A method for setting up remanufacturing disassembly lines with multiple fault constraints based on polychromatic set method

Abstract

A method for setting up a remanufacturing disassembly line based on a polychromatic set method for solving multiple fault constraints relates to the technical field of disassembly line optimization, and mainly includes the following steps: collecting disassembly line and disassembly task information, fault information and other data, with the goal of minimizing the number of sites, idle index, component remanufacturing value index, and disassembly cost, proposing a disassembly line balance problem, and establishing a corresponding objective function based on constraints; setting problem parameters, algorithm parameters and external archives Q, and according to the priority relationship of the products to be disassembled in the problem parameters, encoding and generating the initial population, iterative calculation, and performing crossover and mutation operations on the individuals in the initial population in turn, using the Pareto elite strategy to update the external archive set, using the NSGA‑II crowding distance mechanism to screen out non-inferior solutions, and outputting iterative results that meet the output conditions. The present invention constructs a polychromatic set method to solve the remanufacturing disassembly line under multiple fault constraints, and provides a new disassembly strategy for improving the efficiency of the disassembly line and recycling.

Description

Technical Field

[0001] The invention relates to the technical field of disassembly line optimization, and in particular to a method for setting a disassembly line for remanufacturing based on a polychromatic set method to solve multiple fault constraints. Background Art

[0002] The circular economy has attracted worldwide attention for its potential contribution to green sustainable development and a harmonious society. Remanufacturing is a key strategy for the recovery and recycling of waste products, which can effectively improve resource utilization efficiency, reduce resource waste and achieve clean production. Disassembly is the first step in the remanufacturing process, which directly affects the remanufacturing efficiency and the recycling of retired products. The disassembly line is the most effective way to handle large quantities of waste products. Compared with single-piece disassembly, the disassembly line has the advantages of automation and high efficiency, and is widely used by disassembly companies to recycle large retired products. The automotive manufacturing industry is one of the industries with the largest consumption of metal resources, and extracting raw materials from recycled and reused resources can significantly improve the environment.

[0003] The automotive parts industry has promoted the innovation of remanufacturing retired products and reduced the environmental and social impacts of the manufacturing process. Unlike the traditional disassembly line balancing problem, the remanufacturing disassembly line balancing problem aims to obtain high-value automotive parts with minimal cost and environmental impact. A large amount of uncertain fault information and dangerous attributes are hidden in scrapped car data, which is closely related to the remanufacturing disassembly line. In engineering practice, automobile engines often have multiple faults such as aging, wear and deformation. There is also a complex coupling relationship between these failure modes and information such as scrapping modes. Polychromatic set theory is an information processing tool proposed in 2002. It plays a role in the conceptual design of mechanical products, and the method is convenient for programming. However, the existing polychromatic set theory mainly focuses on product design and does not involve retired products. For example, automobile engines often have multiple faults such as aging, wear and deformation. There is also a complex coupling relationship between these failure modes and scrapping modes and other information. Previous studies have not considered the impact of multiple faults of retired automobile engines on the remanufacturing disassembly line, which leads to a decrease in disassembly efficiency. Therefore, solving the multi-fault constrained remanufacturing disassembly line setting method based on the polychromatic set method is of great significance for the recycling and reuse of scrapped vehicles. Summary of the invention

[0004] In view of this, the main purpose of the present invention is to provide a method for setting up a remanufacturing disassembly line based on a polychromatic set method to solve multiple fault constraints. Multiple fault information will also be fed back to the designer to improve disassembly efficiency and design decisions, thereby improving the disassembly and remanufacturing of new generation products or similar products, and providing a new disassembly strategy for improving disassembly efficiency and recycling.

[0005] The technical solution of the present invention is a method for solving multi-fault constraint remanufacturing disassembly line setting based on a polychromatic set method, comprising the following steps:

[0006] Step S1: With the goal of minimizing the number of sites, idle index, component remanufacturing value index, and disassembly cost, the disassembly line balance problem is proposed, and the corresponding objective function is established based on the constraints.

[0007] Step S2: setting the problem parameters, algorithm parameters and external file Q, and encoding and generating the initial population according to the priority relationship of the products to be disassembled in the problem parameters;

[0008] Step S3: start iterative calculation for the initial population, wherein the individuals in the initial population are subjected to crossover and mutation operations in sequence, the population after the operations is updated, and the external archive set is updated using the Pareto elite strategy;

[0009] Step S4: Determine whether the current number of iterations exceeds the maximum number of iterations. If so, use the NSGA-II crowding distance mechanism to screen out non-inferior solutions and output the iteration results. If not, it is considered that one iteration is completed, and return to step S3 to continue iterative calculation until the output conditions are met.

[0010] The technical effects of the present invention are:

[0011] (1) A multi-color set method is constructed to solve the remanufacturing disassembly line under multiple fault constraints, which provides a new disassembly strategy for improving the efficiency and recycling of the disassembly line. Multiple fault information is also fed back to the designer to improve disassembly efficiency and design decisions, thereby improving the disassembly and remanufacturing of the new generation of products or similar products.

[0012] (2) In order to feed back multiple faults to the disassembly line, a mathematical model of the remanufacturing disassembly line based on multiple fault feedback and multi-color sets was established. This model optimized indicators such as the number of workstations, stability, and cost while taking into account component failure information.

[0013] (3) According to the characteristics of the model, an improved genetic algorithm is designed to solve the problem efficiently. Two different crossover operations are designed to ensure that the priority constraints, recycling constraints and danger constraints are satisfied, thereby improving the efficiency of the algorithm. Then, the interference factor is introduced to reduce the overall probability of falling into the local optimum.

[0014] (4) Three disassembly cases of small, medium and large scales were selected to verify the proposed method and algorithm, and the solution of the multi-color set method for remanufacturing disassembly line under multi-fault constraints was obtained. The results show that the proposed method can improve the disassembly efficiency and is more in line with engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below.

[0016] Figure 1 It is a flow chart of the improved genetic algorithm of the present invention;

[0017] Figure 2 It is a schematic diagram of the crossover operation in the present invention;

[0018] Figure 3 It is a schematic diagram of the mutation process in the present invention;

[0019] Figure 4 It is a schematic diagram of the structure of the multiple fault transmission chain in the present invention;

[0020] FIG5 is a multi-group disassembly scheme considering multiple faults in the present invention; DETAILED DESCRIPTION

[0021] The present invention will be further described in detail below in conjunction with the embodiments and drawings.

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Example:

[0024] A method for solving multi-fault constraint remanufacturing disassembly line setting based on a polychromatic set method comprises the following steps:

[0025] Step S1: With the goal of minimizing the number of stations, idle index, component remanufacturing value index, and disassembly cost, the disassembly line balance problem is proposed, and the corresponding objective function is established based on the constraints;

[0026] Wherein, the objective function is as shown in formula (1)-(5):

[0027] minf1=N (1)

[0028] In formula (1), N is the number of open workstations.

[0029]

[0030] In formula (2), CT is the cycle time; i is the disassembly task; r is the workstation number; M is the number of disassembly tasks; t iis the disassembly time required for disassembly task i; x ir represents a 0-1 variable. If task i is assigned to workstation r, then x ir =1; otherwise x ir =0;

[0031]

[0032] In formula (3): L i is the position of the i-th component to be disassembled on the disassembly line; P i It represents the recycling decision of task i and refers to the remanufacturing value of the i-th disassembly task.

[0033]

[0034] In formula (4): C i represents the disassembly cost information of the i-th disassembly task component; AT represents the actual cycle time; C r represents the unit time cost of opening a workstation; M is the number of disassembly tasks;

[0035] Therefore, the multi-objective function of the balance optimization of the multi-fault constrained remanufacturing disassembly line is:

[0036] minF=(f1,f2,f3,f4) (5)

[0037] Its constraints are shown in equations (6)-(12):

[0038]

[0039] Among them, x ir represents a 0-1 variable. If task i is assigned to workstation r, then x ir =1; otherwise x ir =0; M is the number of disassembly tasks;

[0040]

[0041] S r represents the set of tasks in the rth workstation; M is the number of disassembly tasks;

[0042]

[0043] Among them, x ir represents a 0-1 variable. If task i is assigned to workstation r, then x ir =1; otherwise x ir =0.

[0044]

[0045] Among them, x irrepresents a 0-1 variable. If task i is assigned to workstation r, then x ir =1; otherwise x ir =0; t i is the disassembly time required for disassembly task i; CT is the cycle time;

[0046]

[0047] Among them, S ij Represents the priority relationship between two tasks. If task i is the direct predecessor of task j, then S ij =1, otherwise, S ij =0;

[0048]

[0049] Among them, t i is the disassembly time required for disassembly task i; CT is the cycle time; M is the number of disassembly tasks; N is the number of open workstations;

[0050]

[0051] Among them, h ij is the constructed recycling matrix element;

[0052] The constraint condition represented by equation (12) is determined based on the polychromatic set method, which specifically includes the following steps:

[0053] Step 1: Based on the relationship table between part status and fault information, a hierarchical multiple fault transmission chain is constructed to quantitatively determine the relationship between parts and fault characteristics.

[0054] Multiple fault transmission chains can be found in Figure 4 , based on the hierarchical and coupled characteristics of retired product failures, this transmission chain is used to describe the mapping relationship between product part failure information and recycling decisions and disassembly modes. This transmission chain consists of failure characteristics, failure degree, scrapping reasons, and recycling decisions. For specific content, please refer to the relationship table between part status and failure information shown in Table 1. The specific status of the part can be specifically judged by the content shown in Table 1:

[0055] Table 1 Relationship between parts status and fault information

[0056]

[0057] Among them, the specific quantitative method for the relationship between parts and fault characteristics is the expert scoring method, and its specific steps are as follows:

[0058] Step 1): Invite experts to rank the importance of the fault characteristics of each product.

[0059] Step 2): Construct an indicator set, collect expert opinions, and construct an expert opinion matrix, as shown in formula (13):

[0060]

[0061] In the formula, a xy (x=1,2,…,z;y=Δa,Δb,Δc,Δt,Δd,Δq) represents the importance ranking of the yth indicator by the xth expert, and the value range is (1,2,…,n).

[0062] Calculate the membership degree b xj As shown in formula (14):

[0063]

[0064] In the formula, u is the conversion parameter, and u=n+2 is usually set.

[0065] Determine z experts for indicator u xy The average degree of awareness y The calculation of is shown in formula (15):

[0066]

[0067] Its blindness Q y The calculation of is shown in formula (16):

[0068]

[0069] Then z experts have different opinions on indicator u y Calculation of the overall recognition degree p xy As shown in formula (17):

[0070] p y =(1-b y )(1-Q y ),p y >0 (17)

[0071] The evaluation vector of z experts on all indicators U is shown in formula (18):

[0072] P=[p1,p2,…,p y ] (18)

[0073] Step 3): Normalize equation (18) to get the index u j The weight of is shown in formula (19):

[0074]

[0075] Then, according to formula (20), the fault characteristic value e of the product component is calculated as:

[0076]

[0077] In the formula, α y (y=1,2,…,8) is the weight corresponding to each fine-grained indicator calculated by applying the structural entropy weight method.

[0078] Depend on Figure 4 It can be seen that the multiple fault transmission chain is progressively related by fault characteristics, fault degree, scrapping reasons, and recycling decisions in a hierarchical manner, and the expert scoring method is used to conduct a qualitative evaluation.

[0079] Step 2: Based on the relationship between the parts of the product to be disassembled and the fault feature layer obtained by the expert scoring method, a matrix is ​​constructed, and the matrix is ​​used as the Boolean matrix of the fault feature layer. The surrounding channel matrix of the fault feature layer is obtained by using the expert scoring method.

[0080] The Boolean matrix of the fault feature layer is shown in formula (22):

[0081] C V×Fi(v) ={C if} (twenty two)

[0082] In formula (22), C if is a 0-1 variable. When task i has a fault, C if =1; otherwise C if =0; the specific composition is determined by the above-mentioned expert scoring method.

[0083] The surrounding channel matrix formula of the fault feature layer is shown in formula (23):

[0084]

[0085] Where C fc is a 0-1 variable. When the fault f reaches the fault level c, C fc =1; otherwise C fc =0; the specific composition is determined by the above-mentioned expert scoring method.

[0086] Step 3: Rely on the polychromatic reasoning formula to calculate the correlation matrix between each part of the product to be disassembled and the fault feature layer, and use the correlation matrix as the Boolean matrix of the next layer, namely the fault degree layer, where the polychromatic reasoning formula is shown in formula (21):

[0087] Among them, the multi-color reasoning formula is shown in formula (21):

[0088]

[0089] In the formula, is the Boolean matrix of the current layer of the multiple fault transmission chain; is the peripheral channel matrix of the current layer of the multiple fault transmission chain, It is the correlation matrix between the previous layer and the current layer in the multiple fault transfer chain.

[0090] Step 4: Use the expert scoring method to obtain the surrounding channel matrix of the next layer, i.e., the fault degree layer. Combined with the Boolean matrix of the next layer, i.e., the fault degree layer, obtained in step 3, the correlation matrix between the current layer, i.e., the fault feature layer, and the next layer, i.e., the fault degree layer, is calculated by formula (21). The correlation matrix is ​​used as the Boolean matrix of the next layer, i.e., the scrap cause layer. This process is repeated until the Boolean matrix of the recycling decision layer is calculated. The Boolean matrix of the recycling decision layer is used to guide the recycling decision.

[0091] Step S2: setting the problem parameters, algorithm parameters and external file Q, and encoding and generating the initial population according to the priority relationship of the products to be disassembled in the problem parameters;

[0092] Step S3: start iterative calculation for the initial population, wherein the individuals in the initial population are subjected to crossover and mutation operations in sequence, the population after the operations is updated, and the external archive set is updated using the Pareto elite strategy;

[0093] The traditional crossover operation is random and will generate a large number of infeasible solutions, so the crossover operation used in step S3 is a combination of two-point crossover and three-point crossover. Half of the time, two-point crossover is used, and generally, three-point crossover is used.

[0094] The two-point crossover operation inherits the local information of the parent X1, does not change the relative positions between the parent tasks, generates two random points p1 and p2 on the chromosome, takes the disassembly task sequence between p1 and p2 as the crossover fragment, maps the corresponding disassembly task sequence on the parent X1, and replaces the corresponding fragment on the original parent X1 with the mapped new fragment.

[0095] Three-point crossover: Generate three random change points p1, p2 and p3 by random numbers, and divide the parent X1 into four segments. Randomly select two segments as the position information segments. Except for the position information segment, the sequence order of the parent X1 remains unchanged. Delete the disassembly task of the position information segment in a single X1. Find the task sequence corresponding to the position information segment in the parent node X1, copy it and fill it into the vacant position in X1, that is, the new position New_X1, and generate New_X2 according to the same principle. The specific operation is as follows Figure 2 shown.

[0096] In addition, the mutation operation in step S3 is to introduce a disturbance factor to reduce the overall probability of falling into local optimality. Since the genetic algorithm is prone to falling into local optimality in the later stage of iteration, the solution set lacks diversity. To increase the diversity of the solution set, reduce premature convergence and falling into local optimality, we introduce a local perturbation factor Pd for the newly generated population and adopt a local mutation operation. Whether to perform mutation is judged by formula (24), and the specific operation is as follows. Suppose the chromosome individual is Generate a random number d (a random number in [0, 1]) and a local perturbation factor Pd. If d < Pd, perform a mutation operation on the chromosome individual. The specific mutation method is as Figure 3 shown.

[0097]

[0098] In the formula, where d is a random number between [0, 1], P d is the perturbation factor, x ipr is the position of the immediate predecessor task of the task at x i is the position of the immediate successor task of the task at x isu is the position of the immediate successor task of the task at x i '[]' is the rounding function, and λ is a random number between [0, 1].

[0099] Step S4: Judge whether the current iteration number exceeds the maximum iteration number limit. If it exceeds, use the NSGA-II crowding distance mechanism to screen out the non-dominated solutions and output the iteration result. If it does not exceed, record it as one iteration completed, and return to step S3 to continue iterative calculation until the output condition is met.

[0100] The following is the effect display in the actual operation of this embodiment:

[0101] Program test environment:

[0102] The simulation calculation environment is an Intel(R) Core(TM) i5-12400 CPU @ 2.5GHz, 16GB RAM Windows 10 Pro operating system, and MATLAB R2016a is used for programming and running.

[0103] To further verify the superiority of the proposed improved genetic algorithm (IGA) in solving this problem, the present invention takes the disassembly line of retired engines of a disassembly company in southwest China as an example, and uses the proposed model and method to optimize the performance of the disassembly line to verify the proposed method.

[0104] There are 37 disassembly tasks in the retired engine. By evaluating and recording on-site, the disassembly time t of the disassembly mode is calculated ir / second), cost cir / Yuan), and obtain recycling information through multi-color reasoning. Table 2 lists the specific statistics.

[0105] Table 2 Data information of various disassembly tasks of retired engines

[0106]

[0107]

[0108] Test results:

[0109] According to the actual disassembly of the engine, the predetermined cycle time CT=580s was set, and the program was run independently 10 times with an average running time of 135s. The results of one run are shown in Table 3.

[0110] According to the proposed method and algorithm, the disassembly and assembly scheme of the automobile engine is solved, and the specific scheme is shown in Table 3. From the perspective of goals f1, f2, f3, and f4, each scheme may have its own focus. The Gantt charts of each scheme are shown in Figure 5. The numbers in the rectangles in the figure are the task numbers, and the length of the rectangles represents the disassembly time of each task.

[0111] Table 3 Results of independent operation of IGA algorithm program

[0112]

[0113]

[0114] From Table 3 and Figure 5, we can see that:

[0115] (1) The proposed algorithm can successfully solve both small-scale and large-scale problems and obtain satisfactory solutions. This shows that the proposed algorithm is feasible in solving the removal line balance problem under the consideration of fault constraints.

[0116] (2) The proposed algorithm satisfies both the disassembly priority relation and the recycling priority relation constraints. Therefore, the disassembly solution generated by the proposed method is a feasible solution.

[0117] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for solving multi-fault constraint remanufacturing disassembly line setting based on polychromatic set method, characterized in that: The following steps are involved: Step S1: With the goal of minimizing the number of stations, idle index, component remanufacturing value index, and disassembly cost, the disassembly line balance problem is proposed, and the corresponding objective function is established based on the constraints; Wherein, the objective function is as shown in formula (1)-(5): minf1=N(1) In formula (1): N is the number of open workstations; In formula (2), CT is the cycle time; i is the disassembly task; r is the workstation number; M is the number of disassembly tasks; t i is the disassembly time required for disassembly task i; x ir represents a 0-1 variable. If task i is assigned to workstation r, then x ir =1; otherwise x ir =0; In formula (3): L i is the position of the i-th component to be disassembled on the disassembly line; P i represents the recycling decision of task i, which refers to the remanufacturing value of the i-th disassembly task; In formula (4): C i represents the disassembly cost information of the i-th disassembly task component; AT represents the actual cycle time; C r represents the unit time cost of opening a workstation; M is the number of disassembly tasks; Therefore, the multi-objective function of the balance optimization of the multi-fault constrained remanufacturing disassembly line is: minF=(f1,f2,f3,f4)(5) Its constraints are shown in equations (6)-(12): Among them, x ir represents a 0-1 variable. If task i is assigned to workstation r, then x ir =1; otherwise x ir =0; M is the number of disassembly tasks; S r represents the set of tasks in the rth workstation; M is the number of disassembly tasks; Among them, x ir represents a 0-1 variable. If task i is assigned to workstation r, then x ir =1; otherwise x ir =0; Among them, x ir represents a 0-1 variable. If task i is assigned to workstation r, then x ir =1; otherwise x ir =0; t i is the disassembly time required for disassembly task i; CT is the cycle time; Among them, S ij Represents the priority relationship between two tasks. If task i is the direct predecessor of task j, then S ij =1, otherwise, S ij =0; Among them, t i is the disassembly time required for disassembly task i; CT is the cycle time; M is the number of disassembly tasks; N is the number of open workstations; Among them, h ij is the constructed recycling matrix element; The constraint condition represented by formula (12) is solved and determined based on the polychromatic set method, which specifically includes the following steps: Step 1: Based on the relationship table between part status and fault information, a hierarchical multi-fault transmission chain is constructed to quantitatively determine the relationship between parts and fault characteristics; Step 2: Based on the expert scoring method, the relationship between each part of the product to be disassembled and the fault feature layer is obtained and a matrix is ​​constructed. The matrix is ​​used as the Boolean matrix of the fault feature layer, and the surrounding channel matrix of the fault feature layer is obtained by using the expert scoring method; Step 3: Calculate the correlation matrix between each part of the product to be disassembled and the fault feature layer based on the polychromatic reasoning formula, and use the correlation matrix as the Boolean matrix of the fault degree layer. The polychromatic reasoning formula is shown in formula (21): In the formula, is the Boolean matrix of the current layer of the multiple fault transmission chain; is the peripheral channel matrix of the current layer of the multiple fault transmission chain, It is the correlation matrix between the previous layer and the current layer in the multiple fault transmission chain; Step 4: Use the expert scoring method to obtain the peripheral channel matrix of the fault degree layer, and combine it with the Boolean matrix of the fault degree layer obtained in step 3. Use formula (21) to calculate the correlation matrix between the fault feature layer and the fault degree layer, and use the correlation matrix as the Boolean matrix of the scrap reason layer. And so on, proceed layer by layer until the Boolean matrix of the recycling decision layer is calculated, and the Boolean matrix of the recycling decision layer is used to guide the recycling decision; Step S2: setting the problem parameters, algorithm parameters and external file Q, and encoding and generating the initial population according to the priority relationship of the products to be disassembled in the problem parameters; Step S3: start iterative calculation for the initial population, wherein the individuals in the initial population are subjected to crossover and mutation operations in sequence, the population after the operations is updated, and the external archive set is updated using the Pareto elite strategy; Step S4: Determine whether the current number of iterations exceeds the maximum number of iterations. If so, use the NSGA-II crowding distance mechanism to screen out non-inferior solutions and output the iteration results. If not, it is considered that one iteration is completed, and return to step S3 to continue iterative calculation until the output conditions are met.

2. The method for setting up a remanufacturing disassembly line based on a polychromatic set method for solving multiple fault constraints according to claim 1, characterized in that: The crossover operation in step S3 is performed in the form of a combination of two-point crossover and three-point crossover.

3. The method for solving multi-fault constraint remanufacturing disassembly line setting based on polychromatic set method according to claim 1, characterized in that: The mutation operation in step S3 is a local mutation operation that introduces a local disturbance factor Pd into the newly generated population.

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