A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem

Abstract

As the interest of practitioners and researchers in scheduling in a multi-factory environment is growing, there is an increasing need to provide ecient algorithms for this type of decision problems, characterised by simultaneously addressing the assignment of jobs to dierent factories/workshops and their subsequent scheduling.Here we address the so-called distributed permutation owshop scheduling problem, in which a set of jobs has to be scheduled over a number of identical factories, each one with its machines arranged as a owshop. Several heuristics have been designed for this problem, although there is no direct comparison among them. In this paper, we propose a new heuristic which exploits the specic structure of the problem. The computational experience carried out on a well-known testbed shows that the proposed heuristic outperforms existing state-of-the-art heuristics, being able to obtain better upper bounds for more than one quarter of the problems in the testbed.

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

“…Constraint (5) indicates that the mth machine of the fth factory has the ability to process the J j . Constraint (6) ensures that the jobs are assigned to the factory, and it cannot be assigned to another factory. The maximum completion times of all jobs are compared by constraints (7) and (8).…”

A computational evaluation of constructive and improvement heuristics for the blocking flow shop to minimise total flowtime

“…Fernandez-Viagas & Framinan, 2015b ); and the distributed PFSP to minimise makespan (see e.g. Naderi &Framinan, 2015a ). However, to the best of our knowledge, they have not been applied to the BFSP so far.…”

Multi-objective unrelated parallel machine scheduling: a Tabu-enhanced iterated Pareto greedy algorithm

“…Owing to its simplicity and extensibility, a variety of effective and efficient IG-based algorithms have been proposed to solve single-objective scheduling problems, including the flowshop scheduling problem Stützle 2007, 2008; Baraz and Mosheiov 2008;Ying 2008), the single-machine scheduling problem (Ying, Lin, and Huang 2009), the hybrid flowshop scheduling problem (Ying 2009), the PMSP (Ying and Cheng 2010;Ying 2012), the distributed permutation flowshop problem (Lin, Ying, and Huang 2013;Fernandez-Viagas and Framinan 2015), and the personnel task scheduling problem (Lin and Ying 2014b). The literature indicates that these state-of-the-art IG-based algorithms effectively solve the above single-objective scheduling problems.…”