“…Figure 20.Graph between original subsets and proposed methodology subsets.Figure 21.Motor drive assembly. 41 …”
Section: Comparative Analysis Of the Proposed Methodology With Differmentioning
confidence: 99%
“…The proposed methodology is compared in terms of tool changes, direction changes and optimal assembly sequences with the different algorithms considered by Mishra and Deb. 41 Later on the solutions obtained with these algorithms have been compared with flower pollination algorithm. The results of the proposed methodology and the remaining algorithms are tabulated in Table 14.…”
Section: Comparative Analysis Of the Proposed Methodology With Differmentioning
Assembly sequence planning is one of the multi-model optimization problems, in which more than one objective function has to be optimized at a time to obtain the quality assembly sequence. Moreover obtaining the feasible sequences from the possible finite set of sequences is a difficult task as the assembly sequence planning problem is N-P hard combinatorial problem. To solve the assembly sequence planning problem, researchers have developed various techniques to obtain the optimum solution. The developed methodologies have many drawbacks like struck at local optima, poor performance, huge search space and many more. To overcome these difficulties, the current research work aims to use stability graph to generate stable assembly subsets for obtaining the optimum assembly sequences. In the proposed methodology, to reduce the search space and to obtain the quality assembly sequences, stability graph is considered. Moreover, the fitness of assembly subsets is evaluated according to the user weights at each level before proceeding to the higher levels. Due to this, the higher fitness value subsets are eliminated at each stage by which time of execution will reduce enormously. The proposed methodology has implemented on various industrial products and compared the results with the various well-known algorithms.
“…Figure 20.Graph between original subsets and proposed methodology subsets.Figure 21.Motor drive assembly. 41 …”
Section: Comparative Analysis Of the Proposed Methodology With Differmentioning
confidence: 99%
“…The proposed methodology is compared in terms of tool changes, direction changes and optimal assembly sequences with the different algorithms considered by Mishra and Deb. 41 Later on the solutions obtained with these algorithms have been compared with flower pollination algorithm. The results of the proposed methodology and the remaining algorithms are tabulated in Table 14.…”
Section: Comparative Analysis Of the Proposed Methodology With Differmentioning
Assembly sequence planning is one of the multi-model optimization problems, in which more than one objective function has to be optimized at a time to obtain the quality assembly sequence. Moreover obtaining the feasible sequences from the possible finite set of sequences is a difficult task as the assembly sequence planning problem is N-P hard combinatorial problem. To solve the assembly sequence planning problem, researchers have developed various techniques to obtain the optimum solution. The developed methodologies have many drawbacks like struck at local optima, poor performance, huge search space and many more. To overcome these difficulties, the current research work aims to use stability graph to generate stable assembly subsets for obtaining the optimum assembly sequences. In the proposed methodology, to reduce the search space and to obtain the quality assembly sequences, stability graph is considered. Moreover, the fitness of assembly subsets is evaluated according to the user weights at each level before proceeding to the higher levels. Due to this, the higher fitness value subsets are eliminated at each stage by which time of execution will reduce enormously. The proposed methodology has implemented on various industrial products and compared the results with the various well-known algorithms.
“…Input: the interference matrix IM 10+ of the EOL product, the disassembly component sequence X, and its corresponding disassembly direction sequence D Output: an improved disassembly sequence Update D by modifying the disassembly direction of c. Finally, a new component sequence X new (3,5,1,4,2,6,8,7) and its corresponding direction sequence D new (+x, +y, +x, -x, -x, +z, +x, +y) are generated.…”
Section: Nsomentioning
confidence: 99%
“…Although the DSP problem needs to be solved urgently, most of the existing research focuses on assembly sequence planning [3][4][5][6][7]. The research on DSP is relatively few.…”
Section: Introductionmentioning
confidence: 99%
“…The pseudo-code of NSOFig.4provides a schematic diagram of NSO. Firstly, selecting component 1 to change its position and disassembly direction in the original component sequence X original(3,5,4,2,1,6,8,7). Secondly,…”
Disassembly sequence planning (DSP) can effectively increase the disassembly efficiency, shorten the disassembly cycle, reduce disassembly costs and reduce environmental hazards of end-oflife (EOL) products, playing an important role in manufacturing industries. Thus, it is urgent to propose an approach to solve the DSP problem. DSP is a famous NP-hard combinatorial optimization problem.As the size of components increases, exact algorithms can hardly obtain the optimal disassembly sequence. Therefore, we proposes a promising intelligence algorithm, modified grey wolf optimizer (MGWO), for solving the DSP problem. MGWO inherits the main idea of the hierarchy and hunting mechanism of the original grey wolf optimizer (GWO). Three new operators are designed in MGWO to ensure the feasibility of solutions under the complex constraint of disassembly precedence. The feasible solution generator (FSG) is designed to obtain feasible disassembly sequences, the neighborhood search operator (NSO) is developed to make wolves (solutions) self-evolving, and the guided search operator (GSO) is used to make the wolf group guided by three leaders of wolves. Two engineering cases are applied to validate the effectiveness of the proposed operators. Then, they and one real-world application are used to compare the MGWO with other reported methods. The results demonstrate that MGWO can solve the DSP problem effectively.
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