文献类型：期刊文献

英文题名：Particle swarm optimization combined with genetic operators for job shop scheduling problem with fuzzy processing time

作者：Niu, Qun[1,2]; Jiao, Bin[3]; Gu, Xingsheng[1]

机构：[1] Research Institution of Automation, East China University of Science and Technology, Shanghai, 200237, China; [2] Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China; [3] Electrical Engineering Department, Shanghai DianJi University, Shanghai, 200240, China

年份：2008

卷号：205

期号：1

起止页码：148

外文期刊名：Applied Mathematics and Computation

收录：EI(收录号：20084311657354)

语种：英文

外文关键词：Job shop scheduling - Problem solving - Scheduling algorithms - Fuzzy rules - Computational complexity - Behavioral research - Particle swarm optimization (PSO)

摘要：Job shop scheduling problem is a NP-hard problem. The processing time for each job is often imprecise in many real-world applications and the imprecision in the data is critical for the scheduling procedures. Therefore, job shop scheduling problem with fuzzy processing time is addressed in the paper. The processing time is described by triangular fuzzy numbers. The objective is to find a job sequence that minimizes the makespan and the uncertainty of the makespan by using an approach for ranking fuzzy numbers. The particle swarm optimization (PSO) is a randomized, population-based optimization method that was inspired by the flocking behavior of birds and human social interactions. PSO has been successfully applied to various real-world applications, but there is a little literature reported regarding application to scheduling problems as it was unsuitable for them. In this paper, PSO is redefined and modified by introducing genetic operators such as crossover and mutation operator to update the particles. We call this particle swarm optimization combined with genetic operators (GPSO). This is successfully employed to solve the formulated problem. Ten benchmarks with fuzzy processing time are used to test GPSO. The feasibility, as well as the efficiency of the proposed method, is assessed in comparison with genetic algorithm (GA). ? 2008 Elsevier Inc. All rights reserved.