Developing a resilient and sustainable non-linear closed-loop supply chain management framework for the automotive sector industry using a gaussian fuzzy optimization based non-linear model predictive control approach

Abstract

ABSTRACTEfforts to merge sustainability and resilience within the automotive industry’s supply chain models have proven challenging. This paper proposes a novel non-linear closed-loop supply chain management framework tailored to the tire industry supply chain from the automotive sector to address the issue of exploring interrelationships. Framework employs trapezoidal linguistic cubic fuzzy Z-score technique for order of preference by similarity to the ideal solution ranking approach to prioritize resilience strategies to maintain sustainability performance during sudden disturbances. Furthermore, Gaussian fuzzy optimization-based non-linear model predictive control acts as a feedback controller to integrate sustainability and resilience by providing a stable output based on the objective function related to sustainability dimensions. An experimental study assesses the impact of resilience strategies on total supply chain costs, highlighting significant cost savings. Adopting strategies like multiple sourcing, information sharing, and improved design quality of the supply chain keeps total expected costs optimal for various sustainability levels.KEYWORDS: Resilience strategysustainable supply chainclosed-loop supply chainnon-linear model predictive controlfuzzy optimal control Abbreviations=DescriptionNLCLSCM=Non-linear closed loop supply chain managementSC=Supply ChainSCM=Supply Chain ManagementTOPSIS=Technique for Order of Preference by Similarity to Ideal SolutionTLCF-ZTOPSIS=Trapezoidal Linguistic Cubic Fuzzy Z-score Technique for Order of Preference by Similarity to Ideal SolutionGFO-NMPC=Gaussian Fuzzy Optimization-based Non-Linear Model Predictive ControlCO2=Carbon dioxideRS=Resilient strategyMILP=Mixed Integer Linear ProgrammingDEMATEL=Decision-Making Trial and Evaluation LaboratoryDMU=Decision Making UnitClosed-loop SC=Closed-loop Supply ChainDisclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSachin B. KhotSachin B. Khot is a Ph.D student at Vellore Institute of Technology, Vellore, India. He is Masters in Industrial Engineering from National Institute of Technology, Tiruchirappalli. He is currently working as Assistant professor at Rajarambapu Institute of Technology, Rajaramnagar, India. He has 2 years of industrial experience and around 10 years of academic experience. He is teaching Industrial Engineering, Supply Chain Management, Total Quality Management and Additive Manufacturing to the UG students. He has also taught Supply Chain Management to PG Students. He has guided 10 UG Projects and 3 PG Projects. He has research interests in Supply Chain Management, productivity improvement, decision making under uncertainty, risk management in supply chain and engineering education etc.S. ThiagarajanS. Thiagarajan is a Professor in the Department of Manufacturing Engineering, School of Mechanical Engineering, VIT University, Vellore, Tamilnadu, India. He has around 25 years of administrative and academic experience. Currently he is teaching Logistics and Supply Chain Management to both UG and PG graduates, Optimization Techniques to PG graduates. He has published several papers in reputed journals such as International Journal of Production Research, European Journal of Operations Research, Computers and Industrial Engineering to name a few. His research interests include scheduling in manufacturing systems, risk analysis in supply chain management, stochastic programming, decision making under uncertainty, etc.