A critical review of selective maintenance for mission-oriented systems: challenges and a roadmap for novel contributions

Abstract

AbstractThe selective maintenance problem (SMP) arises in many mission-oriented multi-component systems that are operated for consecutive missions interspersed with finite breaks, during which only limited component repairs can be performed due to constrained resources. This NP-hard problem decides which components to maintain and to what levels of repair to guarantee a pre-specified performance level during the subsequent mission. Over the last two decades, a sizeable body of literature has been published on this topic. However, the contributions have stagnated in quality, and most articles deal with small to moderate problems. This paper provides a critical review of the SMP literature. A total of 136 research articles related to SMP are reviewed and a selection of key representative models is discussed in detail. This review is framed according to two feature categories: formulation characteristics, composed of three sub-groups of characteristics related to the system, maintenance and mathematical model characteristics; and solution approaches, grouped by exact methods and approximate algorithms. This critical review is aimed at identifying drawbacks, shortcomings, and blind spots of the SMP literature, and providing a roadmap for the challenges to be addressed and innovative future research topics to further advance the academic and industrial contributions of SMP.Keywords: Selective maintenancemaintenance planningreliability maximisationresource assignmentimperfect maintenance AcknowledgmentsWe also thank the anonymous reviewers for their suggestions and comments.Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article. The review data is freely available at www.smpreview.com (Al-Jabouri, Saif, Diallo, Khatab, and Venkatadri Citation2023), enabling custom sorting based on characteristics of interest.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Canadian Natural Science and Engineering Research Council (NSERC) grants awarded to the second, fourth and fifth authors through the Discovery Grant Programme.Notes on contributorsHamzea Al-JabouriHamzea Al-Jabouri Ph.D., is a Simulation Specialist at MAGNA International, located in Brampton, Ontario. He earned his Ph.D. in Industrial Engineering from Dalhousie University, Halifax, Nova Scotia, and attained a Master of Applied Science in Industrial Engineering from the University of Regina, Saskatchewan. Dr. Al-Jabouri is a member of the Association of Professional Engineers and Geoscientists of Saskatchewan (APEGS). Presently, his research endeavours focus on simulation-based optimisation, as well as large-scale and robust optimisation strategies for intelligent maintenance operations.Ahmed SaifAhmed Saif , P.Eng., Ph.D., is an Associate Professor in the Department of Industrial Engineering at Dalhousie University. He received his Ph.D. in Management Sciences from the University of Waterloo, M.Sc. in Engineering Systems and Management from Masdar Institute of Science and Technology, MBA from New York Institute of Technology and B.Sc. in Production Engineering from Alexandria University. His research interests include large-scale optimisation, decision-making under uncertainty and data analytics techniques and their application in hybrid renewable energy systems, sustainable supply chains, humanitarian logistics and selective maintenance.Abdelhakim KhatabAbdelhakim Khatab Ph.D., is Professor at Université de Lorraine (France). After graduating from the High Normal School (ENS-Mohammedia, Morocco), He received a DEA (Msc degree) and a PhD in Industrial automation engineering from National Institute of Applied Science (INSA) of Lyon (France). Since 2018, Dr. Khatab is adjunct Professor at the Department of Industrial Engineering-Dalhousie University (Canada). He is member of IFAC TC 5.2 where he is chair of a working group on Smart, Reliable and Sustainable Manufacturing-Distribution Systems. He also serves as a scientific expert member of the Natural Sciences and Engineering Research Council of Canada (NSERC). Dr. Khatab research interests include reliability theory, optimisation and decision support for system's production and intelligent maintenance management, design and optimisation of reverse supply chains, sustainability, and remanufacturing.Claver DialloClaver Diallo Ph.D., P.Eng., is Professor in the Department of Industrial Engineering at Dalhousie University in Halifax, Nova Scotia. He has taught at Dalhousie University since October 2007. He holds a Ph.D. and a Master of Applied Science degree in Industrial Engineering, and a bachelor's degree in Mechanical Engineering from Laval University, Quebec, Canada. He is a member of the Institute of Industrial and Systems Engineering (IISE), the Canadian Operational Research Society (CORS) and Engineers Nova Scotia (ENS). He is a member of IFAC TC 5.2. His current research is focused on reliability engineering & predictive maintenance, production and distribution systems design within the Industry 4.0/5.0 context which includes hyperconnected logistics networks, smart production planning and control, and sustainable supply chain management.Uday VenkatadriUday Venkatadri, Ph.D., P.Eng., is Professor in the Department of Industrial Engineering at Dalhousie University in Halifax, Nova Scotia. He has taught at Dalhousie University since July 2001 and served as Department Head. Before joining Dalhousie, he was a Lead Architect for supply chain planning products at Baan (now Infor Global Solutions Inc.). He also worked as a Research Associate at Université Laval in Québec City. He holds a Ph.D. in Industrial Engineering from Purdue University, a Master of Science degree in Industrial Engineering from Clemson University, and a bachelor's degree in mechanical engineering from IIT (BHU) Varanasi, India. His current research is focused on production and distribution systems design and operations within the Industry 5.0 and hyper-connected logistics context. He is interested in the Physical Internet, production planning, and forward and reverse supply chain management using contemporary AI, ML, and Decision Analytic tools.