title: Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.
abstract: A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm.
authors.affiliation: State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang 110819, China ; College of Computer Science, Liaocheng University, Liaocheng 252059, China.
authors.forename: Jun-qing
authors.initials: JQ
authors.lastname: Li
authors.affiliation: State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang 110819, China.
authors.forename: Quan-ke
authors.initials: QK
authors.lastname: Pan
authors.affiliation: State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang 110819, China.
authors.forename: Kun
authors.initials: K
authors.lastname: Mao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D017598:Efficiency, Organizational; D008962:Models, Theoretical; D040841:Resource Allocation / Q000379:methods*