a non-dominated method, and the final output will be the frontier solution set, which is the optimal solution set. The solution space of this optimal solution set is as follows: Figure 6 As shown in the figure, the X-axis represents the maximum completion time (in seconds), the Y-axis represents the equipment energy consumption (in kW / h), and the Z-axis represents the tool wear cost (in yuan). Its maximum completion time is 1225 seconds, the tool wear cost is 3.69 yuan, and the equipment energy consumption is 136.6 kW / h.

[0090] Therefore, the optimal process routes for parts 1 and 2 are obtained, as shown in Table 4.

[0091] Table 4 Optimal Process Route Scheme

[0092]

[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A flexible job-shop process route and production scheduling integrated optimization method, characterized in that, Comprising the following steps: Step one: determine the decision variables of the process route and production scheduling optimization of flexible workshop, the decision variables include the processing sequence of each process, the processing tool selected for each process, the processing equipment selected for each process, and the processing sequence of each part; Step two: establish a multi-objective integrated optimization model of process route and production scheduling of flexible workshop, with the shortest total processing time, the smallest total processing energy consumption, and the smallest processing tool wear cost as the target, the total processing time is the sum of the processing time of each process, the disassembly time of parts, the clamping time of parts, and the tool changing time of equipment, the total processing energy consumption is the sum of the processing energy consumption of equipment, standby energy consumption and tool changing energy consumption, the processing tool wear cost is the sum of the wear cost of all tools; Step three: generate an initial solution of scheduling randomly based on the processing characteristics of each part under the premise of meeting the constraint conditions; the constraint conditions include: (1) the sequence relationship constraint between each processing process of the part must be followed; (2) the machine tool selection and tool selection of each process must meet the specific requirements of processing technology; Step three includes the following steps: Step a): randomly generate a matrix A according to the number of existing parts and the number of processes required for each part, the column number of matrix A is equal to the total number of processes of the part, and the row number is 4, of which the first row is the scheduling number, the second row is the process number, the third row is the equipment number, and the fourth row is the tool number, generate the initial code according to the number of parts and the number of processes; Step b): randomly arrange the process number inside the process set of each part; Step c): place the randomly arranged process number under the corresponding column; Step d): randomly select a machine tool for each process in the selectable equipment in the third row of matrix A; Step e): randomly select a tool for each process in the selectable tool in the fourth row of matrix A; Step f): randomly arrange the first row of matrix A; Step g): calculate the corresponding objective function value of matrix A according to step one, and get an initial solution of scheduling; Step four: repeat step three to generate the corresponding initial population according to the initial population number set in the initial data of the algorithm; Step five: sort the initial population, select the top-ranked population in the initial population, and perform hierarchical crossover and mutation operations to form a new population; Step six: determine whether the algorithm iteration is completed, if yes, execute step seven; if not, return to step five; Step seven: output the optimal solution set result to get the optimal process route scheme.

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