A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem

There are many scheduling problems which are NP-hard in the literature. Several heuristics and dispatching rules are proposed to solve such hard combinatorial optimization problems. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimization problems in view of its characteristic that has high efficiency and that is fit for practical application [1]. Two different scale numerical examples demonstrate the genetic algorithm proposed is efficient and fit for larger scale identical parallel machine scheduling problem for minimizing the makespan. But, even though it is a common problem in the industry, only a small number of studies deal with non-identical parallel machines. In this article, a kind of genetic algorithm based on machine code for minimizing the processing times in non-identical machine scheduling problem is presented. Also triangular fuzzy processing times are used in order to adapt the GA to non-identical parallel machine scheduling problem in the paper. Fuzzy systems are excellent tools for representing heuristic, commonsense rules. That is why we try to use fuzzy systems in this study.

[1]  Pierre Borne,et al.  Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic , 2002, Math. Comput. Simul..

[2]  Cheng Wu,et al.  Scheduling algorithm based on evolutionary computing in identical parallel machine production line , 2003 .

[3]  D. Petrovic,et al.  A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions , 2006, Fuzzy Sets Syst..

[4]  Duc Truong Pham,et al.  Cross breeding in genetic optimisation and its application to fuzzy logic controller design , 1998, Artif. Intell. Eng..

[5]  Dingwei Wang,et al.  The single machine ready time scheduling problem with fuzzy processing times , 2002, Fuzzy Sets Syst..

[6]  Nguyen Van Hop,et al.  The scheduling problem of PCBs for multiple non-identical parallel machines , 2004, Eur. J. Oper. Res..

[7]  Dongwon Kim,et al.  Simulated annealing and genetic algorithm for unrelated parallel machine scheduling considering set-up times , 2006, Int. J. Comput. Appl. Technol..

[8]  Roberto Musmanno,et al.  Robust scheduling of parallel machines with sequence-dependent set-up costs , 2005, Eur. J. Oper. Res..

[9]  Purushothaman Damodaran,et al.  Minimizing makespan on a batch-processing machine with non-identical job sizes using genetic algorithms , 2006 .

[10]  Marin Litoiu,et al.  Fuzzy scheduling with application to real-time systems , 2001, Fuzzy Sets Syst..

[11]  Kudret Demirli,et al.  Fuzzy scheduling of job orders in a two-stage flowshop with batch-processing machines , 2009, Int. J. Approx. Reason..

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  John W. Fowler,et al.  A multi-population genetic algorithm to solve multi-objective scheduling problems for parallel machines , 2003, Comput. Oper. Res..

[14]  H. Ishibuchi,et al.  MOGA: multi-objective genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[15]  Kaushal K. Shukla,et al.  Real-time task scheduling with fuzzy uncertainty in processing times and deadlines , 2008, Appl. Soft Comput..

[16]  Marshall Brinn,et al.  Genetic algorithms for complex, real-time scheduling , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[17]  Chichang Jou,et al.  A genetic algorithm with sub-indexed partitioning genes and its application to production scheduling of parallel machines , 2005, Comput. Ind. Eng..

[18]  Tzung-Pei Hong,et al.  LPT scheduling for fuzzy tasks , 1998, Fuzzy Sets Syst..

[19]  Tarik Çakar,et al.  Parallel robot scheduling to minimize mean tardiness with precedence constraints using a genetic algorithm , 2008, Adv. Eng. Softw..

[20]  Angela C. Sodan,et al.  A multi-stage system in compilation environments , 1999, Fuzzy Sets Syst..

[21]  Yuan-Shin Lee,et al.  Sequencing parallel machining operations by genetic algorithms , 1999 .

[22]  YoungSu Yun.  Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job-shop scheduling problems , 2002 .

[23]  Wu Cheng,et al.  A genetic algorithm for minimizing the makespan in the case of scheduling identical parallel machines , 1999, Artif. Intell. Eng..

[24]  Mitsuo Gen,et al.  Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling , 2003, Appl. Soft Comput..