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

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.

Highlights

• A systematic review of genetic algorithms dedicated to industrial planning and scheduling

• Analysis on how the characteristics of ETO projects impact the design of genetic representation and operators

• Recommendation on approaches employed to reach high-quality solutions

Keywords:

• Engineer-To-Order
• genetic algorithm
• resource-constrained project scheduling
• product customisation
• evolutionary algorithm
• one-of-a-kind product

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The authors confirm that the raw data supporting the findings of this review are available on request from the corresponding author.

Notes

1 www.webofscience.com

2 www.engineeringvillage.com

3 https://prisma-statement.org/Extensions/ScopingReviews

Additional information

Funding

This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC - www.nserc-crsng.gc.ca) under grant number: RDCPJ 532024-18.

Notes on contributors

Anas Neumann

Anas Neumann is a heuristic optimisation, artificial intelligence, and deep learning researcher at CIRRELT (www.cirrelt.ca). He has an MSc. in software engineering from Manouba University in Tunisia and a PhD. in operations and decision systems from the Faculty of Business Administration at Université Laval in Canada (www.ulaval.ca). His main research interest includes industrial planning and scheduling, engineer-to-order, natural language processing, and hypoglycemia prediction and avoidance for patients with type-1 diabetes. Prior to his career in scientific research, M. Neumann was also the lead developer of the www.uprodit.com web platform.

Adnene Hajji

Adnène Hajji is full professor of operations and decision systems at Université Laval in Québec City, Canada. He is member of the Interuniversity Research Center on Enterprise Networks, Logistics and Transportation (CIRRELT). He is the director of the Center for Research on Intelligent Communities (CeRCI). He received the Engineering degree in Mechanical Engineering from Ecole Nationale d'Ingénieurs de Tunis, Tunisia (1999) and his M.Eng (2003) and PhD (2007) in Automated Production Engineering both from école de Technologie Supérieure, Montréal. His main research interest includes production system modelling and control, simulation, integrated reactive models in ERP systems, performance management systems design and implementation, Industry X.0 concepts, and reconfigurable manufacturing systems.

Monia Rekik

Monia Rekik is a full professor in the Department of operations and decision systems at Université Laval, Canada. She is member of the interuniversity research center in enterprise networks, logistics and transport (CIRRELT) and the cardiometabolic health, diabetes, and obesity research network (CMDO). She holds an engineering degree in industrial engineering from the National School of Engineers of Tunis, Tunisia (ENIT), an M.A.SC in applied mathematics and a PhD in mathematical engineering from Polytechnique Montréal, Canada. She has expertise in mathematical modeling, combinatorial optimisation and operations research techniques applied to different fields in industrial and healthcare sectors. Her main works are related to the optimisation of personnel scheduling, production planning and scheduling, combinatorial auctions for transport operations, and type 1 diabetes management. She has carried out numerous projects in collaboration with public and private organisations.

Robert Pellerin

Robert Pellerin is a full professor at Polytechnique Montréal in the Department of Mathematics and Industrial Engineering. He has held the Jarislowsky/SNC-Lavalin Research Chair in Project Management since 2010, and is a member of CIRRELT, IVADO, and the Poly-Industries 4.0 laboratory. Prior to joining the Polytechnique, he led various reengineering and implementation projects of integrated management systems and manufacturing execution systems. His industrial and academic career has always focused on the development of tools dedicated to steering and monitoring operations. His research continues to focus on these issues, and he has led multiple research projects involving the development and adoption of Industry 4.0 practices in various industrial sectors.