Revisão sistemática da literatura de modelos de otimização para o planejamento de redes de logística reversa de resíduos eletroeletrônicos

Abstract

Global waste generation is rapidly increasing; in response, industries and policymakers are prioritizing circular economy initiatives and waste recycling to reduce environmental impact and operational costs. Waste Electrical and Electronic Equipment (WEEE) is among the fastest-growing waste streams, and while it contains hazardous materials, it also offers opportunities for resource recovery. However, managing WEEE is complex due to the high capillarity of collection points and the challenges of transport logistics. The literature on optimization models for reverse network planning problems and decision support systems can help solve these problems, improving efficiency and environmental performance in Reverse Logistics and Closed Loop structures. This dissertation examines optimization models for Reverse Logistics network planning, analyzing how social, environmental, and economic factors are considered in decision-making, such as facility location, transportation, inventory management, and network design. The findings reveal that in WEEE Reverse Logistics, only 38% apply the closed-loop system, reflecting its logistical and technological complexities. Cost minimization remains the dominant objective in 19% of the models, but 60% integrate environmental considerations and 19% adopt a Triple Bottom Line approach, incorporating economic, environmental, and social dimensions. Despite transportation being a critical factor in reverse logistics planning, 22% of the models address the selection of transportation modes. These findings highlight the need for optimization approaches that integrate multi-objective decision-making and sustainability-oriented frameworks. By addressing these gaps, this research contributes to strengthening WEEE Reverse Logistics, providing support for the construction of more efficient, economically viable, and environmentally sustainable systems.