An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective algorithm of combining PSO with AIS for solving the minimum makespan problem of job-shop scheduling is proposed. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. In the artificial immune system, the models of vaccination and receptor editing are designed to improve the immune performance. The proposed algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined by using a set of benchmark instances with various sizes and levels of hardness and is compared with other approaches reported in some existing literature works. The computational results validate the effectiveness of the proposed approach.

[1]  John B. Jensen,et al.  Evaluation of scheduling rules with commensurate customer priorities in job shops , 1995 .

[2]  Ehl Emile Aarts,et al.  A computational study of constraint satisfaction for multiple capacitated job shop scheduling , 1996 .

[3]  Renata M. Aiex,et al.  Parallel GRASP with path-relinking for job shop scheduling , 2003, Parallel Comput..

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

[5]  G. Thompson,et al.  Algorithms for Solving Production-Scheduling Problems , 1960 .

[6]  Jun Zhang,et al.  Implementation of an Ant Colony Optimization technique for job shop scheduling problem , 2006 .

[7]  Can Akkan,et al.  The two-machine flowshop total completion time problem: Improved lower bounds and a branch-and-bound algorithm , 2004, Eur. J. Oper. Res..

[8]  Michael Kolonko,et al.  Some new results on simulated annealing applied to the job shop scheduling problem , 1999, Eur. J. Oper. Res..

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

[10]  L. Darrell Whitley,et al.  Problem difficulty for tabu search in job-shop scheduling , 2003, Artif. Intell..

[11]  Bin Jiao,et al.  A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan , 2006, Appl. Math. Comput..

[12]  Yin Ai-hua,et al.  An improved shifting bottleneck procedure for the job shop scheduling problem , 2004 .

[13]  Robert Klein,et al.  Bidirectional planning: improving priority rule-based heuristics for scheduling resource-constrained projects , 2000, Eur. J. Oper. Res..

[14]  M. Caramanis,et al.  Efficient Lagrangian relaxation algorithms for industry size job-shop scheduling problems , 1998 .

[15]  Erwin Pesch,et al.  Evolution based learning in a job shop scheduling environment , 1995, Comput. Oper. Res..

[16]  Beatrice M. Ombuki-Berman,et al.  Local Search Genetic Algorithms for the Job Shop Scheduling Problem , 2004, Applied Intelligence.

[17]  Stéphane Dauzère-Pérès,et al.  An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search , 1997, Ann. Oper. Res..

[18]  Xu Gang,et al.  Deadlock-free scheduling strategy for automated production cell , 2003, Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).

[19]  Ling Wang,et al.  A Modified Genetic Algorithm for Job Shop Scheduling , 2002 .

[20]  Shengxiang Yang,et al.  Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling , 2000, IEEE Trans. Neural Networks Learn. Syst..

[21]  Peter B. Luh,et al.  An alternative framework to Lagrangian relaxation approach for job shop scheduling , 2003, Eur. J. Oper. Res..

[22]  Carlos A. Coello Coello,et al.  Use of an Artificial Immune System for Job Shop Scheduling , 2003, ICARIS.

[23]  Chinyao Low,et al.  A robust simulated annealing heuristic for flow shop scheduling problems , 2004 .

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

[25]  Shengxiang Yang,et al.  A new adaptive neural network and heuristics hybrid approach for job-shop scheduling , 2001, Comput. Oper. Res..

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

[27]  FEDERICO DELLA CROCE,et al.  A genetic algorithm for the job shop problem , 1995, Comput. Oper. Res..

[28]  Emanuela Merelli,et al.  A tabu search method guided by shifting bottleneck for the job shop scheduling problem , 2000, Eur. J. Oper. Res..

[29]  V. P. Arunachalam,et al.  Improved solutions for job shop scheduling problems through genetic algorithm with a different method of schedule deduction , 2006 .

[30]  Emin Gundogar,et al.  Fuzzy priority rule for job shop scheduling , 2004, J. Intell. Manuf..

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

[32]  Roland Heilmann,et al.  Discrete Optimization A branch-and-bound procedure for the multi-mode resource-constrained project scheduling problem with minimum and maximum time lags , 2002 .

[33]  Tai-Yue Wang,et al.  A revised simulated annealing algorithm for obtaining the minimum total tardiness in job shop scheduling problems , 2000, Int. J. Syst. Sci..

[34]  E. Nowicki,et al.  A Fast Taboo Search Algorithm for the Job Shop Problem , 1996 .

[35]  Tung-Kuan Liu,et al.  Improved genetic algorithm for the job-shop scheduling problem , 2006 .

[36]  Pierre Borne,et al.  A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[37]  Wei-Yen Wang,et al.  Discrete modelling of uncertain continuous systems having an interval structure using higher-order integrators , 2000, Int. J. Syst. Sci..

[38]  J. Billaut,et al.  A dynamic programming algorithm for scheduling jobs in a two-machine open shop with an availability constraint , 2002, J. Oper. Res. Soc..

[39]  Chris N. Potts,et al.  Dynamic programming and decomposition approaches for the single machine total tardiness problem , 1987 .

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

[41]  Ihsan Sabuncuoglu,et al.  Job shop scheduling with beam search , 1999, Eur. J. Oper. Res..

[42]  Haibin Yu,et al.  Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling , 2001 .

[43]  S. Binato,et al.  A GRASP FOR JOB SHOP SCHEDULING , 2001 .

[44]  Ismail Hakki Cedimoglu,et al.  The strategies and parameters of tabu search for job-shop scheduling , 2004, J. Intell. Manuf..

[45]  Christian Bierwirth,et al.  Production Scheduling and Rescheduling with Genetic Algorithms , 1999, Evolutionary Computation.