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Genetic algorithms for planning and scheduling engineer-to-order production: a systematic review

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  • Anas Neumann
  • Adnene Hajji
  • Monia Rekik
  • Robert Pellerin

Abstract

This paper provides a systematic review of the Genetic Algorithm (GA)s proposed to solve planning and scheduling problems in Engineer-To-Order (ETO) contexts. Our review focuses on how the key characteristics of ETO projects affect both the problem studied and the GA algorithmic features. Typical ETO projects consist of one-of-a-kind products with complex structures and uncertain designs. A deep analysis of the papers published between 2000 and 2022 enables identifying 10 main characteristics of ETO projects, six activity types, 10 decision types, eight groups of constraints, and 10 optimisation objectives. Our study shows that none of the reported papers integrates all 10 ETO characteristics. The less studied ETO characteristics are incorporating design and engineering information in the problem definition and the design uncertainty. Our review also identifies 10 recurrent encoding formats and emphasises the most frequently used genetic operators. We observed that most planning and scheduling problems consider objectives and decisions related to product customisation or supply chain configuration yielding multi-objective problems. Most multi-objective GAs use a weighted sum or are based on NSGAII. Diversity maintenance methods, adaptive and parameter tunning mechanisms, or hybridisation with machine learning models are still not used in this context.A systematic review of genetic algorithms dedicated to industrial planning and schedulingAnalysis on how the characteristics of ETO projects impact the design of genetic representation and operatorsRecommendation on approaches employed to reach high-quality solutions

Suggested Citation

  • Anas Neumann & Adnene Hajji & Monia Rekik & Robert Pellerin, 2024. "Genetic algorithms for planning and scheduling engineer-to-order production: a systematic review," International Journal of Production Research, Taylor & Francis Journals, vol. 62(8), pages 2888-2917, April.
    • Handle: RePEc:taf:tprsxx:v:62:y:2024:i:8:p:2888-2917
    DOI: 10.1080/00207543.2023.2237122
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    Cited by:

    1. Chia-Ming Lin & Shang-Liang Chen, 2024. "Improving Ti Thin Film Resistance Deviations in Physical Vapor Deposition Sputtering for Dynamic Random-Access Memory Using Dynamic Taguchi Method, Artificial Neural Network and Genetic Algorithm," Mathematics, MDPI, vol. 12(17), pages 1-25, August.

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