Use of an Artificial Immune System for Job Shop Scheduling

In this paper, we propose an algorithm based on an artificial immune system to solve job shop scheduling problems. The approach uses clonal selection, hypermutations and a library of antibodies to construct solutions. It also uses a local selection mechanism that tries to eliminate gaps between jobs in order to improve solutions produced by the search mechanism of the algorithm. The proposed approach is compared with respect to GRASP (an enumerative approach) in several test problems taken from the specialized literature. Our results indicate that the proposed algorithm is highly competitive, being able to produce better solutions than GRASP in several cases, at a fraction of its computational cost.

[1]  T. Yamada,et al.  Job shop scheduling , 1997 .

[2]  S. Forrest,et al.  Antibody repertoires and pathogen recognition: the role of germline diversity and somatic hypermutation , 1999 .

[3]  Mitsuo Gen,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation , 1996 .

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

[5]  Upendra Dave,et al.  Heuristic Scheduling Systems , 1993 .

[6]  Miao Li,et al.  Study of population diversity of multiobjective evolutionary algorithm based on immune and entropy principles , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[7]  A. J. Clewett,et al.  Introduction to sequencing and scheduling , 1974 .

[8]  O. Catoni.  Solving Scheduling Problems by Simulated Annealing , 1998 .

[9]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[10]  Edward G. Coffman,et al.  Computer and job-shop scheduling theory , 1976 .

[11]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[12]  S. Binato,et al.  A Grasp for Job Shop Scheduling , 2002 .

[13]  Alan S. Perelson,et al.  The Evolution of Emergent Organization in Immune System Gene Libraries , 1995, ICGA.

[14]  Peter Ross,et al.  The evolution and analysis of potential antibody library for use in job-shop scheduling , 1999 .

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

[16]  Thomas E. Morton,et al.  Heuristic scheduling systems : with applications to production systems and project management , 1993 .

[17]  Peter J. Fleming,et al.  Genetic Algorithms in Engineering Systems , 1997 .

[18]  Tapan P. Bagchi,et al.  Multiobjective Scheduling by Genetic Algorithms , 1999 .

[19]  Mauricio G. C. Resende,et al.  Greedy Randomized Adaptive Search Procedures , 1995, J. Glob. Optim..

[20]  Albert Jones,et al.  Survey of Job Shop Scheduling Techniques , 1999 .

[21]  C. Ribeiro,et al.  Essays and Surveys in Metaheuristics , 2002, Operations Research/Computer Science Interfaces Series.

[22]  Yasuhiro Tsujimura,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies , 1999 .

[23]  A S Perelson,et al.  Evolution and somatic learning in V-region genes. , 1996, Research in immunology.

[24]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[25]  Peter Ross,et al.  Producing robust schedules via an artificial immune system , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[26]  Celso C. Ribeiro,et al.  Greedy Randomized Adaptive Search Procedures , 2003, Handbook of Metaheuristics.

[27]  J. Barnes,et al.  Solving the job shop scheduling problem with tabu search , 1995 .

[28]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[29]  Egon Balas,et al.  The Shifting Bottleneck Procedure for Job Shop Scheduling , 1988 .