A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop

The resolution of workshop problems such as the Flow Shop or the Job Shop has a great importance in many industrial areas. The criteria to optimize are generally the minimization of the makespan or the tardiness. However, few are the resolution approaches that take into account those different criteria simultaneously. This paper presents an approach based on hybrid genetic algorithms adapted to the multicriteria case. Several strategies of selection and diversity maintaining are presented. Their performances are evaluated and compared using different benchmarks. A parallel model is also proposed and implemented for the hybrid metaheuristic. It allows to increase the population size and the number of generations, and then leads to better results.

[1]  Eugeniusz Nowicki,et al.  The permutation flow shop with buffers: A tabu search approach , 1999, Eur. J. Oper. Res..

[2]  D. Chaudhuri,et al.  An efficient heuristic approach to the scheduling of jobs in a flowshop , 1992 .

[3]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[4]  Kikuo Fujita,et al.  MULTI-OBJECTIVE OPTIMAL DESIGN OF AUTOMOTIVE ENGINE USING GENETIC ALGORITHM , 1998 .

[5]  Teofilo F. Gonzalez,et al.  Flowshop and Jobshop Schedules: Complexity and Approximation , 1978, Oper. Res..

[6]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[7]  P. Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

[8]  Jack Heller,et al.  Some Numerical Experiments for an M × J Flow Shop and its Decision-Theoretical Aspects , 1960 .

[9]  Peter J. Fleming,et al.  Multiobjective genetic algorithms made easy: selection sharing and mating restriction , 1995 .

[10]  G. Rand.  Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop , 1982 .

[11]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[12]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[13]  Zbigniew Michalewicz,et al.  Evolutionary Computation 2 , 2000 .

[14]  Jatinder N. D. Gupta,et al.  Technical Note - An Improved Combinatorial Algorithm for the Flowshop-Scheduling Problem , 1971, Oper. Res..

[15]  J. Hunsucker,et al.  BRANCH AND BOUND ALGORITHM FOR THE FLOW SHOP WITH MULTIPLE PROCESSORS , 1991 .

[16]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[17]  Peter J. Bentley,et al.  Finding Acceptable Solutions in the Pareto-Optimal Range using Multiobjective Genetic Algorithms , 1998 .