Multi-Objective Robust Fuzzy Analytics of Closed-Loop Supply Chain Management and Its Major Sustainable Application

Abstract

In this paper we explored the multi-Objectiverobust fuzzy analytics of closed-loop supply chain management and its major sustainable application. To build a CLSCN (Closed-Loop Supply Chain Network), a mixed integer programming (MIP) model is used, in which all three facets of sustainability (economic, environmental, and social) are configured simultaneously. The economic component is assessed in our paper by measuring the expense of the SCN, while the environmental dimension is assessed by the use of carbon caps. Finally, the societal component is modelled by reducing the number of days off owing to work-related injuries. The proposed model's instability parameters are split into two classes. Since evidence on missing working days is difficult to come by, ambiguous affiliation theory is used to calculate it. Customer demand and remanufacturing pace are two other forms of unknown parameters in our model, all of which would obviously affect the supply chain's tactical strategy. In this case, rigorous optimization is used to maintain network reliability to some degree, and finally, this paper presents a systematic study of the numerical findings to clarify the effects of complexity on Pareto optimal solutions and supply chain network structure variation. A numerical illustration is used to evaluate the proposed stable fuzzy model as well as the approach approaches. The example's findings show that the proven model and solution methods can be used successfully in practice to achieve fiscal, environmental, and social benefits from CLSC architecture. Extending the latest stochastic demand or lead time model may be a promising area for more research.