CN113191085A - Setting method of incomplete disassembly line considering tool change energy consumption - Google Patents

Setting method of incomplete disassembly line considering tool change energy consumption Download PDF

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CN113191085A
CN113191085A CN202110496495.1A CN202110496495A CN113191085A CN 113191085 A CN113191085 A CN 113191085A CN 202110496495 A CN202110496495 A CN 202110496495A CN 113191085 A CN113191085 A CN 113191085A
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张则强
梁巍
刘思璐
郑红斌
计丹
方潇悦
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Abstract

The setting method of the incomplete disassembly line under the condition of considering tool conversion energy consumption comprises the following steps: providing a constraint condition of an incomplete disassembly line balance problem (PDLBP), establishing a target function of the incomplete disassembly line balance problem, then establishing an initial spider population according to a priority relation of a product to be disassembled, enabling spider individuals to vibrate in a two-point cross operation mode, performing iterative computation on the vibrating spider individuals, screening each iterative computation result by using an elite strategy and a congestion evaluation mechanism, screening out non-inferior solutions concentrated by external files of the target function, and outputting the non-inferior solutions as final results; according to the improved social spider algorithm, random movement and mask changing operation are redesigned, partial individual of the population is replaced by the artificial spiders in stages, local optimization is avoided, the overall search capability of the algorithm is effectively improved, the optimal solution of the incomplete disassembly line problem is achieved, and feasible theoretical guidance is provided for layout optimization setting of the disassembly lines.

Description

Setting method of incomplete disassembly line considering tool change energy consumption
Technical Field
The invention relates to the technical field of facility layout, in particular to a setting method of an incomplete disassembly line under the condition of considering tool change energy consumption.
Background
With the rapid development of science and technology and the continuous progress of society, the requirements of people on the quality of life are higher and higher, and the consumption consciousness also tends to be personalized. Therefore, in order to meet the demands of consumers, enterprises need to continuously produce and update products. Under the dual influence of consumers and enterprises, on one hand, a large amount of natural resources are consumed to produce new products, so that the natural resources are exhausted; on the other hand, a large amount of waste electronic products are continuously eliminated, and valuable resources in the waste products cannot be effectively recovered. Therefore, the technical research on how to recycle the waste products is very important. The resource recovery and remanufacturing process of the waste electronic products does not need to detach the treatment means. Disassembly is to remove environmentally hazardous parts and perform harmless disposal, while valuable parts are disassembled and reused, remanufactured or raw material recycled.
The disassembly of the part is divided into complete disassembly and incomplete disassembly according to the disassembly condition and degree, wherein the complete disassembly needs to complete the disassembly of all parts, but the incomplete disassembly only needs to complete the disassembly of parts with harm and requirements, and compared with the complete disassembly, the incomplete disassembly can greatly improve the disassembly efficiency and the disassembly yield, so that the problem of the balance of the disassembly line related to the basic running state of the disassembly line naturally becomes a key research object in the academic world; the study of the complete disassembly mode and the incomplete disassembly mode is respectively carried out by scholars at home and abroad, the current main study object is the complete disassembly line balance problem, and the study on the incomplete disassembly line balance problem (PDLBP) is very limited.
The Social Spider Algorithm (SSA) is a new intelligent optimization Algorithm proposed in 2015. Simulating the foraging behavior of social spiders, moving towards the food source position in a coordinated manner, and vibrating and propagating through the spiders to determine the potential position of the food source. In an initialization stage, a spider population is generated according to rules, the positions of spiders are randomly distributed, and the vibration intensity and other attributes are set to be 0; the iteration stage comprises five steps of fitness value evaluation, vibration generation, mask change, random walking and constraint processing; the final stage is to output the iterative optimal solution and the optimal fitness value, and it can be seen that the SSA can be effectively used for solving the problem of incomplete disassembly line balance.
However, the SSA algorithm adopts a random walk strategy to ensure population diversity to realize the transition to the optimal state, so that the convergence rate of the algorithm is limited, and the social spider algorithm is easy to fall into the local optimal problem, so that the social spider algorithm for guiding the solution of the incomplete disassembly line balance problem needs to be improved.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for setting a multi-target incomplete disassembly line based on an Improved Social Spider Algorithm (ISSA) under consideration of tool transformation energy consumption, so as to obtain a comprehensive optimal solution in many aspects and provide guidance and basis for the optimal setting of the incomplete disassembly line in various complex situations.
The technical scheme of the invention is that the setting method of the incomplete disassembly line under the condition of considering tool conversion energy consumption comprises the following steps:
step 1, aiming at minimizing the number of start workstations, idle time balance indexes, tool replacement times and disassembly energy consumption indexes, providing constraint conditions of incomplete disassembly line balance problems (PDLBP), and establishing a target function of the incomplete disassembly line balance problems;
step 2, setting problem parameters and algorithm parameters, constructing an initial spider population according to a priority relation matrix TP of a product to be disassembled in the problem parameters, inspecting individual spider population by using an algorithm, changing a feasible sequence disassembly sequence contained in a spider in a two-point cross operation mode, and changing a target value of the sequence disassembly sequence representing vibration so as to enable the individual spider to vibrate;
step 3, performing iterative computation based on the vibrating spider individual in the step 2, randomly moving the spider individual, further improving a feasible disassembly sequence by adopting special mask changing operation in an iterative process, increasing additional vibration, improving the moving efficiency of the spider, improving the iterative computation rate on the whole, and introducing a special means for improving the overall optimization capability of the algorithm in the iterative process;
and 4, step 4: and screening the iteration calculation result of each time by using a Pareto elite strategy, selecting non-inferior individuals which accord with the target function and are not mutually dominant, storing the non-inferior individuals in an external archive set as a next iteration population or a non-inferior solution, and screening the non-inferior solution in the external archive set of the target function by using an NSGA-II congestion distance evaluation standard after the iteration calculation times meet a termination condition, and outputting the non-inferior solution as a final result.
In one embodiment of the present invention, in step 1, the objective function is:
F＝min[NS,IT,TR,EC]
wherein NS is the number of open workstations;
IT is an idle time balance index;
TR is the tool replacement times;
EC is the index of energy consumption for disassembly.
In step 1, the constraint conditions include a part removal constraint, a workstation task allocation constraint, a beat time constraint, a real beat time constraint, a workstation sequence opening constraint, and a task removal priority relationship constraint.
In one embodiment of the present invention, in step 2, the method for constructing the initial spider population is to use a real number coding-based representation, and use each group of feasible task disassembly sequences obtained by coding as the spider individuals.
Further, in step 2, the specific mode of the two-point crossing operation is that two individual spiders are randomly selected from a spider population to define two paired spiders, the same two points are randomly selected from the feasible disassembly sequences of the paired spiders, task numbers are mapped with each other, and the positions of the task numbers are moved, so that the paired spiders respectively form new disassembly sequences.
In one embodiment of the present invention, in step 3, when the spider individual is randomly moved, the paired two rows of detachable task sequences are compared, and the different task numbers are deleted.
One embodiment of the present invention is that, in step 3, the special mask changing operation adopted in the iterative computation process is a single-point random insertion method.
In step 3, the special means for improving the global optimizing capability of the algorithm is to replace the number of (Pop _ num)/10 spider individuals in the population with artificial spiders at iteration G/5, 2G/5, 3G/5 and 4G/5 of the algorithm.
In one embodiment of the present invention, after the spider individual generates the vibration, the spider individual calculates a sequence before and after the generation of the tool transformation using a tool transformation energy consumption matrix.
The invention has the technical effects that:
(1) the tool transformation energy consumption matrix is provided under the condition of considering tool transformation energy consumption, an incomplete disassembly multi-objective mathematical model taking the minimum workstation opening number, the idle time balance index, the tool transformation times and the disassembly energy consumption as optimization objectives is established, and feasible theoretical guidance is provided for layout optimization setting of the disassembly line.
(2) In order to solve PDLBP, an improved social spider algorithm is provided, random movement and mask changing operations are redesigned, manual spiders are executed in stages to replace partial individuals of a population, and the method is combined with a Pareto idea and an NSGA-II crowding distance mechanism, so that the phenomenon of falling into local optimization is avoided, and the global search capability of the algorithm is effectively improved.
(3) In the coding process, a '0' element is used for replacing an unassigned task position, the '0' element is deleted in the random moving, mask changing and decoding processes, then the next operation is carried out, and in the random moving stage, two feasible disassembly sequences are used for comparison, and the difference task number is deleted, so that the disassembly task number can be effectively reduced, and the algorithm global optimization is accelerated.
(4) By using ISSA to solve the scale calculation in P25 and comparing and analyzing the solving results of the conventional algorithm, the algorithm is obviously superior to algorithms such as Hybrid Particle Swarm Optimization (HPSO) and genetic simulated annealing (GASA), the solution result scheme is analyzed by combining the established multi-target mathematical model and the printer disassembly solving example solved by ISSA, and the effectiveness of solving the Disassembly Line Balance Problem (DLBP) by ISSA and the diversity of the solving results are verified.
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In order to more clearly illustrate the technical solution of the embodiments of the present invention, the drawings used in the embodiments will be briefly described below.
FIG. 1 is a general flowchart of a disassembly line setting method according to the present invention;
FIG. 2 is a specific diagram illustrating the process of changing the individual spiders by the two-point crossing operation in the present invention;
FIG. 3 is a detailed diagram illustrating a process of deleting a difference number according to the present invention;
FIG. 4 is a schematic diagram of mask changes in the present invention;
FIG. 5 shows a target f for comparison of three algorithms in the embodiment of the present invention2、f3、f4True Pareto front profile.
Detailed Description
The present invention will be described in further detail with reference to the following examples and the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings, which are attached to the drawings and are a part of the embodiments of the present invention, but not all of the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Notation and decision variables account for:
TABLE 1 notation and decision variable description
Figure BDA0003054614800000051
Figure BDA0003054614800000061
To facilitate understanding of the meanings of the examples and other formulas in the subsequent sections of this specification, the notation and decision variables of Table 1 are given, and unless otherwise specified, the notation and variables described below as being the same as those in Table 1 are also given the same meanings as those in Table 1.
Example (b):
referring to fig. 1, a flow of a method for setting an incomplete disassembly line under consideration of tool change energy consumption is shown in fig. 1, and specifically includes the following steps:
step 1, when a disassembly task is distributed for PDLBP (incomplete disassembly line balancing), in order to reduce energy consumption and improve efficiency as much as possible, the number of the work stations of the disassembly line and the idle time of each work station need to be as small as possible, the change of the disassembly tool needs to be as small as possible, and parts with requirements and hazards need to be disassembled as early as possible, so that the following objective function can be established based on the above elements:
F＝min[NS,IT,TR,EC]
(1)
wherein NS is the number of open workstations;
IT is an idle time balance index;
TR is the tool change times;
EC is the index of energy consumption for disassembly.
Meanwhile, the optimization objectives of the objective function (1) are respectively shown in table 2:
TABLE 2 specific optimization objective Table for objective function
Figure BDA0003054614800000062
Figure BDA0003054614800000071
It should be noted that the energy consumption c of the tool change, as indicated in the objective function, is the tool change time interval ttrAdditional energy consumption c for disassembling harmful partshAnd the additional energy consumption c of the parts with disassembly requirementsdEnergy consumption per unit time of opening workstation cuThe variables can be obtained from the actual operating conditions of the disassembly line.
As can be seen from Table 2, the optimization goal of the objective function is to make the four indicators NS, IT, TR, and EC close to the minimum. Wherein, the first optimization target is shown as formula (2), in order to minimize the number of workstations and reduce the production cost and the facility space of the workstations; the second optimization target is shown as formula (3), and is a minimum idle balance index, meets the requirement of 'one flow' of assembly line operation, and avoids blockage of a disassembly line due to unbalanced upstream and downstream operation time; the third optimization target is shown as formula (4), in order to minimize the tool change times, the tool change times are more, the disassembly efficiency is influenced, the fewer change times are more beneficial to the quick disassembly operation, and the actual production requirements are met; the fourth optimization objective is shown in formula (5), which is to minimize the disassembly energy consumption, which is beneficial to reduce the disassembly cost and increase the disassembly benefit, wherein the energy consumption includes the equipment operation energy consumption, the disassembly hazard, and the additional energy consumption and tool change energy consumption generated by the required parts.
Meanwhile, the constraints of the objective function in the PDLBP problem are shown in table 3:
TABLE 3 constraint table for objective function
Figure BDA0003054614800000072
Figure BDA0003054614800000081
Wherein, the formula (6) and the formula (7) are all parts dismantling deviceBundling, wherein the formula (6) represents that the parts of the waste products are partially removed, and the formula (7) represents that the parts of the demand products and the parts of the hazard products must be removed; equation (8) represents workstation task assignment constraints, with an open workstation assigning at least one tear down task and not exceeding the total tear down task number; equation (9) represents a takt time constraint, the total working time of the disassembly tasks allocated by each opening workstation cannot be greater than a predetermined takt time; the equation (10) represents the real beat time constraint, and the maximum total operation time for starting the workstation is taken as the real beat time; formula (11) represents the workstation sequential opening constraint, and the next workstation can be opened only if the current workstation is in the open state; equation (12) is the tear down task precedence constraint, only if tear down task xiComplete disassembly, post-tightening task xkCan disassembly be performed.
Step 2, problem parameters and algorithm parameters are actually set according to the PDLBP problem, the problem parameters comprise a priority relation matrix TP, a disassembly information matrix KB, a preset beat time RT, the algorithm parameters comprise a population scale Pop _ num, a maximum iteration number G, an optimized target number z and an external file number N, wherein the priority relation matrix TP parameter comprises the priority relation of disassembly tasks, an initial spider population can be established according to a coding mode of real number coding, the feasibility of an individual in the initial spider population as an optimal solution is preliminarily judged according to an algorithm based on a target function, and an external file set of the spider population is established
Figure BDA0003054614800000082
Specifically, the initial spider population adopts a real number coding-based mode, each group of feasible task disassembly sequences obtained through coding is used as a spider individual, a '0' element is used for replacing an unassigned task position in the coding process, and each group of feasible task disassembly sequences can obtain a corresponding feasible sequence target value through a decoding mode and are used for describing the vibration condition.
And then changing the disassembly sequence of the individual spiders in a two-point cross operation mode, and further changing a feasible sequence target value obtained by decoding the individual disassembly sequence, thereby representing the generation of vibration. When passing through the intersectionAfter the disassembly sequence is changed by operation, the disassembly tasks before and after the disassembly sequence are correspondingly changed, and after the judgment of the algorithm, if two adjacent tasks of the same workstation use different disassembly tools, extra energy consumption c for tool change can be generatedrFor this reason, a tool transformation energy consumption matrix (13) is introduced in the present embodiment to realize accurate and convenient calculation of energy consumption changes occurring in the changed disassembly task sequence.
Figure BDA0003054614800000091
In the formula, TL1、TL2、TLnRespectively representing the disassembly tools numbered 1, 2 to n, c12Representing the energy consumption of the conversion from tool No. 1 to tool No. 2, c21Representing the energy consumption of conversion generated when the tool No. 2 is converted into the tool No. 1, and the like; by means of the tool transformation energy consumption moment (13), extra energy consumption brought by different tool transformation processes in the spider individual sequence transformation process can be obtained, and after the extra energy consumption is introduced into the overall algorithm calculation, the evaluation accuracy and effect of the algorithm can be further improved.
Meanwhile, in the present embodiment, a specific method of changing the detachment sequence of an individual spider is shown in fig. 2: randomly selecting two Spider individuals from a Spider population for pairing, respectively numbering the two Spider individuals as Spider _ S and Spider _ M, randomly selecting the same two points on a feasible disassembly sequence of the two Spider individuals, mapping a task number between the two points of the Spider _ S between the two points of the Spider _ M on the Spider _ M, wherein no number of a selected cross sequence exists between the two points of the Spider _ M Spider, and mapping the two points to move forwards or backwards according to the front and back positions of the two points of the Spider _ S Spider, so that the two spiders form a new disassembly sequence. And performing the above operation on the Spider _ S by the Spider _ M, so that the sequence target values of the two Spider individuals are changed, and the new Spider individual generates vibration.
Step 3, setting the initial iteration times as g to be 1, and performing iterative calculation on the vibrated initial spider population;
the iterative computation process enables the spider individuals to generate random movement, when the two-point cross operation is carried out in the step 2 to change the disassembly sequence of the spider individuals, paired spiders with different sequence lengths can be obtained, so that the '0' element is deleted in the random movement, mask change and decoding processes, then the next operation is carried out, and in the random movement stage, the two paired disassembly sequences are used for comparison, and the difference task numbers are deleted, as shown in fig. 3, so that the disassembly task number can be effectively reduced, and the algorithm global optimization is accelerated;
in addition, the random walk algorithm strategy of the general Social Spider Algorithm (SSA) has the problems of low convergence rate, easy falling into local optimum and the like, so in the embodiment, the special mask changing operation is adopted to accelerate the movement of the spider, and extra vibration is introduced through the mask changing operation to improve the moving efficiency of the individual spider;
as shown in fig. 4, the specific operation of the mask change is a single-point random insertion operation: randomly selecting two points on a feasible disassembly sequence, counting the quantity N of disassembly tasks between the two points, and randomly generating any real number in N masks Pm, wherein Pm belongs to (0, 1); selecting a disassembly task with the maximum mask value, defining the disassembly task as a task m, finding a task immediately before and a task immediately after the task, randomly inserting the task m into any position between the task immediately before and the task immediately after, and ensuring that the positions of the task immediately before and after the insertion are not changed, thereby further updating the spider population, introducing additional vibration and finally playing a role in improving the convergence speed of the algorithm.
Furthermore, in order to avoid falling into local optimization, after mask change is completed in iterative calculation, the algorithm jumps out of the local optimization in a mode that the artificial spiders update the population, specifically, in the iterative calculation process, when the algorithm iterates to G/5, 2G/5, 3G/5 and 4G/5, the artificial spiders are used for replacing spider individuals with population size Pop _ num/10 in the population, the artificial spiders do not belong to individuals in the spider population in the algorithm, and are task disassembly sequences generated according to requirements, and the addition of the artificial spiders can effectively improve the optimization ability of the global algorithm and avoid falling into the local optimization.
Step 4, screening results after each iterative calculation to the artificial spider update population by using a Pareto elite strategy, selecting non-inferior individuals which can meet the requirements of a target function and a constraint condition and are not mutually dominant, and storing the non-inferior individuals in an external file set Q as a next iteration population or a non-inferior solution;
and meanwhile, the algorithm judges whether the iterative computation needs to be finished according to the iterative computation times, and screens out non-inferior solutions in the external file set of the objective function by using the NSGA-II congestion distance evaluation standard after the iterative computation times G meet the termination condition that G is larger than G (the maximum iterative times), and outputs the non-inferior solutions as final results.
The following is a description of specific effects of this embodiment:
program test environment:
the operating environment of the improved social spider algorithm is Intel (R) Xeon (R) CPU E5-1620 v3@3.50GHz 3.50GHz processor, 32GB operating memory, and under a Windows7 system, a program is developed through Matlab R2014 b.
1. Comparing the effects of similar algorithms:
using the same type of algorithms HPSO, GASA and ISSA to solve the example P25 containing 25 mobile phone disassembly tasks in a medium scale, wherein f1、f2、f3、f4Respectively representing the number of the optimized target minimum workstations, the total idle time, the hazard index and the demand index.
TABLE 4 solving P25 results for HPSO and GASA
Figure BDA0003054614800000111
TABLE 5 ISSA solution P25 results
Figure BDA0003054614800000112
In contrast to the disassembly scheme, scheme 8 of the ISSA dominates scheme 4 of the HPSO, the others do not. Since 3 algorithms solveThe optimal number of stations of (3) is 9, so as shown in fig. 5, the Pareto front of the 3 algorithms contains only f2、f3And f4Three optimization objectives, as can be seen by combining tables 4 and 5, the algorithm ISSA solves f1And f2The optimal values of (A) are the same as those of HPSO and GASA and are both 9; f. of3And f4Are 70 and 802, respectively, better than the solving results 71 and 812 for HPSO and 73 and 811 for GASA. And the ISSA obtains 10 groups of non-inferior solutions, and the other two algorithms respectively obtain 5 groups and 6 groups of non-inferior solutions, which are both less than the number of the non-inferior solutions of the ISSA. As can be seen from comparative analysis, the solving quality and the solving quantity of the calculation method provided by the invention are superior to those of the GASA algorithm and the HPSO algorithm. By comprehensive analysis, ISSA can be effectively used for solving the DLBP with medium scale, and a better solution can be obtained.
2. Explanation of actual disassembly task effects:
the waste printer disassembly example of an enterprise comprises 55 disassembly tasks, and the established multi-target PDLBP model and the ISSA algorithm provided by the invention are applied to an incomplete disassembly line for disassembling the printer, so that a disassembly scheme which enables the number of work stations of the incomplete disassembly line to be minimum, the idle balance index to be minimum, the tool conversion times to be minimum and the disassembly energy consumption to be minimum is obtained. Table 6 shows data information of each disassembly task of the junked printer.
Table 6 data information of each disassembly task of the waste printer
Figure BDA0003054614800000121
Figure BDA0003054614800000131
And (3) testing results:
according to the actual disassembly condition of the printer, setting the preset beat time RT as 150s, and setting the energy consumption related parameters: c. Cu＝0.12kJ/s，ch＝0.03kJ/s，cd0.02kJ/s, consider a tool change interval tt of 2 s. The energy consumption of tool change in the actual disassembly process is obtained through the matrix (13). The program was run 10 times independently with an average run time of 135s, with the results of one run shown in table 7.
TABLE 7 ISSA Algorithm program independent run results
Figure BDA0003054614800000132
Figure BDA0003054614800000141
As can be seen from table 7, the maximum working time of the workstation is set to be the actual takt time, the optimal value for solving the idle time balance index is 0, the optimal value for the tool transformation times is 7, and the optimal value for the energy consumption is 54.272. Different schemes can be selected according to the requirements of a decision maker, and if the decision maker tends to have the minimum idle time balance index, a scheme 7 can be selected; if the decision maker prefers to have the least number of tool changes, option 3 may be selected; if the decision maker prefers to minimize energy consumption, option 2 may be selected; if the decision maker is more inclined to balance among the targets, schemes 1, 4 to 6 or 8 can be selected, and the actual takt time is set according to the solving situation, so that an efficient and accurate solving means is provided for solving the multi-target PDLBP problem, and the decision maker can conveniently select the disassembly line arrangement scheme most suitable for the actual production conditions.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the embodiments of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (9)

1. The setting method of the incomplete disassembly line under the condition of considering tool conversion energy consumption is characterized by comprising the following steps:
step 1, aiming at minimizing the number of the work stations to be started, an idle time balance index, tool replacement times and a disassembly energy consumption index, providing a constraint condition of the incomplete disassembly line balance problem, and establishing a target function of the incomplete disassembly line balance problem;
step 2, setting problem parameters and algorithm parameters, constructing an initial spider population according to the priority relation of products to be disassembled in the problem parameters, changing a feasible sequence disassembling sequence contained in a spider in a two-point cross operation mode after algorithm investigation, and changing a target value representing vibration of the sequence so as to enable an individual spider to vibrate;
step 3, performing iterative computation based on the vibrating spider individual in the step 2, randomly moving the spider individual, further improving a feasible disassembly sequence by adopting special mask changing operation in an iterative process, increasing additional vibration, improving the moving efficiency of the spider, improving the iterative computation rate on the whole, and introducing a special means for improving the overall optimization capability of the algorithm in the iterative process;
and 4, step 4: and screening the iteration calculation result of each time by using a Pareto elite strategy, selecting non-inferior individuals which accord with the target function and are not mutually dominant, storing the non-inferior individuals in an external archive set as a next iteration population or a non-inferior solution, and screening the non-inferior solution in the external archive set of the target function by using an NSGA-II congestion distance evaluation standard after the iteration calculation times meet a termination condition, and outputting the non-inferior solution as a final result.
2. The disassembly line setting method of claim 1, wherein in step 1, the objective function is:
F＝min[NS,IT,TR,EC]
wherein NS is the number of open workstations;
IT is an idle time balance index;
TR is the tool replacement times;
EC is the index of energy consumption for disassembly.
3. The disassembly line setting method of claim 1, wherein in step 1, the constraint conditions include a part disassembly constraint, a workstation task allocation constraint, a tact time constraint, a real tact time constraint, a workstation sequence activation constraint, and a disassembly task priority relationship constraint.
4. The disassembly line setting method of claim 1, wherein in step 2, the initial spider population is constructed by encoding each feasible set of task disassembly sequences as individual spiders in a real number encoding-based manner.
5. The disassembly line setting method of claim 4, wherein in the step 2, the two-point crossing operation is performed by randomly selecting two individual spiders in the spider population to define two paired spiders, randomly selecting the same two points on the feasible disassembly sequence of the paired spiders, mapping task numbers to each other and moving the positions of the two points so that the paired spiders respectively form a new disassembly sequence.
6. The disassembly line setting method of claim 1, wherein in step 3, the paired two detachable task sequences are compared and the different task numbers are deleted when the spider individual is moved randomly.
7. The disassembly line setting method of claim 1, wherein in step 3, the special mask changing operation used in the iterative calculation process is a single-point random insertion method.
8. The method for setting the disassembly line of claim 1, wherein in step 3, the special means for improving the global optimizing capability of the algorithm is to replace the number of individual spiders in the population, which is Pop _ num/10, with artificial spiders at iterations G/5, 2G/5, 3G/5 and 4G/5 of the algorithm.
9. The detach line setting method according to claim 1, wherein the spider individual calculates a sequence before and after the generation of the tool change using a tool change energy consumption matrix after the generation of the vibration.
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