A GIS-Integrated Spatial Optimization Framework for WEEE Reverse Logistics in High-Density Urban Morphology

The rapid accumulation of Waste Electrical and Electronic Equipment (WEEE) presents severe environmental and resource challenges in high-density metropolises. Traditional reverse logistics (RL) network designs often overlook urban morphological constraints and treat recovery rates as static parameters. To address these gaps, this study proposes a GIS-integrated low-carbon WEEE RL framework. A Spatial Multi-Criteria Decision Analysis (MCDA) workflow first deduces optimal facility layouts avoiding ecological exclusion zones. Subsequently, a Fuzzy Mixed-Integer Linear Programming (FMILP) model endogenizes the dynamic recovery rate and enforces discrete vehicle dispatching, solved via an advanced Geospatially Constrained Multiple-Priority Genetic Algorithm (MPGA). Validated in Jinan, China, the framework consistently outperforms contemporary benchmarks. Crucially, it reveals that traditional continuous models underestimate urban carbon footprints by 34.6%. By adopting the optimal spatial compromise, policymakers can achieve a 19.9% carbon reduction at a marginal 12.7% profit sacrifice, effectively harmonizing decarbonization with commercial viability.