Abstract
This paper proposes a hybridized simheuristic approach that couples a greedy randomized adaptive search procedure (GRASP), a Monte Carlo simulation, a Pareto archived evolution strategy (PAES), and an analytic hierarchy process (AHP), in order to solve a multicriteria stochastic permutation flow shop problem with stochastic processing times and stochastic sequence-dependent setup times. For the decisional criteria, the proposed approach considers four objective functions, including two quantitative and two qualitative criteria. While the expected value and the standard deviation of the earliness/tardiness of jobs are included in the quantitative criteria to address a robust solution in a just-in-time environment, this approach also includes a qualitative assessment of the product and customer importance in order to appraise a weighted priority for each job. An experimental design was carried out in several study instances of the flow shop problem to test the effects of the processing times and sequence-dependent setup times, obtained through lognormal and uniform probability distributions with three levels of coefficients of variation, settled as 0.3, 0.4, and 0.5. The results show that both probability distributions and coefficients of variation have a significant effect on the four decision criteria selected. In addition, the analytical hierarchical process makes it possible to choose the best sequence exhibited by the Pareto frontier that adjusts more adequately to the decision-makers’ objectives.
Highlights
This paper proposes a simheuristic technique that integrates Monte Carlo simulation into a greedy randomized adaptive search procedure (GRASP) metaheuristic hybridized with the Pareto archived evolution strategy (PAES)
The results showed that all main effects are statistically significant on the four objective functions except probability distribution of processing times (PDPT) for E[CI] and E[PI] and the probability distribution of setup times (PDST) and coefficient of variation of setup times (CVST) for E[PI]
This paper presented a multicriteria simheuristic that hybridizes a GRASP, a PAES
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. While Chang and Lo [8] proposed a hybridized genetic algorithm, tabu search, analytic hierarchy process, and fuzzy theory to solve the problem, Chang et al [9] proposed a hybridization of an ant colony algorithm and an analytic hierarchy process for solving the FSP Another approach minimized the expected costs of tardiness as a quantitative criterion and strategic customer importance as a qualitative criterion in a stochastic hybrid. Simheuristics have been successfully used in vehicle routing problems [11,12,13], inventory routing problems [14], facility location problems [15], and scheduling problems [16,17,18,19] In this sense, this paper attempts to contribute to the literature by proposing a systematic technique for solving the stochastic permutation flow shop problem (SPFSP) considering stochastic processing times and sequence-dependent setup times to optimize multiple criteria.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
