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This study presents an advanced optimization model for sustainable facility siting that integrates capacity limitations and emissions constraints within a continuous covering location framework. The objective is to ensure equitable access to services while minimizing total system costs, which include facility installation, penalties for uncovered demand, and carbon taxes from operational and transportation-related emissions. The model addresses the simultaneous consideration of economic, environmental, and social sustainability dimensions, an area still underrepresented in current covering location literature. Designed for real-world applications such as urban infrastructure planning, healthcare logistics, and energy service distribution, the model ensures that service demand is fulfilled within a prescribed radius and under a global emissions threshold. The solution methodology employs two metaheuristic algorithms implemented in MATLAB: a standard genetic algorithm (GA) and a hybrid GA–simulated annealing (GA-SA) technique. Benchmark instances involving 25, 100, and 256 customer zones are used for evaluation. Results show that both methods are effective and scalable across problem sizes, particularly in the 256-zone case, with GA-SA consistently achieving lower system costs, while GA delivers superior runtime efficiency. This research contributes a scalable, sustainability-aligned, and computationally efficient framework for facility location planning, supporting key Sustainable Development Goals such as SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).

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Sustainable Facility Location with Capacity and Emissions Constraints: A Continuous Covering Model for Equitable Infrastructure Access

  • 章节

  • 发表日期：

    2026年

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  • Raziyeh Niknam,
  • Farshid Torabi,
  • Paitoon Tontiwachwuthikul

摘要

This study presents an advanced optimization model for sustainable facility siting that integrates capacity limitations and emissions constraints within a continuous covering location framework. The objective is to ensure equitable access to services while minimizing total system costs, which include facility installation, penalties for uncovered demand, and carbon taxes from operational and transportation-related emissions. The model addresses the simultaneous consideration of economic, environmental, and social sustainability dimensions, an area still underrepresented in current covering location literature. Designed for real-world applications such as urban infrastructure planning, healthcare logistics, and energy service distribution, the model ensures that service demand is fulfilled within a prescribed radius and under a global emissions threshold. The solution methodology employs two metaheuristic algorithms implemented in MATLAB: a standard genetic algorithm (GA) and a hybrid GA–simulated annealing (GA-SA) technique. Benchmark instances involving 25, 100, and 256 customer zones are used for evaluation. Results show that both methods are effective and scalable across problem sizes, particularly in the 256-zone case, with GA-SA consistently achieving lower system costs, while GA delivers superior runtime efficiency. This research contributes a scalable, sustainability-aligned, and computationally efficient framework for facility location planning, supporting key Sustainable Development Goals such as SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).