A Multicriteria Simheuristic Approach for Solving a Stochastic Permutation Flow Shop Scheduling Problem

This paper proposes a hybridized simheuristic approach that couples greedy randomized adaptive search procedure (GRASP), Monte Carlo simulation, Pareto archived evolution strategy (PAES), and an analytic hierarchy process (AHP), in order to solve multicriteria stochastic permutation flow shop problem with processing times sequence-dependent setup times. For the decisional criteria, proposed considers four objective functions, including two quantitative qualitative criteria. While expected value standard deviation of earliness/tardiness jobs are included criteria address robust solution just-in-time environment, this also includes assessment product customer importance appraise weighted priority for each job. An experimental design was carried out several study instances test effects times, obtained through lognormal uniform probability distributions three levels coefficients variation, settled as 0.3, 0.4, 0.5. The results show both variation have significant effect on decision selected. In addition, analytical hierarchical makes it possible choose best sequence exhibited by frontier adjusts more adequately decision-makers’ objectives.