Work

Decomposition heuristics for the Hybrid Flexible Flowshop with transportation times

Year: 2025

Type: article

Abstract: This paper proposes efficient heuristic approaches for the Hybrid Flexible Flowshop with Transportation Times (HFFTT), an extension of both the Hybrid Flowshop (HFP) and Hybrid Flexible Flowshop (HFF) problems. Two classes of heuristics are introduced: Constraint Programming (CP)-based heuristics and decomposition heuristics. While the CP-based heuristics can be applied to any instance of the HFFTT, the decomposition heuristics are specifically designed for “rectangular” instances, where the number of machines is the same at each stage. Both approaches are compared against two iterated greedy algorithms adapted from the state-of-the-art, one of which is tailored exclusively for rectangular instances. The results show that the CP-based heuristics achieve the best performance for non-rectangular instances, while the decomposition heuristics strongly dominate all other approaches for rectangular instances, as soon as the size of the instances considered is large enough. We show that most of the results obtained can be generalized to the case without transportation times, where the HFFTT problem reduces to the HFF.

Source: Computers & Operations Research

DOI: 10.1016/j.cor.2025.107145

Authors: Michele Garraffa, Helmut Simonis, Barry O’Sullivan, Eddie Armstrong

Institutions: Science Foundation Ireland, University College Cork, Johnson & Johnson (Ireland)

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,570