Fuzzy Optimization for Closed-Loop Reverse Logistics: Towards Efficient Resource Management

Achieving the objectives of well- managed reverse logistics in closed cycle is complicated by the multiplicity of problems and is an uncertain process. Fuzzy optimization encompass the possibility in dealing with challenging situations where there is no exact and definite data. This approach has an emerging role in resolving these problems. To this end, the paper offers fuzzy ways of modeling reverse logistics systems and then multi-objective fitness functions used for proper decision-making in order to achieve higher resource allocation. The framework combines membership functions with mathematical models and hybrid programming methods to handle enigmas in demand, marketing, transportation, and recycling operations. Via a set of examples, illustrations how the suggested points work in order to improve resource utilization, to reduce costs, and to lessen environmental impact. The outcome emphasizes the power of fuzzy optimization increase in performance of closed loop reverse logistics with regards to efficiency and sustainability.