An Improved Multiobjective PSO for the Scheduling Problem of Panel Block Construction

• Published in: Discrete dynamics in nature and society Vol. 2016; no. 2016; pp. 1 - 13
• Main Authors: Qian, Weixin, Wang, Xuefeng, Liu, Cungen, Yang, Zhi
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2016; Hindawi Limited; Wiley
• Subjects: Algorithms; Archives & records; Construction; Employment; Fuzzy; Fuzzy sets; Genetic algorithms; Job shops; Methods; Mutation; Objectives; Optimization theory; Panels; Permutations; Production management; Production scheduling; Scheduling; Scheduling (Management); Shipbuilding industry; Studies; Swarm intelligence
• ISSN: 1026-0226; 1607-887X
• DOI: 10.1155/2016/5413520

Uncertainty is common in ship construction. However, few studies have focused on scheduling problems under uncertainty in shipbuilding. This paper formulates the scheduling problem of panel block construction as a multiobjective fuzzy flow shop scheduling problem (FSSP) with a fuzzy processing time, a fuzzy due date, and the just-in-time (JIT) concept. An improved multiobjective particle swarm optimization called MOPSO-M is developed to solve the scheduling problem. MOPSO-M utilizes a ranked-order-value rule to convert the continuous position of particles into the discrete permutations of jobs, and an available mapping is employed to obtain the precedence-based permutation of the jobs. In addition, to improve the performance of MOPSO-M, archive maintenance is combined with global best position selection, and mutation and a velocity constriction mechanism are introduced into the algorithm. The feasibility and effectiveness of MOPSO-M are assessed in comparison with general MOPSO and nondominated sorting genetic algorithm-II (NSGA-II).