Title :
A Hybrid PSO Algorithm for Job-Shop Scheduling Problems with Fuzzy Processing Time and Fuzzy Due Date
Author :
Ming, Guo Fang ; Qiong, Liu
Author_Institution :
Digital Eng. & Simulation Res. Center, HUST, Wuhan, China
Abstract :
This paper presents a hybrid PSO (HPSO) algorithm to the solution of job-shop fuzzy scheduling problem. The proposed algorithm uses processing encoding random key to generate initial population, takes parameter uniformity crossover operator as particle swarm´s update operator, and evaluates each particle properties according to customer satisfaction, and then completes particle individual extremum and neighborhood extremum update according to the above-mentioned evaluation. The algorithm utilizes neighborhood knowledge to direct its local search procedure, which overcome the blindness or randomness introduced by meta-heuristics. Simulation results show that HPSO algorithm can speed up convergence as well as improve the quality of shop scheduling solution.
Keywords :
fuzzy set theory; job shop scheduling; particle swarm optimisation; customer satisfaction; fuzzy due date; fuzzy processing time; hybrid PSO algorithm; job-shop scheduling; local search procedure; neighborhood extremum update; parameter uniformity crossover operator; particle individual extremum; random key; Customer satisfaction; Delay effects; Genetic algorithms; Job production systems; Job shop scheduling; Optimal scheduling; Particle swarm optimization; Processor scheduling; Scheduling algorithm; Single machine scheduling;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.216