CN114282370A - Disassembly line setting method considering physical and mental loads of operator - Google Patents
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Abstract
The invention discloses a method for setting a disassembly line by considering physical and mental loads of an operator, which comprises the following steps: collecting information of disassembly lines and disassembly tasks, and establishing a disassembly line balance problem mathematical model which takes the minimum number of the work stations to be started, idle time balance indexes and energy consumption balance indexes as targets and contains physical and mental load constraints of an operator; and solving the mathematical model. The invention considers the influence of the physical strength and mental load of an operator on the setting of the disassembly line, better conforms to the actual working condition of workers and can obtain a better disassembly line combination. The invention also provides an improved longicorn swarm algorithm aiming at the mathematical model, discretizes the longicorn stigma search algorithm, enables the algorithm to be suitable for processing discrete problems, and enhances the optimizing and converging performance of the algorithm by introducing concentration detection operation, step length moving operation, variation factors and the like.
Description
Technical Field
The invention relates to the technical field of facility layout, in particular to a method for setting a disassembly line by considering physical and mental loads of an operator.
Background
With the rapid advance of science and technology, the updating speed of electromechanical products (such as televisions, refrigerators, computers and the like) is continuously accelerated, and the treatment of waste electromechanical products is particularly important. The traditional treatment modes such as incineration, landfill and the like not only cause serious pollution to the environment but also waste resources, and the recycling, disassembly and reuse become the best treatment mode at present, and the traditional treatment modes are applied to large-scale disassembly enterprises.
Since the Problem of Disassembly Line Balancing (DLBP) is proposed, the Problem is highly concerned by academic circles at home and abroad, more and more scholars carry out deep and complex research on the Problem, Zhang and the like introduce fuzzy operation time by considering uncertainty factors in the Disassembly process and apply the fuzzy operation time to the consideration of multi-target DLBP; zhu et al have studied the uncertainty that its structure and quality exist in the dismantlement process of product, and above-mentioned research all takes dismantlement cost and efficiency as the main target, and all do not consider the problem that workman's physical power and mental load influence the workman in the dismantlement process.
The actual line of removal still takes manual removal as the main thing, and the workman dismantles not only expends physical strength but also expends mental, for example: large simple parts are disassembled, and the physical consumption of workers is huge; to disassemble complex parts, workers need to think about disassembly actions, postures, etc. in order to seek better disassembly comfort. The physical and mental loads of workers not only influence the disassembly efficiency and the disassembly error rate, but also influence the health of the workers. Therefore, it is necessary to take into account the physical and mental loads of the worker during the disassembly process.
Disclosure of Invention
In order to solve the problems, the invention mainly aims to provide a method for setting the disassembly line by considering the physical strength and mental load of an operator.
The technical scheme of the invention is as follows:
the disassembly line setting method considering the physical and mental loads of an operator comprises the following steps:
s1, collecting information of disassembly lines and disassembly tasks, and establishing a disassembly line balance problem mathematical model which takes the minimum number of the work stations to be started, idle time balance indexes and energy consumption balance indexes as targets and contains the physical and mental load constraints of an operator;
the objective function of the mathematical model is as follows:
F＝min[N,T,R]
wherein the content of the first and second substances,
in the formula, N is the number of the opened workstations; t is an idle time balance index; r is an energy consumption index; j is the workstation number; i is the total number of parts to be disassembled; j is the number of the workstations, and the upper limit value is I; sjIf the workstation is started S for judging the variable j1, otherwise S j0; CT is the production beat time of the workstation, unit s; ST (ST)jActual time for completing all tasks of the jth workstation in unit s; Δ E is the average energy consumption rate in kcal/min to complete the tasks in all workstations; ejTo finish the jth workerMaking the average energy consumption rate of all tasks in the station, wherein the unit is kcal/min; i is a task number, and I belongs to {1, 2.., I }; e.g. of the typeiEnergy consumption to complete task i, in kcal; x is the number ofijTo determine the variables, x if task i is assigned to the jth workstation ij1, otherwise xij＝0；
Objective function F1The fewer the number of open stations, the lower the disassembly cost.
Objective function F2The idle time balance index is smaller, and the work time of each workstation is more balanced.
Objective function F3For the energy consumption index of the workstation, the phenomenon that the energy consumption of workers is too large or too small due to uneven distribution of the disassembling tasks can be avoided only if the objective function is as small as possible.
The constraints of the mathematical model are as follows:
B＝(bil)I×I (17)
in the formula, TiIs the working time of the ith task, unit s; TTiThe unit of s is the working time for completing the ith task under the condition of satisfying human factors; STT (spin transfer torque)jThe actual time and unit s for completing all tasks of the jth workstation under the condition of satisfying human factors; beta is aiThe mental stiffness value of task i completed in unit time; beta is adownThe lower limit value of the mental rigidity value of the staff in the workstation is set; beta is aupThe upper limit value of the mental rigidity value of the staff in the workstation is set; b is a priority relation matrix; bilTo determine the variables, if task i is the task immediately preceding task l, b il1, otherwise b il0; the short names meeting the human factor condition are that the physical and mental loads of workers are met;
equation (6) is the workstation opening number range; the total time of the actual work task of the workstation does not exceed the beat time in the formula (7); equation (8) is the total job task time of the workstation; equation (9) can only be assigned to one workstation for each task; equation (10) to ensure that disassembly should satisfy the precedence constraint, equation (14) is a method of estimating worker energy consumption; equation (15) represents the total time of the job task when the worker completes the work station J in consideration of energy consumption on the premise of satisfying the requirement of the tact time; equation (16) represents the mental stiffness value constraint.
And S2, solving the mathematical model.
For solving mathematical models, DLBP has appeared various solving methods through continuous development, a heuristic algorithm is applied for the first time, and then the DLBP is gradually developed into a mathematical programming method and an intelligent algorithm. The solving difficulty of the traditional heuristic algorithm and mathematical programming method increases exponentially with the increase of the scale of the solving task. In recent years, the intelligent algorithm has the advantages of high solving efficiency, good optimizing effect and the like, and is widely applied to the combined optimization problems of workshop scheduling, Job-Shop scheduling, integrated process planning and scheduling, DLBP (digital living Back propagation) and the like. The Particle Swarm Optimization (PSO), Variable Neighborhood Search (VNS), Ant Colony Optimization (ACO), Simulated Annealing (SA), etc. are commonly used in solving combinatorial Optimization problems such as DLBP, etc., but the above methods still convert multiple targets into single target solution, and the single target solution can only obtain one feasible solution, and cannot provide multiple non-inferior solutions of different side targets for selection as the multi-target solution. The longhorn search algorithm is a biological heuristic algorithm proposed by Jiang et al in 2017, and abstracts the foraging principle of the longhorns into mathematical representation. The algorithm has the advantages of simple optimization mechanism, convenience and quickness in implementation, small operand and the like, and is applied to the problems of constraint combination optimization and 0-1 knapsack, and the like. In view of the above reasons, the present invention provides an Improved longicorn Swarm Algorithm (IBSA), discretizes the longicorn stigma search Algorithm, improves the optimization performance of the Algorithm by introducing a concentration detection operation, a step size moving operation and a mutation operation, and finally obtains a plurality of non-inferior solutions by using a Pareto solution set concept and a crowding distance mechanism screening. Specifically, the solving method comprises the following steps:
s20, setting problem parameters and algorithm parameters, wherein the problem parameters comprise preset beat time CT; the algorithm parameters comprise population size Pop _ num, maximum iteration times G and external file number N;
s21, obtaining task disassembly sequences by adopting a real number coding mode according to constraint conditions, and constructing an initial longicorn population by taking each group of task disassembly sequences as longicorn individuals;
s22, judging the feasibility of the longicorn individuals in the initial longicorn population as optimal solutions by means of an algorithm based on an objective function, screening the longicorn individuals with the optimal solution feasibility, and establishing an external archive Q of the longicorn population;
s23, concentration detection operation, namely taking one longicorn individual in the initial longicorn population, changing the disassembly sequence of the longicorn individual by a single-point directional insertion method to obtain a new longicorn individual, and screening the optimal longicorn individual from the new longicorn individual and the original longicorn individual according to a Pareto elite strategy and crowding distance so as to move the longicorn individual to a position with high information concentration;
s24, step length moving operation, namely, selecting the longicorn individuals screened in the step S23 and the longicorn individuals in one external file Q, changing the disassembly sequence of the longicorn individuals through two-point crossing operation to obtain new longicorn individuals, and screening the longicorn individuals through a Pareto elite strategy and a crowded distance mechanism;
s25, performing mutation operation, namely taking the longicorn individuals screened in the step S24 and the longicorn individuals in one external file Q, changing the disassembly sequence of the longicorn individuals by a single-point random insertion method, screening the longicorn individuals by crowding distance, and updating the external file Q by using the obtained longicorn individuals;
s26, selecting the screened longicorn individuals in the step S23, sequentially selecting one longicorn individual of the external file Q, and repeating the steps S24-S25;
s27, sequentially taking the longicorn individuals in the initial longicorn population, and repeating the steps S23-S26 to update the external file Q;
s28, updating the initial longicorn population by using the external file Q and the original longicorn population, repeating the steps S24-S27 to iterate until the iterative computation times meet the termination condition, and screening out non-inferior solutions in the external file Q of the target function as final output by using an NSGA-II congestion distance evaluation standard;
the specific process for updating the initial longicorn population is as follows:
if the number of the longicorn individuals in the external file Q is equal to the number of the longicorn individuals in the initial longicorn population, replacing the longicorn individuals in the longicorn population with the longicorn individuals in the external file;
if the number of the longicorn individuals in the external file Q is smaller than that in the initial longicorn population, generating missing longicorn individuals randomly;
and if the number of the longicorn individuals in the external file Q is larger than that in the initial longicorn population, selecting the longicorn individuals from the external file Q by adopting the crowding distance to replace the longicorn individuals in the initial longicorn population.
The invention has the technical effects that:
(1) the invention considers the influence of the physical strength and mental load of an operator on the setting of the disassembly line, better conforms to the actual working condition of workers and can obtain a better disassembly line combination.
(2) Aiming at the model established by the patent, an improved longicorn swarm algorithm is provided, the longicorn stigma search algorithm is discretized, the algorithm is suitable for processing discrete problems, and concentration detection operation, step length moving operation, variation factors and the like are introduced to enhance the optimizing and converging performance of the algorithm.
Drawings
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 flow chart for solving a disassembly line setup problem that takes into account operator physical and mental loads;
FIG. 2 is a schematic diagram of a new longicorn individual obtained by changing the disassembly sequence of a longicorn individual by a single point directional insertion method;
fig. 3 is an exemplary diagram of a new longicorn individual obtained by changing the disassembly sequence of the longicorn individual by a two-point intersection operation;
FIG. 4 is a comparison graph of solving effects of the IBSA algorithm and the similar algorithm;
FIG. 5 is a graph comparing the results of GA and IBSA calculations, where graph a is the target value F2Is compared with the mean value of the target value F, and the graph b is the target value F3The most and mean of (c) are compared.
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.
Examples
The calculation formula of this embodiment relates to a plurality of symbols, and please refer to Table 1 for each symbol definition
TABLE 1 symbol definition description table
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.
The method comprises the following specific steps:
F＝min[N,T,R]
objective function F1In order to be able to switch on the number of stations,the fewer the number of opening stations, the lower the disassembly cost.
Objective function F2The idle time balance index is smaller, and the work time of each workstation is more balanced.
Objective function F3For the energy consumption index of the workstation, the phenomenon that the energy consumption of workers is too large or too small due to uneven distribution of the disassembling tasks can be avoided only if the objective function is as small as possible.
Wherein the content of the first and second substances,
the energy consumption and the mental stiffness value of the operator can be obtained according to the actual running state of the disassembly line.
Constraint conditions are as follows:
B＝(bil)I×I (17)
equation (6) is the workstation opening number range; the total time of the actual work task of the workstation does not exceed the beat time in the formula (7); equation (8) is the total job task time of the workstation; equation (9) can only be assigned to one workstation for each task; equation (10) to ensure that disassembly should satisfy the precedence constraint, equation (14) is a method of estimating worker energy consumption; equation (15) represents the total time of the job task when the worker completes the work station J in consideration of energy consumption on the premise of satisfying the requirement of the tact time; equation (16) represents the mental stiffness value constraint.
s20, actually setting problem parameters and algorithm parameters according to the PDLBP problem, wherein the problem parameters comprise preset beat time CT; the algorithm parameters comprise population size Pop _ num, maximum iteration number G and external file number N.
S21, obtaining a plurality of groups of task disassembly sequences by adopting a real number coding mode according to constraint conditions, and constructing an initial longicorn population by taking each group of task disassembly sequences as longicorn individuals;
specifically, the initial longicorn population uses each group of feasible task disassembly sequences obtained by encoding as a longicorn individual in a real number encoding-based manner, a "0" element is used in the encoding process to replace an unassigned task position, and each group of feasible task disassembly sequences can obtain a corresponding feasible sequence target value in a decoding manner.
S22, judging the feasibility of the longicorn individuals in the initial longicorn population as optimal solutions by means of an algorithm based on an objective function, screening the longicorn individuals with the optimal solution feasibility, and establishing an external archive Q of the longicorn population;
and S23, concentration detection operation, namely taking one longicorn individual in the initial longicorn population, changing the disassembly sequence of the longicorn individual by a single-point directional insertion method to obtain a new longicorn individual, and screening the optimal longicorn individual from the new longicorn individual and the original longicorn individual according to a Pareto elite strategy and crowding distance so as to move the longicorn individual to a position with high information concentration.
Specifically, referring to fig. 2, fig. 2 is an exemplary diagram of a new longicorn individual obtained by changing a disassembly sequence of the longicorn individual by a single point directional insertion method, which includes the following steps:
randomly selecting a disassembly task in a disassembly sequence of the longicorn individuals, recording the position of the task in the whole disassembly sequence (from the first task, the positions corresponding to the tasks are 1,2 and 3 degrees in sequence), traversing the whole disassembly sequence of the longicorn individuals, finding the tasks immediately before and after the task, and recording the positions corresponding to the tasks immediately before and after in the whole disassembly sequence (from the first task, the positions corresponding to the tasks are 1,2 and 3 degrees in sequence);
according to the priority relation of task disassembly, the position of the randomly selected task before and after changing is required to be behind the position of the task immediately before the task and in front of the position of the task immediately after the task; and operating the task according to the priority relation of the disassembly task: randomly inserting the task between the position of the task and the position of the task immediately before the task to generate a new sequence, namely the cattle individuals on the first day; and randomly inserting the task between the position of the task and the position of the task immediately after the task to generate another new sequence, namely the second longicorn individual.
S24, selecting the longicorn individuals screened in the step S23 and the longicorn individuals in one external file Q, changing the disassembly sequence of the longicorn individuals through two-point intersection operation to obtain new longicorn individuals, and screening the longicorn individuals through a Pareto elite strategy and a crowding distance mechanism;
specifically, referring to fig. 3, fig. 3 is an exemplary diagram of a new longicorn individual obtained by changing the disassembly sequence of the longicorn individual through a two-point crossing operation, and a specific method for changing the disassembly sequence of the longicorn individual through the two-point crossing operation is as follows: matching the 1 longicorn individuals screened in the step S23 with 1 longicorn individual randomly selected from the external file Q, and respectively numbering the longicorn individuals into S and M; randomly selecting two task numbers in the longicorn individuals S, wherein the two task numbers and the task number between the two task numbers form a sequence segment to be mutated; rearranging the positions of all task numbers in the sequence segment to be mutated according to the front-back sequence of the task numbers in the longicorn individual M to obtain an updated longicorn individual S; randomly selecting two task numbers from the longicorn individuals M, wherein the two task numbers and the task number between the two task numbers form a sequence segment to be mutated; and rearranging the positions of the task numbers in the sequence segment to be mutated according to the front-back sequence of the task numbers in the longicorn individual S to obtain an updated longicorn individual M.
S25, selecting the longicorn individuals screened in the step S24 and the longicorn individuals in one external file Q, changing the disassembly sequence of the longicorn individuals by a single-point random insertion method, screening the longicorn individuals by crowding distance, and updating the external file Q by using the screened longicorn individuals;
specifically, the specific method for changing the disassembly sequence of the longicorn individual by the single-point random insertion method is as follows: randomly selecting a task m on a feasible disassembly sequence of the longicorn individual, finding an immediately preceding task and an immediately succeeding task of the task, randomly inserting the task m into any position between the immediately preceding task and the immediately succeeding task, and ensuring that the positions of the immediately preceding task and the immediately succeeding task before and after insertion are not changed, thereby further updating the longicorn individual and playing a role in preventing the algorithm from falling into local optimization.
S26, selecting the screened longicorn individuals in the step S23, sequentially selecting one longicorn individual of the external file Q, and repeating the steps S24-S25;
s27, sequentially taking the longicorn individuals in the initial longicorn population, and repeating the steps S23-S26 to update the external file Q;
s28, updating the initial longicorn population by using the external file Q updated in the step S27 and the initial longicorn population; and repeating the steps S23-S27 to iterate until the iterative computation times meet the termination condition, and screening out non-inferior solutions in the external file Q of the objective function as final output by utilizing the NSGA-II congestion distance evaluation criterion.
Specifically, the number of longhorn beetle individuals in the initial longhorn beetle population is not changed before and after the initial longhorn beetle population is updated, and the specific process for updating the initial longhorn beetle population is as follows:
if the number of the longicorn individuals in the external file Q is equal to the number of the longicorn individuals in the initial longicorn population, replacing the longicorn individuals in the longicorn population with the longicorn individuals in the external file;
if the number of the longicorn individuals in the external file Q is smaller than that in the initial longicorn population, generating missing longicorn individuals randomly;
and if the number of the longicorn individuals in the external file Q is larger than that in the initial longicorn population, selecting the longicorn individuals from the external file Q by adopting the crowding distance to replace the longicorn individuals in the initial longicorn population.
The following is the programmed test environment of this embodiment:
the improved running environment of the Tianniu group algorithm is to adopt MATLAB R2018b to develop an IBSA algorithm program and run the program in a computer environment with Intel (R) core (TM) i5-7400CPU, 3.0GHz main frequency and 4GB internal memory.
1. Comparing the effects of similar algorithms:
using the same type of algorithm: the Ant Colony Algorithm, the Genetic simulated annealing Algorithm, the Ant Colony Genetic Algorithm (ACGA), and the Artificial Fish Colony Algorithm (AFSA) solve the large-scale instance P52 containing 52 handset disassembly tasks, wherein f1、f2、f3Respectively represent the optimized target smoothing rates FidleAverage idle rate FsmoothAnd a disassembly cost FcostThrough a large number of experimental tests, the preferred parameters are as follows: the population size pop _ num is 160, the maximum iteration number Max _ gen is 280, the variation probability pm is 0.7, the longicorn must distance d is 4, the external file size NN is 10, the algorithm is run for 10 times, and the better one of the results is taken. In consideration of comparability, the three targets in the original document are still solved, and the solution results are shown in table 2, and the optimal value of each target is shown in bold.
TABLE 2 IBSA Algorithm solving P52 results
| No | Fidle | Fsmooth | Fcost |
| 1 | 0.0579 | 0.9999 | 141.600 |
| 2 | 0.0579 | 0.8934 | 127.056 |
| 3 | 0.0579 | 0.9252 | 127.146 |
| 4 | 0.0579 | 0.9253 | 127.608 |
| 5 | 0.0579 | 0.9970 | 129.096 |
| 6 | 0.0579 | 0.9985 | 137.808 |
| 7 | 0.0579 | 0.9979 | 130.578 |
| 8 | 0.0579 | 0.9921 | 128.268 |
| 9 | 0.0579 | 0.9644 | 128.178 |
As can be seen from the results analysis in Table 2, the smoothing rate of the 9 results obtained by the IBSA algorithm is between 0.8934 and 0.9999, the average idle rate is 0.0579, and the disassembly cost is between 127.056 RMB/station and 141.60 RMB/station.
Comparing the solution result of the IBSA algorithm with the solution results of the 4 algorithms, and considering that the solution idle rate results of the algorithms are all 0.0579, establishing a two-dimensional rectangular coordinate system to compare two objective functions of the load balancing index and the disassembly cost so as to analyze the advantages and disadvantages of the algorithms as shown in FIG. 4.
It can be clearly seen from fig. 4 that the results obtained by the IBSA algorithm surround the results obtained by the other 4 algorithms, which can show that the IBSA algorithm is better for solving the large-scale problem than the other 4 algorithms compared herein.
2. Explanation of actual disassembly task effects:
the waste printer disassembly example of a certain enterprise comprises 55 disassembly tasks, and the established multi-target DLBP model and the IBSA algorithm provided by the invention are applied to the disassembly line for disassembling the printer, so that the disassembly scheme with the least number of the work stations of the disassembly line, the lowest idle balance index and the least energy consumption index is obtained. Table 3 shows data information of each disassembly task of the waste printer.
TABLE 3 Detachable information Table
The production beat of the printer is 156 seconds, the parameter settings of the IBSA algorithm and the GA algorithm are the same as those of the P52 calculation example, the two algorithms are operated 10 times, the average operation time of the GA algorithm is 325.2s, the average operation time of the IBSA algorithm is 302.1s, the better one of the operation times is shown in the table 4, and the optimal data are shown in a bold mode.
TABLE 4 IBSA and GA task protocol (considering worker physical and mental loads)
1-10 are data obtained by GA algorithm, 11-18 are data obtained by IBSA algorithm, 18 groups of data Pareto obtained in table 4 are screened, and a plurality of groups of non-inferior solutions are obtained as shown in table 5.
TABLE 5 Pareto screening results
| Numbering | Algorithm | | F2 | F3 | |
| 1 | |
5 | 1906 | 0.0019 | |
| 2 | |
5 | 1516 | 0.0056 | |
| 3 | |
5 | 1500 | 0.0397 | |
| 4 | |
5 | 1480 | 24.5415 | |
| 5 | |
5 | 1612 | 0.0020 | |
| 6 | |
5 | 1486 | 0.4793 | |
| 7 | |
5 | 1498 | 0.4723 | |
| 8 | |
5 | 1482 | 20.8091 |
F obtained by GA algorithm and IBSA algorithm1All the optimal target values of (1) are 5, and F obtained by using GA algorithm and IBSA algorithm2And F3The results of (a) were compared and the maximum value, the minimum value and the average value were compared, respectively, as shown in fig. 5.
From the analysis in Table 5 and FIG. 5, it can be seen that the results obtained by IBSA dominate the results obtained by GA in Table 5, and that the results obtained by IBSA are at the target value F in FIG. 52And F3Therefore, the solving performance of the IBSA is better than that of the GA aiming at the problem provided by the method.
A group of schemes are randomly selected from the schemes obtained by the IBSA algorithm to analyze the influence of the physical and mental loads of the workers as shown in a table 6, wherein the physical and mental loads of the workers are considered in the table, the physical and mental loads of the workers are not considered, and data exceeding the physical and mental loads of the workers are represented in a bold mode.
Under the condition of considering the energy consumption and the mental rigidity value of workers, the physical and mental loads of the workers in 5 workstations are in a bearable range; under the condition that the assigned work tasks are the same, under the condition that the energy consumption and the mental rigidity value of workers are not considered, the energy consumption of the workers in the work stations 1,2 and 3 is large, the workers are easy to fatigue, and the long-time overload work can cause the efficiency reduction and even cause the misoperation to cause danger due to insufficient physical strength.
TABLE 6 comparison of worker physical and mental stiffness values
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 (6)
1. A disassembly line setting method considering physical and mental loads of an operator is characterized by comprising the following steps:
s1, collecting information of disassembly lines and disassembly tasks, and establishing a disassembly line balance problem mathematical model which takes the minimum number of the work stations to be started, idle time balance indexes and energy consumption balance indexes as targets and contains the physical and mental load constraints of an operator;
the objective function of the mathematical model is as follows:
F＝min[N,T,R]
wherein the content of the first and second substances,
in the formula, N is the number of the opened workstations; t is an idle time balance index; r is an energy consumption index; j is the number of workstations; j is the number of the workstation, J belongs to (1, 2, …, J); i is the total number of parts to be disassembled; the upper limit value is I; sjIf the workstation is started S for judging the variablej1, otherwiseSj0; CT is the production beat time of the workstation, unit s; ST (ST)jActual time for completing all tasks of the jth workstation in unit s; Δ E is the average energy consumption rate in kcal/min to complete the tasks in all workstations; ejAverage energy consumption rate in kcal/min for all tasks in the jth workstation; i is a task number, and I belongs to {1, 2.., I }; e.g. of the typeiEnergy consumption to complete task i, in kcal; x is the number ofijTo determine the variables, x if task i is assigned to the jth workstationij1, otherwise xij＝0；
The constraints of the mathematical model are as follows:
B＝(bil)I×I (17)
in the formula, TiIs the working time of the ith task, unit s; TTiThe unit of s is the working time for completing the ith task under the condition of satisfying human factors; STT (spin transfer torque)jThe actual time and unit s for completing all tasks of the jth workstation under the condition of satisfying human factors; beta is aiThe mental stiffness value of task i completed in unit time; beta is adownThe lower limit value of the mental rigidity value of the staff in the workstation is set; beta is aupThe upper limit value of the mental rigidity value of the staff in the workstation is set; b is a priority relation matrix; bilTo determine the variables, if task i is the task immediately preceding task l, bil1, otherwise bil＝0；
And S2, solving the mathematical model.
2. The disassembly line setting method considering the physical and mental loads of the operator as claimed in claim 1, wherein said step S2 comprises the steps of:
s20, setting problem parameters and algorithm parameters, wherein the problem parameters comprise preset beat time CT; the algorithm parameters comprise population size Pop _ num, maximum iteration times G and external file number N;
s21, obtaining task disassembly sequences by adopting a real number coding mode according to constraint conditions, and constructing an initial longicorn population by taking each group of task disassembly sequences as longicorn individuals;
s22, judging the feasibility of the longicorn individuals in the initial longicorn population as optimal solutions by means of an algorithm based on an objective function, screening the longicorn individuals with the optimal solution feasibility, and establishing an external archive Q of the longicorn population;
s23, taking one longicorn individual from the initial longicorn population, changing the disassembly sequence of the longicorn individual by a single-point directional insertion method to obtain a new longicorn individual, and screening the longicorn individual from the new longicorn individual and the original longicorn individual according to a Pareto elite strategy and crowding distance;
s24, selecting the longicorn individuals screened in the step S23 and the longicorn individuals in one external file Q, changing the disassembly sequence of the longicorn individuals through two-point intersection operation to obtain new longicorn individuals, and screening the longicorn individuals through a Pareto elite strategy and a crowding distance mechanism;
s25, selecting the longicorn individuals screened in the step S24 and the longicorn individuals in one external file Q, changing the disassembly sequence of the longicorn individuals by a single-point random insertion method, screening the longicorn individuals by crowding distance, and updating the external file Q by using the screened longicorn individuals;
s26, selecting the screened longicorn individuals in the step S23, sequentially selecting one longicorn individual of the external file Q, and repeating the steps S24-S25;
s27, sequentially taking the longicorn individuals in the initial longicorn population, and repeating the steps S23-S26 to update the external file Q;
s28, updating the initial longicorn population by using the external file Q updated in the step S27 and the initial longicorn population; and repeating the steps S23-S27 to iterate until the iterative computation times meet the termination condition, and screening out non-inferior solutions in the external file Q of the objective function as final output by utilizing the NSGA-II congestion distance evaluation criterion.
3. The disassembly line setting method considering the physical and mental loads of the operator as claimed in claim 2, wherein said obtaining a new celestial cow individual by changing the disassembly sequence of celestial cow individuals through the single point directional insertion method in step S23 comprises the steps of:
randomly selecting a disassembly task in the longicorn individual disassembly sequence, recording the position of the disassembly task in the whole disassembly sequence, traversing the whole longicorn individual disassembly sequence, finding the tasks immediately before and immediately after the disassembly task and recording the positions of the tasks immediately before and immediately after in the whole disassembly sequence;
under the condition of meeting the priority relation of the disassembly task, moving the disassembly task to the task direction immediately before the disassembly task to generate a first day ox individual; and moving the disassembly task to the direction of the task immediately after the disassembly task to generate the second longicorn individual.
4. The disassembly line setting method considering the physical and mental loads of the operator as set forth in claim 2, wherein the obtaining of the new celestial cow individual by changing the disassembly sequence of the celestial cow individual through the two-point crossing operation in step S24 comprises the steps of: pairing the 1 longicorn individuals screened in the step S23 with 1 longicorn individual randomly selected from an external file Q, and respectively numbering the longicorn individuals into S and M; randomly selecting two task numbers in the longicorn individuals S, wherein the two task numbers and the task number between the two task numbers form a sequence segment to be mutated; rearranging the positions of all task numbers in the sequence segment to be mutated according to the front-back sequence of the task numbers in the longicorn individual M to obtain an updated longicorn individual S; randomly selecting two task numbers from the longicorn individuals M, wherein the two task numbers and the task number between the two task numbers form a sequence segment to be mutated; and rearranging the positions of the task numbers in the sequence segment to be mutated according to the front-back sequence of the task numbers in the longicorn individual S to obtain an updated longicorn individual M.
5. The disassembly line setting method considering the physical and mental loads of the operator as set forth in claim 2, wherein the obtaining of the new longicorn individual by changing the disassembly sequence of the longicorn individual by the single point random insertion method in step S25 comprises the steps of: randomly selecting a disassembly task m on a feasible disassembly sequence of the longicorn individuals, finding an immediately preceding task and an immediately succeeding task of the disassembly task, and randomly inserting the disassembly task m into any position between the immediately preceding task and the immediately succeeding task.
6. The disassembly line setting method considering the physical and mental loads of the operator as set forth in claim 2, wherein said updating the initial longicorn population in step S30 comprises the steps of:
if the number of the longicorn individuals in the external file Q is equal to the number of the longicorn individuals in the initial longicorn population, replacing the longicorn individuals in the longicorn population with the longicorn individuals in the external file;
if the number of the longicorn individuals in the external file Q is smaller than that in the initial longicorn population, generating missing longicorn individuals randomly;
and if the number of the longicorn individuals in the external file Q is larger than that in the initial longicorn population, selecting the longicorn individuals from the external file Q by adopting the crowding distance to replace the longicorn individuals in the initial longicorn population.
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