3 research outputs found
A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times
This paper describes a simulation optimization algorithm for the Permutation Flow shop
Problem with Stochastic processing Times (PFSPST). The proposed algorithm combines
Monte Carlo simulation with an Iterated Local Search metaheuristic in order to deal with
the stochastic behavior of the problem. Using the expected makespan as initial minimization
criterion, our simheuristic approach is based on the assumption that high-quality solutions
(permutations of jobs) for the deterministic version of the problem are likely to be
high-quality solutions for the stochastic version i.e., a correlation will exist between both
sets of solutions, at least for moderate levels of variability in the stochastic processing
times. No particular assumption is made on the probability distributions modeling each
job-machine processing times. Our approach is able to solve, in just a few minutes or even
less, PFSPST instances with hundreds of jobs and dozens of machines. Also, the paper
proposes the use of reliability analysis techniques to analyze simulation outcomes or
historical observations on the random variable representing the makespan associated with
a given solution. This way, criteria other than the expected makespan can be considered by
the decision maker when comparing different alternative solutions. A set of classical
benchmarks for the deterministic version of the problem are adapted and tested under
several scenarios, each of them characterized by a different level of uncertainty variance
level of job-machine processing times.This work has been partially supported by the Spanish Ministry of Science and Innovation (TRA2010-21644-C03). It has been developed in the context of the IN3-ICSO program and the CYTED-HAROSA network (http://dpcs.uoc.edu).Juan, AA.; Barrios, BB.; Vallada Regalado, E.; Riera, D.; Jorba, J. (2014). A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times. Simulation Modelling Practice and Theory. 46:101-117. https://doi.org/10.1016/j.simpat.2014.02.005S1011174