Iterated Greedy methods for the distributed permutation flowshop scheduling problem

Abstract

Large manufacturing firms operate more than one production center. As a result, in relation to scheduling problems, which factory manufactures which product is an important consideration. In this paper we study an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure. The objective is to minimize the maximum completion time or makespan among all factories. The resulting problem is known as the distributed permutation flowshop and has attracted considerable interest over the last few years. Contrary to the recent trend in the scheduling literature, where complex nature-inspired or metaphor-based methods are often proposed, we present simple Iterated Greedy algorithms that have performed well in related problems. Improved initialization, construction and destruction procedures, along with a local search with a strong intensification are proposed. The result is a very effective algorithm with little problem-specific knowledge that is shown to provide demonstrably better solutions in a comprehensive and thorough computational and statistical campaign.