An efficient job-shop scheduling algorithm based on particle swarm optimization

The job-shop scheduling problem has attracted many researchers' attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of particle swarm optimization, simulated annealing technique and multi-type individual enhancement scheme is presented to solve the job-shop scheduling problem. The experimental results show that the new proposed job-shop scheduling algorithm is more robust and efficient than the existing algorithms.

[1]  Pin Luarn,et al.  A discrete version of particle swarm optimization for flowshop scheduling problems , 2007, Comput. Oper. Res..

[2]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  P. Aravindan,et al.  A Tabu Search Algorithm for Job Shop Scheduling , 2000 .

[5]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[6]  R. Suresh,et al.  Pareto archived simulated annealing for job shop scheduling with multiple objectives , 2006 .

[7]  Bin Jiao,et al.  A Dual Similar Particle Swarm Optimization Algorithm for Job-Shop Scheduling with Penalty , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[8]  John E. Beasley,et al.  OR-Library: Distributing Test Problems by Electronic Mail , 1990 .

[9]  Eugene L. Lawler,et al.  Sequencing and scheduling: algorithms and complexity , 1989 .

[10]  Hyung Rim Choi,et al.  A hybrid genetic algorithm for the job shop scheduling problems , 2003, Comput. Ind. Eng..

[11]  Ling Wang,et al.  An effective hybrid optimization strategy for job-shop scheduling problems , 2001, Comput. Oper. Res..

[12]  Mauricio G. C. Resende,et al.  Discrete Optimization A hybrid genetic algorithm for the job shop scheduling problem , 2005 .

[13]  Mitsuo Gen,et al.  A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem , 2005, Comput. Ind. Eng..

[14]  Voratas Kachitvichyanukul,et al.  Multiple colony ant algorithm for job-shop scheduling problem , 2008 .

[15]  Eugeniusz Nowicki,et al.  An Advanced Tabu Search Algorithm for the Job Shop Problem , 2005, J. Sched..

[16]  C.K. Wong,et al.  Two simulated annealing-based heuristics for the job shop scheduling problem , 1999, Eur. J. Oper. Res..

[17]  Feng Qian,et al.  A Hybrid Algorithm Based on Particle Swarm Optimization and Simulated Annealing for Job Shop Scheduling , 2007, Third International Conference on Natural Computation (ICNC 2007).

[18]  Zhiming Wu,et al.  An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems , 2005, Comput. Ind. Eng..

[19]  Zhou Pin,et al.  An ant colony algorithm for job shop scheduling problem , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[20]  Weijun Xia,et al.  A hybrid particle swarm optimization approach for the job-shop scheduling problem , 2006 .

[21]  Pingzhi Fan,et al.  An efficient flow-shop scheduling algorithm based on a hybrid particle swarm optimization model , 2007, Expert Syst. Appl..

[22]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[23]  Yanchun Liang,et al.  An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  George L. Nemhauser,et al.  Handbooks in operations research and management science , 1989 .