An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm
Assembly sequence planning (ASP) becomes one of the major challenges in product design and manufacturing. A good assembly sequence leads to reduced costs and duration in the manufacturing process. However, assembly sequence planning is known to be a classical NP-hard combinatorial optimization probl...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer Verlag
2015
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/15700/1/An_assembly_sequence_planning_approach_with_a_rule-based_multi-state.pdf |
_version_ | 1825720719168765952 |
---|---|
author | Ibrahim, I. Ibrahim, Z. Ahmad, Hamzah Jusof, M.F.M. Yusof, Z.M. Nawawi, S.W. Mubin, M. |
author_facet | Ibrahim, I. Ibrahim, Z. Ahmad, Hamzah Jusof, M.F.M. Yusof, Z.M. Nawawi, S.W. Mubin, M. |
author_sort | Ibrahim, I. |
collection | UM |
description | Assembly sequence planning (ASP) becomes one of the major challenges in product design and manufacturing. A good assembly sequence leads to reduced costs and duration in the manufacturing process. However, assembly sequence planning is known to be a classical NP-hard combinatorial optimization problem; ASP with many product components becomes more difficult to solve. In this paper, an approach based on a new variant of the gravitational search algorithm (GSA) called the rule-based multi-state gravitational search algorithm (RBMSGSA) is used to solve the assembly sequence planning problem. As in the gravitational search algorithm, the RBMSGSA incorporates Newton's law of gravity, the law of motion, and a rule that makes each assembly component of each individual solution occur once based on precedence constraints; the best feasible sequence of assembly can then be determined. To verify the feasibility and performance of the proposed approach, a case study has been performed and a comparison has been conducted against other three approaches based on simulated annealing (SA), a genetic algorithm (GA), and binary particle swarm optimization (BPSO). The experimental results show that the proposed approach has achieved significant improvement in performance over the other methods studied. |
first_indexed | 2024-03-06T05:39:25Z |
format | Article |
id | um.eprints-15700 |
institution | Universiti Malaya |
language | English |
last_indexed | 2024-03-06T05:39:25Z |
publishDate | 2015 |
publisher | Springer Verlag |
record_format | dspace |
spelling | um.eprints-157002019-08-06T07:58:04Z http://eprints.um.edu.my/15700/ An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm Ibrahim, I. Ibrahim, Z. Ahmad, Hamzah Jusof, M.F.M. Yusof, Z.M. Nawawi, S.W. Mubin, M. T Technology (General) TA Engineering (General). Civil engineering (General) Assembly sequence planning (ASP) becomes one of the major challenges in product design and manufacturing. A good assembly sequence leads to reduced costs and duration in the manufacturing process. However, assembly sequence planning is known to be a classical NP-hard combinatorial optimization problem; ASP with many product components becomes more difficult to solve. In this paper, an approach based on a new variant of the gravitational search algorithm (GSA) called the rule-based multi-state gravitational search algorithm (RBMSGSA) is used to solve the assembly sequence planning problem. As in the gravitational search algorithm, the RBMSGSA incorporates Newton's law of gravity, the law of motion, and a rule that makes each assembly component of each individual solution occur once based on precedence constraints; the best feasible sequence of assembly can then be determined. To verify the feasibility and performance of the proposed approach, a case study has been performed and a comparison has been conducted against other three approaches based on simulated annealing (SA), a genetic algorithm (GA), and binary particle swarm optimization (BPSO). The experimental results show that the proposed approach has achieved significant improvement in performance over the other methods studied. Springer Verlag 2015-07 Article PeerReviewed application/pdf en http://eprints.um.edu.my/15700/1/An_assembly_sequence_planning_approach_with_a_rule-based_multi-state.pdf Ibrahim, I. and Ibrahim, Z. and Ahmad, Hamzah and Jusof, M.F.M. and Yusof, Z.M. and Nawawi, S.W. and Mubin, M. (2015) An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm. The International Journal of Advanced Manufacturing Technology, 79 (5-8). pp. 1363-1376. ISSN 0268-3768, DOI https://doi.org/10.1007/s00170-015-6857-0 <https://doi.org/10.1007/s00170-015-6857-0>. http://link.springer.com/article/10.1007/s00170-015-6857-0 10.1007/s00170-015-6857-0 |
spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) Ibrahim, I. Ibrahim, Z. Ahmad, Hamzah Jusof, M.F.M. Yusof, Z.M. Nawawi, S.W. Mubin, M. An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm |
title | An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm |
title_full | An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm |
title_fullStr | An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm |
title_full_unstemmed | An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm |
title_short | An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm |
title_sort | assembly sequence planning approach with a rule based multi state gravitational search algorithm |
topic | T Technology (General) TA Engineering (General). Civil engineering (General) |
url | http://eprints.um.edu.my/15700/1/An_assembly_sequence_planning_approach_with_a_rule-based_multi-state.pdf |
work_keys_str_mv | AT ibrahimi anassemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT ibrahimz anassemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT ahmadhamzah anassemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT jusofmfm anassemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT yusofzm anassemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT nawawisw anassemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT mubinm anassemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT ibrahimi assemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT ibrahimz assemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT ahmadhamzah assemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT jusofmfm assemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT yusofzm assemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT nawawisw assemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm AT mubinm assemblysequenceplanningapproachwitharulebasedmultistategravitationalsearchalgorithm |