A biased-randomized simheuristic for the distributed assembly permutation flowshop problem with stochastic processing times

Modern manufacturing systems are composed of several stages. We consider a manufacturing environment in which different parts of a product are completed in a first stage by a set of distributed flowshop lines, and then assembled in a second stage. This is known as the distributed assembly permutation flowshop problem (DAPFSP). This paper studies the stochastic version of the DAPFSP, in which processing and assembly times are random variables. Besides minimizing the expected makespan, we also discuss the need for considering other measures of statistical dispersion in order to account for risk. A hybrid algorithm is proposed for solving this NP-hard and stochastic problem. Our approach integrates biased randomization and simulation techniques inside a metaheuristic framework. A series of computational experiments contribute to illustrate the effectiveness of our approach.