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<p>This study develops a robust mixed-integer linear programming model for the location and transportation planning of municipal solid waste facilities, including sorting, recycling, and landfill centers. The model minimizes total costs and transportation risks while keeping greenhouse gas emissions within acceptable limits and explicitly accounts for uncertainty in costs and capacity requirements using a robust optimization approach. Also, the problem incorporates a rectangular distance metric for calculating costs and risks associated with material transport via urban roads, while a Euclidean distance metric is employed for estimating GHG emissions, as these gases disperse through the air. To account for the importance, sensitivity, and population density of residential areas, the model assigns specific weight factors to each location. A case study for Tehran province demonstrates that the robust model consistently outperforms the deterministic model under realistic conditions. The realization tests show that mean total costs and risks are reduced by up to 99%, compared to deterministic solutions. Sensitivity analysis further confirms that increasing the uncertainty budget can decrease total costs and risks by approximately 20%, while larger deviations from nominal values increase them by about 26%. These results confirm the model’s practical value for reliable MSW facility planning under uncertainty.</p>

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A Robust Optimization Model for Semi-desirable Facilities Location with the Consideration of Greenhouse Gas Emissions

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • Samaneh Moradi Pirbalouti,
  • Ali Bozorgi-Amiri,
  • Mohamad Sadegh Sangari,
  • Adel Pourghader Chobar

摘要

This study develops a robust mixed-integer linear programming model for the location and transportation planning of municipal solid waste facilities, including sorting, recycling, and landfill centers. The model minimizes total costs and transportation risks while keeping greenhouse gas emissions within acceptable limits and explicitly accounts for uncertainty in costs and capacity requirements using a robust optimization approach. Also, the problem incorporates a rectangular distance metric for calculating costs and risks associated with material transport via urban roads, while a Euclidean distance metric is employed for estimating GHG emissions, as these gases disperse through the air. To account for the importance, sensitivity, and population density of residential areas, the model assigns specific weight factors to each location. A case study for Tehran province demonstrates that the robust model consistently outperforms the deterministic model under realistic conditions. The realization tests show that mean total costs and risks are reduced by up to 99%, compared to deterministic solutions. Sensitivity analysis further confirms that increasing the uncertainty budget can decrease total costs and risks by approximately 20%, while larger deviations from nominal values increase them by about 26%. These results confirm the model’s practical value for reliable MSW facility planning under uncertainty.