FUZZY MULTI-OBJECTIVE OPTIMIZATION FOR THE PLACEMENT OF REVERSE VENDING MACHINE IN URBAN WASTE MANAGEMENT SYSTEM

This study proposes a fuzzy multi-objective optimization model for strategically placing Reverse Vending Machines (RVMs) within urban waste management systems. The research follows a structured methodology comprising seven key stages. First, a conceptual model was designed to address the challenges of post-consumer waste collection. Second, a mathematical model was formulated to optimize two conflicting objectives: maximizing recyclable waste collection and minimizing transportation distances. Third, the model was reformulated using fuzzy parameters—specifically, triangular membership functions—to account for uncertainties in waste generation rates, disposal demand, and transportation costs. Fourth, data were collected from a Lampang Province, Thailand case study covering 15 communities and 17 candidate RVM locations. Fifth, the fuzzy model was solved using the Weighted Sum Method and implemented via exact optimization in LINGO software. Sixth, results were analyzed, showing that five RVMs can be optimally installed under a 5,000,000 THB budget, achieving 23,911.50 kilograms of waste collection with a minimized transportation distance of 179.90 kilometers. Sensitivity analyses on distance, budget, and objective weights revealed key trade-offs between operational efficiency and environmental performance. Finally, the study concludes with implications for policy and planning, emphasizing the potential of fuzzy optimization in enhancing real-world recycling infrastructure. The proposed framework supports data-driven, sustainable decision-making for urban waste systems. Future research may explore dynamic waste generation patterns, behavioral modeling, and the use of metaheuristic algorithms for large-scale implementation.