Multiobjective scheduling of a reentrant hybrid flowshop

This paper deals with the multiobjective scheduling of a two stages reentrant hybrid flow shop. The system studied here is reentrant: jobs have to be processed more than once at each stage which is made of several identical parallel machines. Furthermore, the sequence is the same on each stage. In this study the two objectives are the minimization of both the maximum completion time and the sum of the tardiness. Two evolutionary algorithms are proposed : our Lorenz-Non dominated Sorting Genetic Algorithm (L-NSGA) and the Strength Pareto Evolutionary Algorithm version 2 (SPEA2). Several configurations of the system are tested and the results of the two algorithms are compared with the Pareto optimal front with respect to two different measures. The results show that our L-NSGA is more efficient than SPEA2 in 81% of the configurations. Furthermore the L-NSGA reaches optimal solutions for some instances.

[1]  Haoxun Chen,et al.  Multi-objective Supply Chain Optimization: An Industrial Case Study , 2007, EvoWorkshops.

[2]  Maw-Sheng Chern,et al.  Multi-family scheduling in a two-machine reentrant flow shop with setups , 2008, Eur. J. Oper. Res..

[3]  Wen Lea Pearn,et al.  Solution strategies for multi-stage wafer probing scheduling problem with reentry , 2008, J. Oper. Res. Soc..

[4]  Jen-Shiang Chen,et al.  Minimizing makespan in reentrant flow-shops using hybrid tabu search , 2007 .

[5]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[6]  Lionel Amodeo,et al.  New multi-objective method to solve reentrant hybrid flow shop scheduling problem , 2010, Eur. J. Oper. Res..

[7]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[8]  Lionel Amodeo,et al.  Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion , 2007 .

[9]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[10]  J.-S. Chen,et al.  Minimizing makespan in re-entrant permutation flow-shops , 2003, J. Oper. Res. Soc..

[11]  Jen-Shiang Chen,et al.  A branch and bound procedure for the reentrant permutation flow-shop scheduling problem , 2006 .

[12]  Lionel Amodeo,et al.  Reentrant lines scheduling and Lorenz dominance: a comparative study , 2008 .

[13]  S. Ruzika,et al.  Approximation Methods in Multiobjective Programming , 2005 .

[14]  Dong-Ho Lee,et al.  Scheduling algorithms for two-stage reentrant hybrid flow shops: minimizing makespan under the maximum allowable due dates , 2009 .

[15]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[16]  Chun-Hung Chen,et al.  Efficient Simulation-Based Composition of Scheduling Policies by Integrating Ordinal Optimization With Design of Experiment , 2007, IEEE Transactions on Automation Science and Engineering.

[17]  Hyun Joon Shin,et al.  A scheduling algorithm for the reentrant shop: an application in semiconductor manufacture , 2007 .

[18]  James C. Chen,et al.  A study of the flexible job shop scheduling problem with parallel machines and reentrant process , 2008 .

[19]  Appa Iyer Sivakumar,et al.  Job shop scheduling techniques in semiconductor manufacturing , 2006 .

[20]  Hisao Ishibuchi,et al.  Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling , 2003, IEEE Trans. Evol. Comput..

[21]  John W. Fowler,et al.  Genetic algorithm-based subproblem solution procedures for a modified shifting bottleneck heuristic for complex job shops , 2007, Eur. J. Oper. Res..

[22]  Haoxun Chen,et al.  Ant colony optimization for solving an industrial layout problem , 2007, Eur. J. Oper. Res..

[23]  Stephen C. Graves,et al.  Scheduling of re-entrant flow shops , 1983 .

[24]  Yih-Long Chang,et al.  Multi-criteria sequence-dependent job shop scheduling using genetic algorithms , 2009, Comput. Ind. Eng..