Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems

•We present an Automatic Algorithm Design system for the hybrid flowshop problem.•Algorithm components are combined according to a grammar to generate new algorithms.•New algorithms are compared in three objectives with the state-of-the-art algorithms.•Experiments prove our approach to be competitiv...

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Bibliographic Details
Published in:European journal of operational research Vol. 282; no. 3; pp. 835 - 845
Main Authors: Alfaro-Fernández, Pedro, Ruiz, Rubén, Pagnozzi, Federico, Stützle, Thomas
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2020
Subjects:
Automatic algorithm configuration
Automatic Algorithm Design
Hybrid flowshop
Scheduling
Automatic Algorithm Design
Automatic algorithm configuration
Scheduling
Hybrid flowshop
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:•We present an Automatic Algorithm Design system for the hybrid flowshop problem.•Algorithm components are combined according to a grammar to generate new algorithms.•New algorithms are compared in three objectives with the state-of-the-art algorithms.•Experiments prove our approach to be competitive against state-of-the-art algorithms. Industrial production scheduling problems are challenges that researchers have been trying to solve for decades. Many practical scheduling problems such as the hybrid flowshop are NP-hard. As a result, researchers resort to metaheuristics to obtain effective and efficient solutions. The traditional design process of metaheuristics is mainly manual, often metaphor-based, biased by previous experience and prone to producing overly tailored methods that only work well on the tested problems and objectives. In this paper, we use an Automatic Algorithm Design (AAD) methodology to eliminate these limitations. AAD is capable of composing algorithms from components with minimal human intervention. We test the proposed AAD for three different optimization objectives in the hybrid flowshop. Comprehensive computational and statistical testing demonstrates that automatically designed algorithms outperform specifically tailored state-of-the-art methods for the tested objectives in most cases.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2019.10.004