Solving Multi-Objective Flexible Job Shop Scheduling Problems Using Immune Algorithm

Scheduling for the flexible job-shops has great importance in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization methods because of the high computational complexity. Considering several optimization criteria in this problem will bring on additional complexity and new problems. So it makes traditional methods not practical, and urges new ways of optimization like meta-heuristic algorithms. Immune algorithm is an evolutionary computation technique imitating the behavior of biological immune systems in body. We developed an easily implemented approach for the multiobjective flexible job-shop scheduling problems (FJSP). The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the multi-objective FJSP, especially for problems on a large scale.

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

[2]  Zuobao Wu,et al.  Multiagent scheduling method with earliness and tardiness objectives in flexible job shops , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Luca Maria Gambardella,et al.  Effective Neighborhood Functions for the Flexible Job Shop Problem , 1998 .

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

[5]  Mitsuo Gen,et al.  A hybrid of genetic algorithm and bottleneck shifting for flexible job shop scheduling problemA hybrid of genetic algorithm and bottleneck shifting for flexible job shop scheduling problem , 2006, GECCO '06.

[6]  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..

[7]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: Part I-Basic Theory and Applications , 1999 .

[8]  Li Lin,et al.  Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems , 1999 .

[9]  Pierre Borne,et al.  Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[10]  I L Weissman,et al.  How the immune system develops. , 1993, Scientific American.

[11]  Johann L. Hurink,et al.  Tabu search for the job-shop scheduling problem with multi-purpose machines , 1994 .

[12]  J. C. Tay,et al.  Applying the Clonal Selection Principle to Find Flexible Job-Shop Schedules , 2005, ICARIS.

[13]  Paolo Brandimarte,et al.  Routing and scheduling in a flexible job shop by tabu search , 1993, Ann. Oper. Res..

[14]  Mitsuo Gen,et al.  Multistage-Based Genetic Algorithm for Flexible Job-Shop Scheduling Problem , 2009 .

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

[16]  Alberto Gómez,et al.  A knowledge-based evolutionary strategy for scheduling problems with bottlenecks , 2003, Eur. J. Oper. Res..

[17]  F. Burnet.  The clonal selection theory of acquired immunity , 1959 .

[18]  Nhu Binh Ho,et al.  GENACE: an efficient cultural algorithm for solving the flexible job-shop problem , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[19]  Jie Gao,et al.  A Hybrid of Genetic Algorithm and Bottleneck Shifting for Flexible Job-Shop Scheduling Problem , 2005 .

[20]  Peter Brucker,et al.  Job-shop scheduling with multi-purpose machines , 1991, Computing.