CN107808210B - Disassembling, regenerating and disassembling scheme and regenerating scheme integrated decision-making method for complex product - Google Patents
Disassembling, regenerating and disassembling scheme and regenerating scheme integrated decision-making method for complex product Download PDFInfo
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Abstract
The invention discloses a disassembly, regeneration and disassembly scheme oriented complex product and a regeneration scheme integrated decision method, which comprises the following steps: randomly generating an initial disassembly scheme and a regeneration scheme of a complex product to be disassembled; generating a feasible disassembly scheme and a regeneration scheme according to the quality state of the parts of the complex product to be disassembled and the disassembly priority relationship; coding the disassembly scheme and the regeneration scheme by adopting a real number coding mechanism; and carrying out evolution operation on the codes by utilizing an improved coevolution algorithm, optimizing the overall regeneration value and obtaining an optimal disassembly scheme and a regeneration scheme at the same time. The integrated decision method provided by the invention overcomes the defect that the prior method cannot realize benefit maximization because the mutual influence of the disassembly scheme and the regeneration scheme is not considered at the same time, and realizes the integrated decision of disassembly and regeneration of complex products.
Description
The technical field is as follows:
the invention relates to the technical field of waste product recycling application, in particular to a disassembly scheme and a regeneration scheme integrated decision method for complex product disassembly and regeneration.
Background art:
the recycling application mode of the waste products mainly comprises the following steps: material recycling, component remanufacturing, direct reuse and the like. The recycling application mode is reasonably determined, and the overall regeneration value of the waste products can be effectively improved. The remanufacturing is one of effective modes for improving the recycling and reusing additional value of the waste products through high-technology repair and modification of the waste products. The disassembly is a key prerequisite link for remanufacturing and direct recycling of products, and the disassembly planning is to disassemble some or some specified components (such as valuable parts, toxic or damaged parts) from the products according to the information of product structures, assembly relations and the like to generate a target component disassembly sequence meeting certain constraint conditions so as to reduce the disassembly time and cost and improve the working efficiency.
With the improvement of the complexity of the waste products to be researched, the traditional planning method is easy to fall into 'combined explosion', and the traditional decision method only considers the problem of disassembly planning generally, does not consider the mutual influence of the disassembly scheme planning and the regeneration scheme selection at the same time, and is difficult to realize the maximization of the recycling application benefit. The Chinese invention patent application No. 200910155247.X entitled "Complex product target supporting Green design and cooperative disassembly planning method" is focused on complete disassembly planning of parts and does not consider a regeneration scheme. The documents "Zhang Xiufen, zhang Shu have, e national, and so on, a method for planning a target selective disassembly sequence for a complex mechanical product, namely a mechanical engineering project (journal), 2010, volume 46, no. 11, 172-178, proposes a method for combining a disassembly hybrid diagram and a particle swarm algorithm, solves the disassembly depth and the disassembly sequence of the complex mechanical product, but does not consider a recycling application processing mode of parts. The documents Zhang Xiufen and Zhang Shu have a product disassembly sequence planning method based on a particle swarm algorithm [ (periodical) computer integrated manufacturing system ],2009, volume 15, stage 3, 508-514", a product disassembly empowerment mixed graph model is established, a detachable sequence is generated by a geometric reasoning method, the documents Liu Zhifeng, yang Dejun and Gu Guogang, a disassembly sequence planning based on a simulated annealing particle swarm optimization algorithm [ (periodical) science and report natural edition of university of joint fertilization, 2011, volume 34, stage 2 and 161-165" a product structure expression model is established based on a disassembly constraint graph and a disassembly sequence planning is performed by utilizing a simulated annealing particle swarm optimization algorithm, the documents Wang Junfeng, li Shiji, liu Jigong, a product selection technology research oriented to green manufacturing [ (periodical) computer integrated manufacturing system ],2007, modeling is proposed in volume 6, volume 13, volume 1097-1102, and a product disassembly sequence selection method based on a group disassembly sequence planning method is provided, and a disassembly sequence selection process selection method based on a disassembly sequence planning method is provided, and a disassembly process selection process is not considered in a dynamic selection process, and a disassembly sequence selection process selection method is provided. In order to overcome the defects, an integrated decision problem is provided, a disassembly scheme and a regeneration scheme are considered simultaneously according to the problem characteristics, and an integrated decision method of the disassembly scheme and the regeneration scheme is designed.
The invention content is as follows:
the invention aims to provide an integrated decision-making method for a disassembly, regeneration and disassembly scheme and a regeneration scheme of a complex product, wherein the disassembly scheme and the regeneration scheme of the complex product to be disassembled are randomly generated, and are corrected according to the quality state and the disassembly priority relation of parts of the complex product to be disassembled; coding the disassembly scheme and the regeneration scheme by adopting a real number coding mechanism; carrying out evolution operation on the codes by utilizing an improved coevolution algorithm, and optimizing the overall regeneration value; and simultaneously determining the optimal disassembly scheme and regeneration scheme of the complex product.
The invention adopts the following technical scheme: a disassembly, regeneration and disassembly scheme and regeneration scheme integrated decision method for complex products comprises the following steps:
the method comprises the following steps: randomly generating an initial disassembly scheme and a regeneration scheme of a complex product to be disassembled;
step two: generating a feasible disassembly scheme and a regeneration scheme according to the quality state of the parts of the complex product to be disassembled and the disassembly priority relationship;
step three: coding the disassembly scheme and the regeneration scheme by adopting a real number coding mechanism;
step four: carrying out evolution operation on the codes by utilizing an improved coevolution algorithm, optimizing the overall regeneration value and simultaneously obtaining an optimal disassembly scheme and a regeneration scheme;
Further, a feasible disassembly scheme and a feasible regeneration scheme are generated in the second step, and the specific steps are as follows:
step 1: generating an initial random regeneration scheme and a disassembly weight set;
step 1.1: randomly assigning a regeneration scheme to each part;
step 1.2: assigning a certificate which is not repeated from 1 to n to each part as the unique disassembly weight of the part;
step 2: generating a feasible regeneration scheme;
step 2.1: checking the regeneration scheme of each part, and randomly modifying the regeneration scheme into a feasible regeneration scheme if the scheme is not feasible;
step 2.2: when there is a part assigned for reuse, if its end-of-life quality level does not meet the minimum quality level for reuse, then modifying the part regeneration scheme to recycle or remanufacture;
step 2.3: checking the regeneration rate of the new regeneration scheme, if the regeneration rate does not meet the minimum threshold, randomly selecting one regeneration scheme as a recycled part, and changing the scheme into remanufacturing;
step 2.4: if the regeneration ratio requirement is still not met, repeating the step 2.3, otherwise, obtaining a feasible regeneration scheme, and continuing to execute the step 3;
and step 3: generating a minimum set of parts to be disassembled by reversely traversing the disassembly priority graph;
Step 3.1: traversing the regeneration scheme of each part, if the part is to be remanufactured or reused, adding the part into the minimum disassembly set, and if the part contains toxic substances, adding the part into the minimum disassembly set, thereby obtaining an initial minimum disassembly set;
step 3.2: traversing the direct precursor set of each part in the minimum disassembly set, if one part exists as the direct precursor of the currently traversed part and the minimum disassembly set is not added, adding the part into the minimum disassembly set, thereby obtaining the minimum disassembly set corresponding to the regeneration scheme;
and 4, step 4: constructing a local topological graph corresponding to the minimum disassembly set;
step 4.1: generating a disassembly priority relationship matrix corresponding to the minimum disassembly set
If all parts need to be disassembled, directly executing the step 5 for complete disassembly; otherwise, for any two parts u and v in the minimum disassembly set, if u is the direct precursor of v, the order is carried out
Otherwise, the value is 0;
step 4.2: constructing a local topological graph PTG = { V, E }, wherein V is a vertex set representing each part, and E is an edge representing a disassembly relation;
step 4.3: calculating an inflow I for each node in a local topology graph u ，I u The total number of direct front drives of the part u is reflected;
and 5: generating a feasible disassembly sequence based on the disassembly weight and a topological sorting method;
step 5.1: any one of the parts (I) without direct disassembly of the front drive u = 0) can be directly disassembled, and searching for detachable part node configuration S in the current diagram d If the potential of the set | S d I =0, which means that there is no detachable part and the operation is stopped; if | S d If | =1, directly adding the part into a disassembly sequence set; if | S d If the | is more than 1, the parts in the sequence are arranged in a descending order according to the disassembly weight, and the parts are added into the disassembly sequence according to the sequence;
step 5.2: deleting the nodes added with the disassembly sequence set and the edges corresponding to the parts in the local topological graph PTG, and updating the inflow of the rest nodes in the PTG;
step 5.3: if the local topology is not null
Step 5.1 is executed repeatedly, otherwise, the operation is stopped, and when all nodes in the local topological graph PTG are traversed, one node can be executedThe disassembly scheme of the rows is obtained, and then a complete feasible solution is obtained.
Further, in the real number encoding mechanism in the third step, each gene in the individual regeneration scheme represents the regeneration scheme of one part, 1 represents material recycling, 2 represents remanufacturing, 3 represents direct recycling, and the disassembly sequence individual is represented by random disassembly weight values of n parts.
Further, the improved co-evolution algorithm in step four comprises the following steps:
step 1: initializing a population, initializing a regeneration scheme, namely combining a random scheme and a random weight, checking the feasibility of the regeneration scheme, and repairing the regeneration scheme into a feasible regeneration scheme;
step 2: respectively calculating the initial fitness of the individual, calculating the regeneration benefit, the disassembly cost and the net profit of each feasible complete solution, and taking the net profit value as a fitness evaluation value;
and step 3: constructing a neighborhood of local interactive search;
and 4, step 4: symbiotic cooperation and competition operation, each individual selects a random individual from the neighborhood of another population as a symbiotic partner; then, calculating the fitness of each individual in the two neighborhoods, namely an objective function value, confirming the current local optimal fitness, evaluating the fitness of each individual and confirming the local optimal fitness in the neighborhood; if the fitness is higher than the original fitness, randomly replacing a inferior solution with the gene of the optimal solution;
and 5: genetic evolution, namely performing selection, crossing and mutation operations on individuals in each neighborhood to generate new sub-individuals and replace poor individuals in the original population;
step 6: performing local reinforcement search on an elite solution;
And 7: stopping evolution, if the maximum iteration number is reached, stopping, otherwise, repeating the step 3;
and 8: global perturbation strategy if the local optimal solution continues g B And if the generation is not updated, performing disturbance operation and updating on part of the inferior individuals with a certain probability to increase the diversity of the population, and going to execute the step 3.
The invention has the following beneficial effects:
1. repairing a random regeneration scheme, and providing a method for combining multi-target reverse recursion and local topological sorting to ensure the feasibility of disassembling a sequence and the matching of the sequence and the regeneration scheme. Firstly, according to a regeneration scheme, a product structure, material toxicity and the like, all parts needing to be disassembled are confirmed by adopting multi-target reverse recursion to form a set of minimum disassembly parts, so that the problem of local disassembly sequence planning is converted into the problem of complete disassembly planning of the set of minimum disassembly parts; and then randomly creating a feasible sequence satisfying the disassembly precedence relationship constraint by traversing the local topological graph. The method can generate a feasible complete solution, avoids complex preferential constraint repairing operation, and improves the convenience of genetic operation in the solution of the evolutionary algorithm.
2. According to the real number coding mechanism provided by the invention, each gene in a regeneration scheme individual represents a regeneration scheme of a part, such as 1 represents material recycling, 2 represents remanufacturing, and 3 represents direct recycling. The disassembly sequence individuals are represented by random disassembly weight values for the n parts, and are not directly represented as a part sequence. This encoding not only changes the dynamically varying length of the sequence chromosome in the partial disassembly to a fixed length chromosome, but also does not result in infeasible sequences for any evolutionary manipulation of the chromosome.
3. The improved coevolution algorithm is established on the basis of a cooperative coevolution algorithm and a local interactive endosymosis evolution algorithm, a local interaction and endosymosis evolution strategy is adopted, an elite solution reinforced search strategy is added, small-range local interaction is effectively prevented from getting into precocity through a global disturbance mechanism so as to realize global optimization, an optimal disassembly scheme and a regeneration scheme are obtained under the aim of optimizing the overall regeneration value, and the problem of 'combined explosion' brought by a target assembly disassembly sequence planning strategy based on a graph search method is solved.
Description of the drawings:
FIG. 1 is a prior art disassembly diagram of a test product for example use in the present invention.
FIG. 2 is a flow chart of an algorithm for generating feasible solutions for the disassembly scheme and the regeneration scheme according to the present invention.
FIG. 3 is a flow diagram of the integer coding mechanism employed in the present invention.
FIG. 4 is a flow chart of a disassembly and regeneration disassembly scheme and a regeneration scheme integration decision method facing to complex products.
The specific implementation mode is as follows:
the invention will be further described with reference to the accompanying drawings.
The invention relates to a disassembly, regeneration and disassembly scheme oriented to complex products and a regeneration scheme integrated decision-making method, which comprises the following steps:
the method comprises the following steps: randomly generating an initial disassembly scheme and a regeneration scheme of a complex product to be disassembled;
step two: generating a feasible disassembly scheme and a regeneration scheme according to the quality state of the parts of the complex product to be disassembled and the disassembly priority relationship;
step three: coding the disassembly scheme and the regeneration scheme by adopting a real number coding mechanism;
step four: carrying out evolution operation on the codes by utilizing an improved coevolution algorithm, optimizing the overall regeneration value and obtaining an optimal disassembly scheme and an optimal regeneration scheme;
the method combines multi-target reverse recursion and local topological sequencing to ensure the feasibility of a disassembly scheme and the matching of the disassembly scheme and a regeneration scheme.
And generating a feasible disassembly scheme and a feasible regeneration scheme in the second step, wherein the specific steps are as follows:
step 1: generating an initial random regeneration scheme and a disassembly weight set;
step 1.1: randomly assigning a regeneration scheme to each part;
step 1.2: assigning a certificate which is not repeated from 1 to n to each part as the unique disassembly weight of the part;
step 2: generating a feasible regeneration scheme;
step 2.1: checking the regeneration scheme of each part, and if the scheme is not feasible, randomly modifying the regeneration scheme into a feasible regeneration scheme;
step 2.2: when there is a part assigned for reuse, if its end-of-life quality level does not meet the minimum quality level for reuse, then modifying the part regeneration scheme to recycle or remanufacture;
step 2.3: checking the regeneration rate of the new regeneration scheme, if the regeneration rate does not meet the minimum threshold value, randomly selecting one regeneration scheme as a recycled part, and changing the scheme into remanufacturing;
step 2.4: if the regeneration ratio requirement is still not met, repeating the step 2.3, otherwise, obtaining a feasible regeneration scheme, and continuing to execute the step 3;
and 3, step 3: generating a minimum set of parts to be disassembled by reversely traversing the disassembly priority graph;
Step 3.1: traversing the regeneration scheme of each part, if the part is to be remanufactured or reused, adding the part into the minimum disassembly set, and if the part contains toxic substances, adding the part into the minimum disassembly set, thereby obtaining an initial minimum disassembly set;
step 3.2: traversing the direct precursor set of each part in the minimum disassembly set, if one part exists as the direct precursor of the currently traversed part and the minimum disassembly set is not added, adding the part into the minimum disassembly set, thereby obtaining the minimum disassembly set corresponding to the regeneration scheme;
and 4, step 4: constructing a local topological graph corresponding to the minimum disassembly set;
step 4.1: generating a disassembly priority relationship matrix corresponding to the minimum disassembly set
If all parts need to be disassembled, directly executing the step 5 for complete disassembly; otherwise, for any two parts u and v in the minimum disassembly set, if u is the direct precursor of v, the order is carried out
Otherwise, the value is 0;
and 4.2: constructing a local topological graph PTG = { V, E }, wherein V is a vertex set representing each part, and E is an edge representing a disassembly relation;
step 4.3: calculating an inflow I for each node in a local topology graph u ，I u The total number of direct front drives of the part u is reflected;
and 5: generating a feasible disassembly sequence based on the disassembly weight and a topological sorting method;
step 5.1: any one of the parts (I) without direct disassembly of the front drive u = 0) can be directly disassembled, and node formation S of the detachable part in the current drawing is searched d If the potential of the set | S d I =0, which means that there is no detachable part and the operation is stopped; if | S d If | =1, directly adding the part into a disassembly sequence set; if | S d If the | is more than 1, the parts in the sequence are arranged in a descending order according to the disassembly weight, and the parts are added into the disassembly sequence according to the sequence;
step 5.2: deleting the nodes added with the disassembly sequence set and the edges corresponding to the parts in the local topological graph PTG, and updating the inflow of the rest nodes in the PTG;
step 5.3: if the local topology is not null
And repeating the step 5.1, otherwise, stopping the operation, and obtaining a feasible disassembly scheme when all nodes in the local topological graph PTG are traversed, thereby obtaining a complete feasible solution.
In the real number encoding mechanism in the third step, each gene in the regeneration scheme individual represents a regeneration scheme of a part, such as 1 represents material recycling, 2 represents remanufacturing, and 3 represents direct reuse. The disassembly sequence individuals are represented by a random disassembly weight value for the n parts, and not directly as a part sequence. This encoding not only changes the dynamically varying length of the sequence chromosome in the partial disassembly to a fixed length chromosome, but also does not result in infeasible sequences for any evolutionary manipulation of the chromosome.
The improved coevolution algorithm in the fourth step comprises the following steps:
step 1: initializing a population, and initializing a regeneration scheme, namely combining a random scheme with random weights; checking the feasibility of the regeneration scheme, and repairing the regeneration scheme into a feasible regeneration scheme;
step 2: respectively calculating the initial fitness of the individual, and calculating the regeneration benefit, the disassembly cost and the net profit of each feasible complete solution, wherein the net profit value is used as a fitness evaluation value;
and step 3: constructing a neighborhood of local interactive search;
and 4, step 4: symbiotic cooperation and competition operation, each individual selects a random individual from the neighborhood of another population as a symbiotic partner; then, calculating the fitness (objective function value) of each individual in the two neighborhoods and confirming the current local optimal fitness, then evaluating the fitness of each individual and confirming the local optimal fitness in the neighborhoods; if the fitness is higher than the original fitness, randomly replacing a inferior solution with the gene of the optimal solution;
and 5: genetic evolution, namely performing selection, crossing and mutation operations on individuals in each neighborhood to generate new sub-individuals and replace poor individuals in the original population;
step 6: performing local reinforcement search on an elite solution;
And 7: stopping evolution, if the maximum iteration number is reached, stopping, otherwise, repeating the step 3;
and step 8: global perturbation strategy if local optimal solution continues g B And if the generation is not updated, performing disturbance operation and updating on part of the inferior individuals with a certain probability to increase the diversity of the population, and going to execute the step 3.
The improved coevolution algorithm is established on the basis of a cooperative coevolution algorithm and a local interaction endosymosis evolution algorithm, adopts a local interaction and endosymosis evolution strategy, increases an elite solution reinforced search strategy, and effectively avoids the situation that small-range local interaction is trapped in precocity through a global disturbance mechanism so as to realize global optimization.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications can be made without departing from the principle of the present invention, and these modifications should also be regarded as the protection scope of the present invention.
Claims (3)
1. A disassembly, regeneration and disassembly scheme oriented to complex products and a regeneration scheme integration decision method are characterized in that: the steps are as follows
The method comprises the following steps: randomly generating an initial disassembly scheme and a regeneration scheme of a complex product to be disassembled;
Step two: generating a feasible disassembly scheme and a regeneration scheme according to the quality state of the parts of the complex product to be disassembled and the disassembly priority relationship;
step three: coding the disassembly scheme and the regeneration scheme by adopting a real number coding mechanism;
step four: carrying out evolution operation on the codes by utilizing an improved coevolution algorithm, optimizing the overall regeneration value and simultaneously obtaining an optimal disassembly scheme and a regeneration scheme;
generating a feasible disassembly scheme and a feasible regeneration scheme in the second step, and specifically comprising the following steps of:
step 1: generating an initial random regeneration scheme and a disassembly weight set;
step 1.1: randomly assigning a regeneration scheme to each part;
step 1.2: assigning a certificate which is not repeated from 1 to n to each part as the unique disassembly weight of the part;
step 2: generating a feasible regeneration scheme;
step 2.1: checking the regeneration scheme of each part, and randomly modifying the regeneration scheme into a feasible regeneration scheme if the scheme is not feasible;
step 2.2: when there is a part assigned for reuse, if its end-of-life quality level does not meet the minimum quality level for reuse, then modifying the part regeneration scheme to recycle or remanufacture;
step 2.3: checking the regeneration rate of the new regeneration scheme, if the regeneration rate does not meet the minimum threshold value, randomly selecting one regeneration scheme as a recycled part, and changing the scheme into remanufacturing;
Step 2.4: if the regeneration ratio requirement is still not met, repeating the step 2.3, otherwise, obtaining a feasible regeneration scheme, and continuing to execute the step 3;
and step 3: generating a minimum set of parts to be disassembled by reversely traversing the disassembly priority graph;
step 3.1: traversing the regeneration scheme of each part, if the part is to be remanufactured or reused, adding the part into the minimum disassembly set, and if the part contains toxic substances, adding the part into the minimum disassembly set, thereby obtaining an initial minimum disassembly set;
step 3.2: traversing the direct precursor set of each part in the minimum disassembly set, if one part exists as the direct precursor of the currently traversed part and the minimum disassembly set is not added, adding the part into the minimum disassembly set, thereby obtaining the minimum disassembly set corresponding to the regeneration scheme;
and 4, step 4: constructing a local topological graph corresponding to the minimum disassembly set;
step 4.1: generating a disassembly priority relation matrix corresponding to the minimum disassembly set
If all parts need to be disassembled, directly executing the step 5 for complete disassembly; otherwise, for any two parts u and v in the minimum disassembly set, if u is the direct precursor of v, the order is carried out 
Otherwise, the value is 0;
and 4.2: constructing a local topological graph PTG = { V, E }, wherein V is a vertex set representing each part, and E is an edge representing a disassembly relation;
step 4.3: calculating an inflow I for each node in a local topology graph u ，I u The total number of direct front drives of the part u is reflected;
and 5: generating a feasible disassembly sequence based on the disassembly weight and a topological sorting method;
step 5.1: any one of the parts (I) without direct disassembly of the front drive u = 0) can be directly disassembled, and node formation S of the detachable part in the current drawing is searched d If the potential of the set | S d I =0, which means that there is no detachable part and the operation is stopped; if | S d If | =1, directly adding the part into a disassembly sequence set; if | S d If the | is more than 1, the parts in the sequence are arranged in a descending order according to the disassembly weight, and the parts are added into the disassembly sequence according to the sequence;
step 5.2: deleting the nodes added with the disassembly sequence set and the edges corresponding to the parts in the local topological graph PTG, and updating the inflow of the rest nodes in the PTG;
step 5.3: if the local topology is not null
And repeating the step 5.1, otherwise, stopping the operation, and obtaining a feasible disassembly scheme when all nodes in the local topological graph PTG are traversed, thereby obtaining a complete feasible solution.
2. The complex product-oriented disassembly and regeneration scheme integration decision method as claimed in claim 1, wherein: and in the real number coding mechanism in the third step, each gene in the individual regeneration scheme represents the regeneration scheme of one part, 1 represents material recycling, 2 represents remanufacturing, 3 represents direct recycling, and the individual disassembly sequence is represented by random disassembly weight values of n parts.
3. The complex product-oriented disassembly and regeneration scheme integration decision method as claimed in claim 1, wherein: the improved coevolution algorithm in the fourth step comprises the following steps:
step 1: initializing a population, initializing a regeneration scheme, namely combining a random scheme and a random weight, checking the feasibility of the regeneration scheme, and repairing the regeneration scheme into a feasible regeneration scheme;
step 2: respectively calculating the initial fitness of the individual, calculating the regeneration benefit, the disassembly cost and the net profit of each feasible complete solution, and taking the net profit value as a fitness evaluation value;
and step 3: constructing a neighborhood of local interactive search;
and 4, step 4: performing symbiotic cooperation and competition operation, wherein each individual selects a random individual from the neighborhood of another population as a symbiotic partner; then, calculating the fitness of each individual in the two neighborhoods, namely an objective function value, confirming the current local optimal fitness, evaluating the fitness of each individual and confirming the local optimal fitness in the neighborhood; if the fitness is higher than the original fitness, randomly replacing a inferior solution with the gene of the optimal solution;
And 5: genetic evolution, namely, selecting, crossing and mutating individuals in each neighborhood to generate new sub-individuals and replace poor individuals in the original population;
step 6: performing local reinforcement search on an elite solution;
and 7: stopping evolution, if the maximum iteration number is reached, stopping, otherwise, repeating the step 3;
and step 8: global perturbation strategy if local optimal solution continues g B And if the generation is not updated, performing disturbance operation and updating on part of the inferior individuals with a certain probability to increase the diversity of the population, and going to execute the step 3.
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| CN108921722A (en) * | 2018-07-04 | 2018-11-30 | 武汉科技大学 | One kind remanufacturing engine design platform |
| CN109814509B (en) * | 2019-01-31 | 2021-01-26 | 合肥工业大学 | Low-carbon efficient parallel disassembly line balance optimization method |
| CN110135725A (en) * | 2019-05-10 | 2019-08-16 | 北京理工大学 | A kind of cable assembly sequence-planning method, device and equipment |
| CN110674953A (en) * | 2019-10-09 | 2020-01-10 | 青岛科技大学 | Disassembly recovery method based on value evaluation of waste smart phone |
| CN113283616A (en) * | 2021-04-14 | 2021-08-20 | 暨南大学 | Waste product disassembly sequence and disassembly depth integrated decision-making method |
| CN117893139A (en) * | 2024-03-15 | 2024-04-16 | 山东未来网络研究院(紫金山实验室工业互联网创新应用基地) | Material proportioning method based on industrial chain |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106203696A (en) * | 2016-07-08 | 2016-12-07 | 桂林电子科技大学 | A kind of hybrid precast sequence generating method based on symbol |
Family Cites Families (4)
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| CN101706895A (en) * | 2009-12-10 | 2010-05-12 | 浙江大学 | Method for planning destination and cooperation disassembly of complex product supporting green design |
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-
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Non-Patent Citations (3)
| Title |
|---|
| Multi-objective optimization decision-making of quality dependent product recovery for sustainability;Kai Meng等;《International Journal of Production Economics》;20170325;全文 * |
| 不确定环境下再制造生产计划与车间调度集成优化硏究;温海骏;《中国博士学位论文全文数据库信息科技辑》;20170215;第1-131页 * |
| 航天产品虚拟装配工艺设计技术及其应用基础研究;张丹;《中国博士学位论文全文数据库工程科技Ⅱ辑》;20120115;第1-140页 * |
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