Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem
This paper proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem....
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Imperial College Press
2011
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/32639 |
| _version_ | 1848753718644703232 |
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| author | Ling, S. Jiang, F. Nguyen, H. Chan, Kit Yan |
| author_facet | Ling, S. Jiang, F. Nguyen, H. Chan, Kit Yan |
| author_sort | Ling, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly. |
| first_indexed | 2025-11-14T08:28:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-32639 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:28:58Z |
| publishDate | 2011 |
| publisher | Imperial College Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-326392017-09-13T16:08:11Z Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem Ling, S. Jiang, F. Nguyen, H. Chan, Kit Yan scheduling particle swarm optimization fuzzy logic Flow shop roulette wheel mechanism This paper proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly. 2011 Journal Article http://hdl.handle.net/20.500.11937/32639 10.1142/S1469026811003136 Imperial College Press restricted |
| spellingShingle | scheduling particle swarm optimization fuzzy logic Flow shop roulette wheel mechanism Ling, S. Jiang, F. Nguyen, H. Chan, Kit Yan Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem |
| title | Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem |
| title_full | Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem |
| title_fullStr | Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem |
| title_full_unstemmed | Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem |
| title_short | Hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem |
| title_sort | hybrid fuzzy logic-based particle swarm optimization for flow shop scheduling problem |
| topic | scheduling particle swarm optimization fuzzy logic Flow shop roulette wheel mechanism |
| url | http://hdl.handle.net/20.500.11937/32639 |