Open AccessA fast hybrid particle swarm optimization algorithm for flow shop sequence dependent group scheduling problem
Author(s) -
D. Hajinejad,
Nasser Salmasi,
Reza Mokhtari
Publication year - 2011
Publication title -
scientia iranica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.299
H-Index - 51
eISSN - 2345-3605
pISSN - 1026-3098
DOI - 10.1016/j.scient.2011.05.023
Subject(s) - ant colony optimization algorithms , particle swarm optimization , metaheuristic , flow shop scheduling , mathematical optimization , algorithm , job shop scheduling , computer science , meta optimization , swarm behaviour , mathematics , schedule , operating system
A Particle Swarm Optimization (PSO) algorithm for a Flow Shop Sequence Dependent Group Scheduling (FSDGS) problem, with minimization of total flow time as the criterion (Fm|fmls,Splk,prmu|∑Cj), is proposed in this research. An encoding scheme based on Ranked Order Value (ROV) is developed, which converts the continuous position value of particles in PSO to job and group permutations. A neighborhood search strategy, called Individual Enhancement (IE), is fused to enhance the search and to balance the exploration and exploitation. The performance of the algorithm is compared with the best available meta-heuristic algorithm in literature, i.e. the Ant Colony Optimization (ACO) algorithm, based on available test problems. The results show that the proposed algorithm has a superior performance to the ACO algorithm