A Reverse-Logistics Goal-Programming Framework for Post-Conflict Rubble Management in Aleppo with MCDM-Based Evaluation

Rubble management in Aleppo is a complex recovery planning problem because debris removal is closely linked to transport access, recycling capacity, reconstruction demand, disposal limits, and local acceptance. This study develops a multi-objective reverse-logistics model for managing post-conflict rubble in the city. The proposed network consists of 21 rubble source nodes, 5 collection and sorting sites, 4 recycling facilities, 5 reconstruction-demand nodes, and 2 disposal sites. The model is formulated as a weighted non-preemptive goal programming problem that considers four objectives: total cost, carbon emissions, dust/social acceptance penalty, and operational implementation risk. Because the last two objectives are strongly context-dependent, they were developed through a Delphi process with 13 experts, and the related subcriteria were weighted using fuzzy FUCOM. Four compromise formulations were tested: weighted goal programming (WGP), augmented max–min fuzzy goal programming (AMM-FGP), weighted additive fuzzy goal programming (WA-FGP), and Max–Min optimization. The resulting solutions were ranked using MARCOS and validated using CRADIS. WGP ranked first, with a MARCOS utility score of 0.697919, followed by Max–Min at 0.654658, AMM-FGP at 0.633123, and WA-FGP at 0.629647. The policy analysis also showed that a 30% recycling subsidy produced the largest improvement, reducing the WGP achievement value from 0.192 to 0.046. These results suggest that, for Aleppo, a rubble-management plan cannot be judged only by cost or emissions. Social nuisance, site operability, and policy support also affect whether a technically efficient network can be implemented in practice.