Flexible workshop scheduling method
By applying a scheduling method based on genetic algorithms in a flexible working workshop, the problem of inefficient production in the existing technology is solved, and more efficient workshop production and resource utilization are achieved.
Patent Information
- Application Number
- CN202411930298.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-04-11
AI Technical Summary
The prior art is difficult to effectively dispatch flexible operation workshops, resulting in low production efficiency and inability to meet modern production requirements.
A flexible workshop scheduling method based on genetic algorithm is adopted, and by setting parameters such as population number, iteration number, cross operator and mutation operator, simulation is carried out to find the relative minimum time scheduling that meets the constraints.
Improves workshop production efficiency, can meet the requirements of modern production efficiency, and provides transformation suggestions to maximize resource utilization.
Smart Images
Figure CN119806077A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a scheduling method and belongs to the technical field of workshop scheduling. Background Art
[0002] As customer demand and workshop production requirements continue to increase, the constraints on workshop production are also rising at the same rate. Manual scheduling of production can no longer meet the requirements of production efficiency in today's era. For the traditional Chinese medicine industry, workshop operation flexible workshop scheduling can be described as follows: there are N kinds of raw materials that need to be processed {j1, j2, ..., j n}, each raw material has corresponding process requirements, and there are M process steps {M1, M2, ..., M m}, and there are constraints between the corresponding processes. For example, the processing order of the processes corresponding to M2, M3, and M4 must meet the order requirements: M2>M3>M4, the latter order cannot exceed the former order, and there is no order requirement for other processes. This kind of flexible job shop with constraints between processes is undoubtedly a more complex NP-hard problem. For flexible job shops with constraints between processes, genetic algorithms have higher quality than artificial bee colony algorithms. The selection, crossover, and mutation operations in genetic algorithms can better meet the needs of flexible job shops with constraints between processes. Summary of the invention
[0003] The present invention aims to solve the problem that manual production scheduling can no longer meet the production efficiency requirements of the current era, and further proposes a flexible workshop scheduling method.
[0004] The technical solution adopted by the present invention to solve the above-mentioned problem is: the steps of the present invention include:
[0005] Step 1: Assume that there are 20 kinds of raw materials, which correspond to 60 operations according to the inventory ratio, and 8 equipments corresponding to 8 processes respectively. Assume that the process corresponding to each equipment is unique, and there are constraints between the processes. Assume that the processing sequence of the processes corresponding to M2, M3, and M4 must meet the sequence requirements: M2>M3>M4, the latter sequence cannot exceed the former sequence, and there is no sequence requirement between other processes. Ensure that each operation does not work on two equipments at the same time, and each equipment does not process two jobs at the same time. Assume that each job takes the same time on the 8 processes.
[0006] Step 2: Set parameters;
[0007] Step 3: The total working time of the equipment that completes the processing work the slowest is taken as the solution;
[0008] Step 4: Perform the above-mentioned flexible job shop simulation according to the parameters set in step 2, and take different values of the above five parameters to perform the simulation experiment algorithm.
[0009] Furthermore, the parameters set in step 2 specifically include the population size SUM, i.e., the number of individuals in the population, the number of iterations i, the crossover operator crossp, the mutation operator mp, and when the fitness difference of the best individual in 10 generations of the population is less than limit, the algorithm exits the iteration and outputs the optimal solution.
[0010] Furthermore, in step 3, when the solution value is smaller, it means that the total time of flexible workshop scheduling is shorter, and the fitness value of the corresponding chromosome is higher. Therefore, the total time of flexible workshop scheduling is taken as the solution value, and the algorithm finds the scheduling with the least relative time that meets the constraints.
[0011] Furthermore, in step 4, the five parameters SUM, i, crossp, mp, and limit are all set to five levels of values, and 50 simulation experiments are performed. According to the best optimal solution in the 50 simulation experiments, it is determined whether reasonable parameters that best meet the requirements of the algorithm have been found.
[0012] The specific steps of the present invention include:
[0013] Step 1. Assume that there are 4 kinds of raw materials. According to the inventory ratio, each raw material corresponds to 10 batches. There are 27 equipment corresponding to 4 sections: extraction section, double-effect section, alcohol precipitation section, and single-effect section. Among them, the extraction section corresponds to 12 parallel machines, the double-effect section corresponds to 6 parallel machines, the alcohol precipitation section corresponds to 6 parallel machines, and the single-effect section corresponds to 3 parallel machines. Assume that the process corresponding to each equipment is unique, and the four sections corresponding to the four processes have the following constraints: extraction section>double-effect section>alcohol precipitation section>single-effect section. The latter sequence cannot exceed the former sequence, that is, each work must first be carried out in the extraction section and then in the double-effect section, etc. It is necessary to ensure that each operation does not work on two devices at the same time, and each device does not process two jobs at the same time;
[0014] Step 2: Set parameters;
[0015] Step 3: The total working time of the equipment that completes the processing work the slowest is taken as the solution;
[0016] Step 4: Perform a simulation of the flexible job shop based on the parameters set in step 2 and simulate different values of the five parameters to perform a simulation experiment algorithm.
[0017] Furthermore, the parameters set in step 2 include: the population SUM value, i.e., the number of nectar sources in the population, the number of iterations i, the probability P1 of the neighborhood solution generating Method1#, the probability 1-P1 of Method2#, and the generation of scout bees when the trail value in each nectar source reaches the limit.
[0018] Furthermore, in step 3, when the solution value is smaller, it means that the total time of workshop scheduling is shorter, and the corresponding nectar source fitness value is higher, so the total time of workshop scheduling is taken as the solution value, and the algorithm finds the scheduling with the least relative time that meets the constraints.
[0019] Furthermore, in step 4, the four parameters SUM, i, P1, and limit are all set to four levels of values, and 50 simulation experiments are performed. According to the best optimal solution in the 50 simulation experiments, it is determined whether reasonable parameters that best meet the requirements of the algorithm have been found.
[0020] The beneficial effects of the present invention are as follows: the present invention can not only perform scheduling for workshops to improve production efficiency, but also provide transformation suggestions so that subsequent resources can play a practical role to the greatest extent; the present invention can meet the requirements of today's era for production efficiency, improve workshop production efficiency, and improve the scheduling of flexible workshops. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] Figure 1 It is a schematic diagram of 50 simulation results of population size SUM;
[0022] Figure 2 It is a schematic diagram of 50 simulation results of the crossover operator;
[0023] Figure 3 It is a schematic diagram of 50 simulation results of the mutation operator;
[0024] Figure 4 This is the schematic diagram of the simulation structure of P1 value 50 times; DETAILED DESCRIPTION
[0025] Specific implementation method 1: Figures 1 to 3 As shown, a flexible workshop scheduling method includes the following specific steps:
[0026] Step 1: Assume that there are 20 kinds of raw materials, which correspond to 60 operations according to the inventory ratio, and 8 equipments corresponding to 8 processes respectively. Assume that the process corresponding to each equipment is unique, and there are constraints between the processes. Assume that the processing sequence of the processes corresponding to M2, M3, and M4 must meet the sequence requirements: M2>M3>M4, the latter sequence cannot exceed the former sequence, and there is no sequence requirement between other processes. Ensure that each operation does not work on two equipments at the same time, and each equipment does not process two jobs at the same time. Assume that each job takes the same time on the 8 processes.
[0027] Step 2, set parameters; the parameters to be set specifically include the population size SUM, i.e. the number of individuals in the population, the number of iterations i, the crossover operator crossp, the mutation operator mp, and when the fitness difference of the best individual in the 10th generation population is less than limit, exit the iteration to end the algorithm and output the optimal solution;
[0028] Step 3: The total working time of the equipment that completes the processing work the slowest is taken as the solution; when the solution value is smaller, it means that the total time of flexible workshop scheduling is less, and the fitness value of the corresponding chromosome is higher, so the total time of flexible workshop scheduling is taken as the solution value, and the algorithm finds the scheduling with the least relative time that meets the constraints;
[0029] Step 4. Perform the above-mentioned flexible job shop simulation according to the parameters set in step 2, and take different values of the above five parameters to perform the simulation experiment algorithm; take 5 levels of values for the five parameters SUM, i, crossp, mp, and limit, and perform 50 simulation experiments. According to the best optimal solution in the 50 simulation experiments, determine whether reasonable parameters that best meet the algorithm requirements have been found.
[0030] Assuming that each job takes the same amount of time on the eight processes, the time corresponding to each job is shown in the following table:
[0031] Table 5.1 Timetable corresponding to each task
[0032]
[0033]
[0034] like Figure 1 As shown in Figure 2, 50 simulation experiments were conducted when the population size SUM was 1000, 2000, 3000, 4000, and 5000, and the simulation results were as follows: Figure 1 : When SUM = 1000, the optimal solution obtained by 50 simulation experiments is 480.215649, that is, the total time of flexible workshop scheduling is the shortest, that is, the total working time of the equipment that completes the processing last is the solution. In 50 simulation experiments, the minimum solution value is 480.215649. Similarly, when SUM = 4000, the optimal solution value of the simulation experiment is 382.50247. According to Figure 1 It can be seen that when the population size is between 1000 and 4000, the solution value continues to decrease. When the population size is between 4000 and 5000, the solution value remains basically unchanged. Therefore, it can be seen that when the population size is between 1000 and 4000, the gene segment has not yet achieved diversity. When the population size is between 4000 and 5000, the algorithm has found a relatively global optimal solution. The population size is too large, which reduces the efficiency of the algorithm, so the population size SUM is set to 4000.
[0035] like Figure 2 As shown in Figure 2, 50 simulation experiments were performed when the crossover operator was 0.4, 0.5, 0.6, 0.7, and 0.8, and the simulation results were as follows: Figure 2As shown in the figure, when the crossover operator is set to 0.4, the optimal solution obtained from 50 simulation experiments is 394.25683. Similarly, when crossp is set to 0.8, the optimal solution obtained from 50 simulation experiments is 388.56489. Figure 2 As shown in the figure, when the crossover operator is less than 0.7, the crossover amount of each individual in the population is insufficient and cannot meet the scheduling algorithm's requirements for gene diversity. On the contrary, when the crossover operator is greater than 0.7, the crossover amount of individuals in the population is too saturated in one iteration, resulting in the destruction of excellent gene segments and affecting the generation of the optimal solution.
[0036] like Figure 3 As shown in Figure 2, 50 simulation experiments were performed when the mutation operator was 0.03, 0.09, 0.13, 0.15, and 0.2, and the simulation results were as follows: Figure 3 As shown in the figure, when the mutation operator is set to 0.03, the optimal solution obtained from 50 simulation experiments is 390.45623. Similarly, when mp is set to 0.2, the optimal solution obtained from 50 simulation experiments is 400.87569. Figure 3 As shown in the figure, when the mutation operator is less than 0.09, there are fewer mutation operations in the algorithm iteration process, and the optimal solution found by the scheduling algorithm is not easy to jump out of the local optimum. On the contrary, when the crossover operator is greater than 0.09, there are more mutation operations in the population in one iteration, and the frequency of destroying the optimal solution is relatively large, which is easy to affect the generation of the optimal solution.
[0037] The parameter iteration number i and fitness difference limit are similar. When limit is 0.001, the iteration ends when the fitness difference of the optimal chromosome of the 10th generation population is less than 0.001, which helps to improve the efficiency of the algorithm without affecting the result of the optimal solution of the algorithm. In summary, when the parameter population size SUM is set to 4000, the iteration number i is 10000, the crossover operator is 0.7, the mutation operator is 0.09, and the limit is 0.001, the algorithm result is optimal and the efficiency of the algorithm is improved.
[0038] Specific implementation method 2: Figure 4 As shown, a flexible workshop scheduling method includes the following specific steps:
[0039] Step 1. Assume that there are 4 kinds of raw materials. According to the inventory ratio, each raw material corresponds to 10 batches. There are 27 equipment corresponding to 4 sections: extraction section, double-effect section, alcohol precipitation section, and single-effect section. Among them, the extraction section corresponds to 12 parallel machines, the double-effect section corresponds to 6 parallel machines, the alcohol precipitation section corresponds to 6 parallel machines, and the single-effect section corresponds to 3 parallel machines. Assume that the process corresponding to each equipment is unique, and the four sections corresponding to the four processes have the following constraints: extraction section>double-effect section>alcohol precipitation section>single-effect section. The latter sequence cannot exceed the former sequence, that is, each work must first be carried out in the extraction section and then in the double-effect section, etc. It is necessary to ensure that each operation does not work on two devices at the same time, and each device does not process two jobs at the same time;
[0040] Step 2, set parameters; the parameters to be set include: population SUM value, i.e., the number of nectar sources in the population, number of iterations i, probability P1 of the neighborhood solution generating Method1#, probability 1-P1 of Method2#, and generation of scout bees when the trail value in each nectar source reaches the limit;
[0041] Step 3: The total working time of the equipment that completes the processing work the slowest is taken as the solution; when the solution value is smaller, it means that the total time of workshop scheduling is less, and the corresponding honey source fitness value is higher, so the total time of workshop scheduling is taken as the solution value, and the algorithm finds the scheduling with the least relative time that meets the constraints;
[0042] Step 4: Perform simulation of the flexible job shop according to the parameters set in step 2 and conduct simulation experiment algorithm with different values of the five parameters; take 4 levels of values for the four parameters SUM, i, P1, and limit, and conduct 50 simulation experiments. According to the best optimal solution in the 50 simulation experiments, determine whether reasonable parameters that best meet the requirements of the algorithm have been found.
[0043] Among them, the working time of each raw material on each equipment is shown in the following table:
[0044] Table 5.3 Working schedule of each equipment in the extraction section
[0045]
[0046] The table above shows the processing time of 12 parallel extraction sections corresponding to 4 raw materials, in hours;
[0047] The following table summarizes the working schedule of each equipment in the double-effect section:
[0048] Table 5.4 Working schedule of each equipment in the double-effect section
[0049]
[0050] The table above shows the processing time of 6 dual-effect section parallel machines corresponding to 4 kinds of raw materials, in hours;
[0051] The following table summarizes the working schedule of each equipment in the alcohol precipitation section and single-effect section:
[0052] Table 5.5 Working schedule of each equipment in the alcohol precipitation section and single effect section
[0053]
[0054] The above table shows the processing time of 6 parallel machines for alcohol precipitation sections and 3 parallel machines for single-effect sections corresponding to 4 kinds of raw materials, in hours.
[0055] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.
Claims
1. A flexible workshop scheduling method, characterized in that: The specific steps include: Step 1: Assume that there are 20 kinds of raw materials, which correspond to 60 operations according to the inventory ratio, and 8 equipments corresponding to 8 processes respectively. Assume that the process corresponding to each equipment is unique, and there are constraints between the processes. Assume that the processing sequence of the processes corresponding to M2, M3, and M4 must meet the sequence requirements: M2>M3>M4, the latter sequence cannot exceed the former sequence, and there is no sequence requirement between other processes. Ensure that each operation does not work on two equipments at the same time, and each equipment does not process two jobs at the same time. Assume that each job takes the same time on the 8 processes. Step 2: Set parameters; Step 3: The total working time of the equipment that completes the processing work the slowest is taken as the solution; Step 4: Perform the above-mentioned flexible job shop simulation according to the parameters set in step 2, and take different values of the above five parameters to perform the simulation experiment algorithm.
2. A flexible workshop scheduling method according to claim 1, characterized in that: The parameters set in step 2 specifically include the population size SUM, i.e. the number of individuals in the population, the number of iterations i, the crossover operator crossp, the mutation operator mp, and when the fitness difference of the best individual in the 10th generation population is less than limit, the algorithm exits the iteration and outputs the optimal solution.
3. A flexible workshop scheduling method according to claim 1, characterized in that: In step 3, when the solution value is smaller, it means that the total time of flexible workshop scheduling is shorter, and the fitness value of the corresponding chromosome is higher. Therefore, the total time of flexible workshop scheduling is taken as the solution value, and the algorithm finds the scheduling with the least relative time that meets the constraints.
4. A flexible workshop scheduling method according to claim 1, characterized in that: In step 4, the five parameters SUM, i, crossp, mp, and limit are all set to five levels of values, and 50 simulation experiments are performed. Based on the best optimal solution in the 50 simulation experiments, it is determined whether reasonable parameters that best meet the algorithm requirements have been found.
5. A flexible workshop scheduling method, characterized in that: The specific steps include: Step 1. Assume that there are 4 kinds of raw materials. According to the inventory ratio, each raw material corresponds to 10 batches. There are 27 equipment corresponding to 4 sections: extraction section, double-effect section, alcohol precipitation section, and single-effect section. Among them, the extraction section corresponds to 12 parallel machines, the double-effect section corresponds to 6 parallel machines, the alcohol precipitation section corresponds to 6 parallel machines, and the single-effect section corresponds to 3 parallel machines. Assume that the process corresponding to each equipment is unique, and the four sections corresponding to the four processes have the following constraints: extraction section>double-effect section>alcohol precipitation section>single-effect section. The latter sequence cannot exceed the former sequence, that is, each work must first be carried out in the extraction section and then in the double-effect section, etc. It is necessary to ensure that each operation does not work on two devices at the same time, and each device does not process two jobs at the same time; Step 2: Set parameters; Step 3: The total working time of the equipment that completes the processing work the slowest is taken as the solution; Step 4: Perform a simulation of the flexible job shop based on the parameters set in step 2 and simulate different values of the five parameters to perform a simulation experiment algorithm.
6. A flexible workshop scheduling method according to claim 5, characterized in that: The parameters set in step 2 include: the population SUM value, i.e., the number of nectar sources in the population, the number of iterations i, the probability P1 of the neighborhood solution generating Method1#, the probability 1-P1 of Method2#, and the generation of scout bees when the trail value in each nectar source reaches the limit.
7. A flexible workshop scheduling method according to claim 5, characterized in that: In step 3, when the solution value is smaller, it means that the total time of workshop scheduling is shorter, and the corresponding nectar source fitness value is higher. Therefore, the total time of workshop scheduling is taken as the solution value, and the algorithm finds the scheduling with the least relative time that meets the constraints.
8. A flexible workshop scheduling method according to claim 5, characterized in that: In step 4, the four parameters SUM, i, P1, and limit are all set to four levels of values, and 50 simulation experiments are performed. According to the best optimal solution in the 50 simulation experiments, it is determined whether reasonable parameters that best meet the requirements of the algorithm have been found.