A Strategic and Operational Framework for Pre-Disaster Management Considering Sustainability, Resilience, and Smart City Using Multi-Criteria Decision-Making and Mathematical Optimization
الموضوعات :
Farshad Kaveh
1
(Industrial Engineering Department, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Mahdi Karbasian
2
(Industrial Engineering Department, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Omid Boyer Hassani
3
(Assistant Professor,Faculty of EngineeringNajafabad branchIslamic Azad University)
Hadi Shirouyehzad
4
(Department of Industrial Engineering, Faculty of Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran)
الکلمات المفتاحية: Relief Logistics Network, Smart City, Pre-Disaster, Location-Allocation, Internet of Things, SWARA, WASPAS,
ملخص المقالة :
With surging population and rising natural disaster threats, disaster management and its continuous improvement have become global concerns. This study presents a strategic framework for enhancing disaster management in preparedness and risk reduction phases, employing MCDM and mathematical optimization methods while incorporating sustainability, resilience, and smart city approaches. Aspects and criteria for disaster management are identified through literature review and expert consultation. SWARA and WASPAS methods determine the importance of Aspects and criteria, region prioritization, and earthquake vulnerability assessment. A non-linear three-objective integer programming mathematical model is formulated to minimize operational costs, maximize camera and sensor coverage, and enhance reliability. This model encompasses supplier selection, warehouse location, inventory control, and IoT equipment allocation to establish a smart city infrastructure in selected regions. The research findings highlight the importance of infrastructure, social, and physical Aspects, along with criteria such as the number of healthcare centers, transportation networks, fire stations, population density, and ICT infrastructure, for prioritizing disaster management efforts. Emergency supplies, warehouses, and suppliers were identified to ensure preparedness during crises. Inventory control policies for order quantity and safety stock determination were employed to reduce costs and enhance crisis response readiness. Furthermore, several normal regions were selected for smart city infrastructure development, and the allocation of various cameras and sensors was optimized considering coverage radius, reliability, and demand variability reduction compared to normal regions. A case study of Isfahan's 15 districts demonstrated the framework's problem-solving capability. Sensitivity analysis revealed that the objective function is influenced by maintenance costs, demand correlation coefficient, and average demand. This research can serve as a foundation for future studies in disaster and crisis management optimization and has the potential for application in disaster management organizations and other regions.
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