برنامه‌ريزي و مسيريابي شبكه بازيافت زباله شهري در توليد سوخت زيستي با در نظر گرفتن تكنولوژي اينترنت اشياء

Planning an‎d routing of municipal urban waste recycling network for biofuel production

مهندسي صنايع و آينده پژوهي

In recent years, the issue of energy an‎d the pollution caused by fossil fuel production has consistently been among the major challenges facing the planet. On the other han‎d, the reuse of waste an‎d refuse has been a hot topic for humanity in order to reduce the physical an‎d environmental damage resulting from the mass generation of such waste. The application of Industry 4.0 technologies can be employed to reduce operational costs an‎d optimize processes. This research addresses the scheduling of waste collection vehicles for the pickup of both wet an‎d dry waste bins, as well as leaf an‎d grass collection centers. The objective of this scheduling is waste reuse; specifically, wet waste an‎d plant materials (leaves an‎d grass) are intended for biofuel production, while dry waste is to be recycled after undergoing separation processes. This is achieved through inventory management at collection an‎d separation centers. Initially, a nonlinear integer programming model was developed with the objectives of maximizing profit from waste sales an‎d minimizing pollution from transportation an‎d processes. After linearization, the epsilon-constraint an‎d weighted sum methods were employed to solve the model. To address large-scale problems, a heuristic method aligned with the model was defined. A second heuristic method was developed by incorporating Industry 4.0 technology to monitor waste volume inside bins an‎d transmit signals. The results indicate that the exact method was able to cover small-scale problems, while the heuristic method covered large-scale ones. For the heuristic method, the Taguchi method was used for parameter tuning. By comparing the solutions of the two exact solution methods, the epsilon-constraint method was identified as the superior method for decision-making, an‎d the weighted sum method was determined to be best for the optimal pollution state. Comparing the results of the exact an‎d heuristic methods for small-scale problems yielded a 13% error for the heuristic method relative to the exact method. The two heuristic methods were compared, revealing that the heuristic method equipped with Internet of Things technology resulted in an average 5% improvement in final profit an‎d a 7% reduction in transportation-related pollution, demonstrating superiority concerning the problemʹs objectives. Through sensitivity analysis performed on key parameters, it was observed that increasing the bin pickup threshold led to a decrease in total pollution, but conversely, total profit also decreased. Furthermore, decreasing this threshold steered conditions towards complete bin coverage, which entailed higher profit accompanied by greater total pollution. Comparing different scenarios indicates that a 50% pickup threshold establishes an appropriate balance between these two criteria, profit an‎d pollution.

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