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Title: Combining simulation with a GRASP metaheuristic for solving the permutation flow-shop problem with stochastic processing times
Author: Ferone, Daniele
Gruler, Aljoscha
Festa, Paola
Juan Pérez, Ángel Alejandro
Keywords: stochastic processes
random variables
probability distribution
mathematical model
Issue Date: Dec-2016
Publisher: Winter Simulation Conference (WSC). Proceedings
Citation: Ferone, D., Gruler, A., Festa, P. & Juan, A.A. (2016). Combining simulation with a GRASP metaheuristic for solving the permutation flow-shop problem with stochastic processing times. Winter Simulation Conference (WSC). Proceedings, 2016(), 2205-2215. doi: 10.1109/WSC.2016.7822262
Published in: Winter Simulation Conference, Washington D.C., EUA, 11-14, desembre de 2016
Project identifier: info:eu-repo/grantAgreement/TRA2013-48180-C3-P
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Abstract: Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for the solution of combinatorial optimization problems. While GRASP is a relatively simple and efficient framework to deal with deterministic problem settings, many real-life applications experience a high level of uncertainty concerning their input variables or even their optimization constraints. When properly combined with the right metaheuristic, simulation (in any of its variants) can be an effective way to cope with this uncertainty. In this paper, we present a simheuristic algorithm that integrates Monte Carlo simulation into a GRASP framework to solve the permutation flow shop problem (PFSP) with random processing times. The PFSP is a well-known problem in the supply chain management literature, but most of the existing work considers that processing times of tasks in machines are deterministic and known in advance, which in some real-life applications (e.g., project management) is an unrealistic assumption.
Language: English
ISBN: 9781509044863
ISSN: 1558-4305MIAR
Appears in Collections:Articles

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