نوع مقاله : پژوهشی
نویسندگان
1 دانشکده مهندسی صنایع، دانشکدگان فنی،،دانشگاه تهران
2 دانشکده مهندسی صنایع، دانشکدگان فنی-دانشگاه تهران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
This study presents a multi-objective location-routing model for the management of electronic waste (e-waste) collection, which dynamically utilizes real-time data from the Internet of Things (IoT). Unlike previous studies that examined location, allocation, or routing decisions separately and within static environments, this research develops an integrated multi-level framework. In this framework, decisions regarding the optimal location of collection centers, the allocation of e-waste from generation points, and the routing of a heterogeneous fleet of vehicles are made simultaneously, based on real-time fill-level alerts received from IoT-equipped smart bins. For the first time in the literature, a dynamic service mechanism triggered by fill-level alarms is formulated as a bi-objective optimization model. The two objectives are minimizing total system costs—including fixed costs of collection centers, vehicle rental, and transportation—and minimizing greenhouse gas emissions generated during collection and transportation operations. This approach enables a shift from traditional fixed-schedule collection to a demand-responsive system, significantly improving operational efficiency. To validate the mathematical model, small-scale problem instances were solved using GAMS software and compared against exact solutions. For larger and more complex instances, an improved version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed and its performance was benchmarked against the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The results demonstrate that the enhanced NSGA-II outperforms MOPSO in terms of solution accuracy, proximity to the true Pareto front, and computational time. Furthermore, sensitivity analysis reveals that the capacities of collection centers and vehicles are the most influential parameters affecting both total cost and environmental emissions. A real-world case study conducted across 22 districts of Tehran confirms the practical applicability of the proposed model. The implementation of IoT-based fill-level alerts reduces unnecessary inspection trips by 32%, leading to 15.7% savings in fuel consumption and a 12.4% reduction in greenhouse gas emissions compared to conventional collection systems. These findings highlight the pivotal role of IoT in enabling dynamic and sustainable e-waste management.
کلیدواژهها [English]
)