Robust-heuristic-based optimisation for an engine oil sustainable supply chain network under uncertainty

Abstract

This study configures various carbon regulation mechanisms to control carbon emissions following clean technology strategies in engine oil production. Considering clean technology strategies for designing a sustainable supply chain (SSC) in the engine oil industry, two carbon reduction policies, namely, carbon capacity and carbon emissions tax, are discussed to study the effects of environmental factors. A mixed-integer linear programming model that examines demand, technology, budget, carbon policies, and capacity constraints under several uncertainties is proposed for engine oil production from petrochemical resources, refinery plant production, and distribution system capacities. This study controls and mitigates risk and timing decisions for output decisions from a hybrid robust-heuristic-based method, wherein a modified scenario-based GA is used to eliminate the effect of uncertainties. The results indicate high-quality convergence of solutions for different strategic scenarios. We successfully apply the introduced model to address a real-world supply chain (SC) of the engine oil industry. The proposed model improves the state-of-the-art models for the engine oil SC. Finally, the study finding shows that managers can improve technologies with the lowest possible cost, maximum product profitability, and minimum possible losses in the production process and product quality through the carbon tax policy to reduce the environmental effects.

KEYWORDS:

• Robust-heuristic-based optimisation
• genetic algorithm
• clean technology
• sustainable supply chain
• carbon policies

Disclosure statement

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

Data availability statement

Data are unavailable due to [ethical/legal/commercial] restrictions. Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is unavailable.

Additional information

Notes on contributors

Maedeh Chaleshigar Kordasiabi

Maedeh Chaleshigar Kordasiabi was born in Ghaemshahr in Mazandaran province of Iran. She earned her B.Sc. from Mazandaran University of Science and Technology, Babol, Iran (MUST) and M.Sc. from Shomal University. Her researches are principally about optimisation techniques and operation research to solve the problems and moreover industrial engineering issues containing reverse logistics, supply chain management, closed-loop supply chain, and relief logistics. She has also published several papers in the above fields in leading scientific and engineering journals e.g. ISAT, IJPE.

Hadi Gholizadeh

Hadi Gholizadeh was born in Amol in Mazandaran province of Iran. He earned his B.Sc. from Shomal University, Amol, Iran, and M.Sc. from Mazandaran University of Science and Technology, Babol, Iran (MUST). He is Ph.D. candidate in industrial engineering at Laval university in Canada the present. His researches are mainly about metaheuristics and optimisation techniques to solve the single and multi-objective optimisation problems and also industrial engineering issues including Supply Chain Network Design, Sustainable developments for these supply chain systems and transportation network design, and also related multi-level programming models in all mentioned contents and Maintenance Engineering, Queuing systems and Fuzzy Computing and MCDM. He has also published more than 20 papers in leading scientific and engineering journals e.g. TRE, CAIE, JCLEP, ENV, ESPR IJAMT, NCCA, IJFS, SOSO, ISAT, JORS, etc.

Marzieh Khakifirooz

Marzieh Khakifirooz has a Ph.D. in Industrial Engineering and Engineering Management and an M.S. degree in Industrial Statistics from the National Tsing Hua University (NTHU), Hsinchu, Taiwan. Currently, she is an assistant professor at the school of engineering, Monterrey Institute of Technology, Mexico. Dr. Khakifirooz has outstanding practical experience from her various global consultancies for high-tech industries and numerous publications in AI-based decision making. Her research interests include optimisation, data-driven, AI-based, and human-in-the-loop intelligent decision making, with application in social goods, smart society, smart manufacturing, smart health, smart energy, and smart mobility. She is an active member of System Dynamic Society, Institute of Electrical and Electronics Engineers (IEEE), Association for Computing Machinery (ACM), and Institute of Industrial and Systems Engineers (IISE).

Mahdi Fathi

Mahdi Fathi received the B.S. and M.S. degree from the Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, in 2006 and 2008, respectively, and the Ph.D. degree from the Iran University of Science and Technology, Tehran, Iran, in 2013. He was Visiting Scholar with the University of Florida, U.S.A., National Tsing Hua University, Taiwan, and Tecnológico de Monterrey, Mexico. He is currently an Assistant Professor at the University of North Texas, U.S.A. He has authored or co-authored articles in journals such as Technometrics, IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, and IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS. His research interests include Operations Research, Data Science, AI in Business, Cybersecurity and Information Systems, Energy Systems, Healthcare, and Social Goods. Dr. Fathi has received three Postdoctoral Fellowships at Ecole Centrale Paris, France, Ghent University, Belgium, and Mississippi State University, U.S.A. He is the Corresponding Editor of the textbooks Large Scale Optimisation in Supply Chains and Smart Manufacturing: Theory and Applications and Optimisation in Large Scale Problems: Industry 4.0 and Society 5.0 Applications. Dr. Fathi is a member of the Institute for Operations Research and the Management Sciences, Production and Operations Management Society, and Decision Sciences Institute and serves as an associate editor for AI in Business Journal, Energy Systems Journal, and Operations Research Forum Journal. He is also currently editing the Handbook of Smart Energy Systems.