A Strategic and Operational Framework for Pre-Disaster Management Considering Sustainability, Resilience, and Smart City Using Multi-Criteria Decision-Making and Mathematical Optimization
Subject Areas : Supply chain management and logistics
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)
Keywords: Relief Logistics Network, Smart City, Pre-Disaster, Location-Allocation, Internet of Things, SWARA, WASPAS,
Abstract :
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.
AbdelAziz, N. M., Eldrandaly, K. A., Al-Saeed, S., Gamal, A., & Abdel-Basset, M. (2024). Application of GIS and IoT Technology based MCDM for Disaster Risk Management: Methods and Case Study. Decision Making: Applications in Management and Engineering, 7(1), 1-36.
Aksoy, E., & Selim, S. (2020). An automated approach for determination and prioritization of urban potential risk areas within the scope of superstructure. Natural Hazards, 103(1), 1077-1091.
AKTAS, A. (2022). Prioritization of Districts in terms of Disaster Preparedness Planning: A Case Study for the Expected Istanbul Earthquake. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 11(3), 880-892.
Alizadeh, M., Hashim, M., Alizadeh, E., Shahabi, H., Karami, M. R., Beiranvand Pour, A., ... & Zabihi, H. (2018). Multi-criteria decision making (MCDM) model for seismic vulnerability assessment (SVA) of urban residential buildings. ISPRS International Journal of Geo-Information, 7(11), 444.
Aljohani, F. H., Abi Sen, A. A., Ramazan, M. S., Alzahrani, B., & Bahbouh, N. M. (2023). A Smart Framework for Managing Natural Disasters Based on the IoT and ML. Applied Sciences, 13(6), 3888.
Alsamhi, S. H., Ma, O., Ansari, M. S., & Almalki, F. A. (2019). Survey on collaborative smart drones and internet of things for improving smartness of smart cities. Ieee Access, 7, 128125-128152.
Anvari, M., Anvari, A., & Boyer, O. (2023). A prepositioning model for prioritized demand points considering lateral transshipment. Journal of Humanitarian Logistics and Supply Chain Management, (ahead-of-print).
Arif, A., Wang, Z., Chen, C., & Chen, B. (2019). A stochastic multi-commodity logistic model for disaster preparation in distribution systems. IEEE Transactions on Smart Grid, 11(1), 565-576.
Baser, B., & Behnam, B. (2020). An emergency response plan for cascading post-earthquake fires in fuel storage facilities. Journal of Loss Prevention in the Process Industries, 65, 104155.
Bashiri, M., Nikzad, E., Eberhard, A., Hearne, J., & Oliveira, F. (2021). A two stage stochastic programming for asset protection routing and a solution algorithm based on the Progressive Hedging algorithm. Omega, 104, 102480.
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., ... & Portugali, Y. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214, 481-518.
Bitarafan, M., Amini Hosseini, K., & Hashemkhani Zolfani, S. (2023). Evaluating Natural Hazards in Cities Using a Novel Integrated MCDM Approach (Case Study: Tehran City). Mathematics, 11(8), 1936.
Bullock, J., Haddow, G., & Coppola, D. P. (2017). Introduction to emergency management. Butterworth-Heinemann.
Chang, K. H., Chiang, Y. C., & Chang, T. Y. (2024). Simultaneous location and vehicle fleet sizing of relief goods distribution centers and vehicle routing for post-disaster logistics. Computers & Operations Research, 161, 106404.
Cheng, C., Adulyasak, Y., & Rousseau, L. M. (2021). Robust facility location under demand uncertainty and facility disruptions. Omega, 103, 102429.
Colajanni, G., Daniele, P., Nagurney, A., Nagurney, L. S., & Sciacca, D. (2023). A three-stage stochastic optimization model integrating 5G technology and UAVs for disaster management. Journal of global optimization, 1-40.
Cotes, N., & Cantillo, V. (2019). Including deprivation costs in facility location models for humanitarian relief logistics. Socio-Economic Planning Sciences, 65, 89-100.
DESA, U. (2019). World urbanization prospects: The 2018 revision (st/esa/ser. a/420). United Nations: New York, NY, USA.
Du, B., Zhou, H., & Leus, R. (2020). A two-stage robust model for a reliable p-center facility location problem. Applied Mathematical Modelling, 77, 99-114.
Eshghi, A., Tavakkoli-Moghaddam, R., Ebrahimnejad, S., & Ghezavati, V. (2020). Multi-objective robust mathematical modeling for emergency relief in disaster under uncertainty. Scientia Iranica.
Fang, Y. P., Fang, C., Zio, E., & Xie, M. (2019). Resilient critical infrastructure planning under disruptions considering recovery scheduling. IEEE Transactions on Engineering Management, 68(2), 452-466.
Gharib, Z., Bozorgi-Amiri, A., Tavakkoli-Moghaddam, R., & Najafi, E. (2018). A cluster-based emergency vehicle routing problem in disaster with reliability. Scientia Iranica, 25(4), 2312-2330.
Ghasemi, P., Khalili-Damghani, K., Hafezalkotob, A., & Raissi, S. (2019). Uncertain multi-objective multi-commodity multi-period multi-vehicle location-allocation model for earthquake evacuation planning. Applied Mathematics and Computation, 350, 105-132.
Goodarzian, F., Abraham, A., & Fathollahi-Fard, A. M. (2021). A bi objective home health care logistics considering the working time and route balancing: a self-adaptive social engineering optimizer. Journal of Computational Design and Engineering, 8(1), 452-474.
GÜLER, E., AVCI, S., & ALADAĞ, Z. (2023). Earthquake risk prioritization via two-step cluster analysis and SWARA-ELECTRE methods. Sigma, 41(2), 356-372.
Hajipour, V., Niaki, S. T. A., Akhgar, M., & Ansari, M. (2021). The healthcare supply chain network design with traceability: A novel algorithm. Computers & Industrial Engineering, 161, 107661.
Javadi, B., & Yadegari, M. (2024). An integrated relief network design model for logistics planning under uncertainty. Sharif Journal of Industrial Engineering & Management.
Jena, R., Pradhan, B., & Beydoun, G. (2020). Earthquake vulnerability assessment in Northern Sumatra province by using a multi-criteria decision-making model. International journal of disaster risk reduction, 46, 101518.
Khalili-Damghani, K., Tavana, M., & Ghasemi, P. (2022). A stochastic bi-objective simulation–optimization model for cascade disaster location-allocation-distribution problems. Annals of Operations Research, 309(1), 103-141.
Khatibi, H., Wilkinson, S., Eriwata, G., Sweya, L. N., Baghersad, M., Dianat, H., ... & Javanmardi, A. (2022). An integrated framework for assessment of smart city resilience. Environment and Planning B: Urban Analytics and City Science, 49(5), 1556-1577.
Liu, Q., Xu, P., Wu, Y., & Shen, T. (2023). A two‐stage algorithm for vehicle routing problem with charging relief in post‐disaster. IET Intelligent Transport Systems.
Mahtab, Z., Azeem, A., Ali, S. M., Paul, S. K., & Fathollahi-Fard, A. M. (2022). Multi-objective robust-stochastic optimisation of relief goods distribution under uncertainty: a real-life case study. International journal of systems science: operations & logistics, 9(2), 241-262.
Malakar, S., & Rai, A. K. (2022). Earthquake vulnerability in the Himalaya by integrated multi-criteria decision models. Natural Hazards, 1-25.
McLennan, M. The Global Risks Report 2023 18th Edition.
Ming, J., Richard, J. P. P., Qin, R., & Zhu, J. (2022). Distributionally robust optimization for fire station location under uncertainties. Scientific reports, 12(1), 5394.
Mohammadi, S., Darestani, S. A., Vahdani, B., & Alinezhad, A. (2020). A robust neutrosophic fuzzy-based approach to integrate reliable facility location and routing decisions for disaster relief under fairness and aftershocks concerns. Computers & Industrial Engineering, 148, 106734.
Naderi, F., Fotoohi, S., Negaresh, H., & Khalili, M. (2021). Prioritization of Earthquake Risk in Areas Affected by Sarpol-e-Zahab Earthquake Using ELECTERE Model. Journal of Geography, 19(70).
Nodoust, S., Pishvaee, M. S., & Seyedhosseini, S. M. (2023). A Robust Possibilistic Programming Model for Disaster Relief Routing under Information and Communication Technology. International Journal of Supply and Operations Management, 10(1), 38-56.
Ozkaya, G., & Erdin, C. (2020). Evaluation of smart and sustainable cities through a hybrid MCDM approach based on ANP and TOPSIS technique. Heliyon, 6 (10), e05052.
Peng, Y. (2015). Regional earthquake vulnerability assessment using a combination of MCDM methods. Annals of Operations Research, 234, 95-110.
Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer networks, 101, 63-80.
Sahraei, Z., & Samouei, P. (2023). Locating local warehouses and controlling inventory and allocating normal and perishable items in post-crisis with uncertain demand and budget. Emergency Management, 11(3).
Sarma, D., Das, A., & Bera, U. K. (2020). Uncertain demand estimation with optimization of time and cost using Facebook disaster map in emergency relief operation. Applied Soft Computing, 87, 105992.
Seraji, H., Tavakkoli-Moghaddam, R., Asian, S., & Kaur, H. (2022). An integrative location-allocation model for humanitarian logistics with distributive injustice and dissatisfaction under uncertainty. Annals of Operations Research, 319(1), 211-257.
Shakibaei, H., Farhadi-Ramin, M. R., Alipour-Vaezi, M., Aghsami, A., & Rabbani, M. (2023). Designing a post-disaster humanitarian supply chain using machine learning and multi-criteria decision-making techniques. Kybernetes.
Shao, Q. G., Jiang, C. C., Lo, H. W., & Liou, J. J. (2023). Establishing a sustainable development assessment framework for a smart city using a hybrid Z-fuzzy-based decision-making approach. Clean Technologies and Environmental Policy, 1-18.
Sheikholeslami, M., & Zarrinpoor, N. (2023). Designing an integrated humanitarian logistics network for the preparedness and response phases under uncertainty. Socio-Economic Planning Sciences, 86, 101496.
Stienen, V. F., Wagenaar, J. C., den Hertog, D., & Fleuren, H. A. (2021). Optimal depot locations for humanitarian logistics service providers using robust optimization. Omega, 104, 102494.
Sun, H., Li, J., Wang, T., & Xue, Y. (2022). A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions. Transportation research part E: logistics and transportation review, 157, 102578.
Tadaros, M., & Migdalas, A. (2022). Bi-and multi-objective location routing problems: classification and literature review. Operational Research, 22(5), 4641-4683.
Taki, E., Mirghafoori, S. H. A., Morovati Sharifabadi, A., & Mirfakhreddini, S. H. (2022). Development of an Innovative Algorithm Based on Complete Counting for Bilateral Optimization Problem Design of Hub Logistics Network in Relief and Disaster Supply Chain. Emergency Management, 11(1), 81-96.
Taş, M. A., & Alptekin, S. E. (2023). Evaluation of major cities in terms of smart cities: A developing country perspective. Procedia Computer Science, 225, 1717-1726.
United Nations Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Summary of Results. UN DESA/POP/2022/TR/NO. 3.
Yáñez-Sandivari, L., Cortés, C. E., & Rey, P. A. (2021). Humanitarian logistics and emergencies management: New perspectives to a sociotechnical problem and its optimization approach management. International Journal of Disaster Risk Reduction, 52, 101952.
Ye, Y., Jiao, W., & Yan, H. (2020). Managing relief inventories responding to natural disasters: Gaps between practice and literature. Production and Operations Management, 29(4), 807-832.
Zabihi, O., Siamaki, M., Gheibi, M., Akrami, M., & Hajiaghaei-Keshteli, M. (2023). A smart sustainable system for flood damage management with the application of artificial intelligence and multi-criteria decision-making computations. International Journal of Disaster Risk Reduction, 84, 103470.
Zare‐Bahramabadi, M., Farzin, H., & Ehsan, M. (2023). A mixed integer linear programming model for risk‐based remote‐controlled switches, distributed generation, and tie line placement in distribution systems with complex topologies to improve the resilience. IET Renewable Power Generation.
Zarrinpoor, N., Aray, Z., & Sheikholeslami, M. (2023). A Robust-Stochastic Optimization Approach for Designing Relief Logistics Operations under Network Disruption. International Journal of Supply and Operations Management, 10(3), 271-294.
Zhang, G., Wang, X., Meng, Z., Zhang, Q., & Wu, K. (2023). Operation decision of logistics service supply chain: Considering interruption probability in emergencies. Kybernetes, 52(1), 284-303.
Zhang, X., & Chen, D. (2023). Prepositioning network design for humanitarian relief purposes under correlated demand uncertainty. Computers & Industrial Engineering, 109365.