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Designing Humanitarian Relief Supply Chains by Considering the Reliability of Route, Repair Groups and Monitoring Route | ||
Advances in Industrial Engineering | ||
دوره 53، شماره 4، دی 2019، صفحه 93-126 اصل مقاله (1.03 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/jieng.2021.306436.1733 | ||
نویسندگان | ||
Bahman Momeni1؛ Amir Aghsami2؛ Masoud Rabbani* 3 | ||
1School of Industrial & Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
2School of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran | ||
3School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
چکیده | ||
Most humanitarian relief items' investigations try to satisfy demands in disaster areas in an appropriate time and reduce the rate of causality. Time is an essential element in humanitarian relief items; the quietest response time, the more rescued people. Reducing response time with high reliability is the main objective of this research. In our investigation, monitoring the route’s situation after occurrence disaster with drones and motorcycles is planned for collecting information about routes and demand points in the first stage. The collected information is analyzed by the disaster management to determine the probability of each scenario. By evaluating collected data, the route repair groups are sent to increase the route’s reliability. In the final step, the relief items operation allocates the relief items to demand points. All in all, this research tries to present a practical model and real situation to survive more people after occurrence disaster. An exact solver solves the evolutionary model in small and medium scales; the developed model in big scale is solved by Grasshopper Optimization Algorithm (GOA), and then results are evaluated. The evaluation results indicate the positive effect of valid initial information on the humanitarian supply chain’s performance. | ||
کلیدواژهها | ||
Humanitarian Relief Supply Chain؛ Monitoring Routes؛ Repairing Groups؛ Reliability of Routes؛ Grasshopper Optimization Algorithm | ||
مراجع | ||
[1] Noham, R., & Tzur, M. (2018). Designing humanitarian supply chains by incorporating actual post-disaster decisions. European Journal of Operational Research, 265(3), 1064-1077. [2] Oruc, B. E., & Kara, B. Y. (2018). Post-disaster assessment routing problem. Transportation research part B: methodological, 116, 76-102. [3] Vahdani, B., Veysmoradi, D., Shekari, N., & Mousavi, S. M. (2018). Multi-objective, multi-period location-routing model to distribute relief after earthquake by considering emergency roadway repair. Neural Computing and Applications, 30(3), 835-854. [4] Rennemo, S. J., Rø, K. F., Hvattum, L. M., & Tirado, G. (2014). A three-stage stochastic facility routing model for disaster response planning. Transportation research part E: logistics and transportation review, 62, 116-135. [5] Edrissi, A., Nourinejad, M., & Roorda, M. J. (2015). Transportation network reliability in emergency response. Transportation research part E: logistics and transportation review, 80, 56-73. [6] Huang, K., Jiang, Y., Yuan, Y., & Zhao, L. (2015). Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 75, 1-17. [7] Torabi, S.A., Doodman, M. and Bozorgi Amiri, A., (2018). Integrating Pre-and Post-Disaster Operations Considering the Restoration of Disrupted Routes and Warehouses. Advances in Industrial Engineering, 52(2), pp.179-192. [8] Danesh Alagheh Band, T.S., Aghsami, A. and Rabbani, M., 2020. A Post-disaster Assessment Routing Multi-Objective Problem under Uncertain Parameters. International Journal of Engineering, 33(12), pp.2503-2508. [9] Bozorgi-Amiri, A., Jabalameli, M. S., & Al-e-Hashem, S. M. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR spectrum, 35(4), 905-933. [10] Döyen, A., Aras, N., & Barbarosoğlu, G. (2012). A two-echelon stochastic facility location model for humanitarian relief logistics. Optimization Letters, 6(6), 1123-1145. [11] Galindo, G., & Batta, R. (2013). Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies. Socio-Economic Planning Sciences, 47(1), 20-37. [12] Chang, F. S., Wu, J. S., Lee, C. N., & Shen, H. C. (2014). Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling. Expert Systems with Applications, 41(6), 2947-2956. [13] Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. [14] Sheu, J. B., & Pan, C. (2014). A method for designing centralized emergency supply network to respond to large-scale natural disasters. Transportation research part B: methodological, 67, 284-305. [15] Kabra, G., & Ramesh, A. (2015). Analyzing ICT issues in humanitarian supply chain management: A SAP-LAP linkages framework. Global Journal of Flexible Systems Management, 16(2), 157-171. [16] Khayal, D., Pradhananga, R., Pokharel, S., & Mutlu, F. (2015). A model for planning locations of temporary distribution facilities for emergency response. Socio-Economic Planning Sciences, 52, 22-30. [17] Ruan, J., Shi, P., Lim, C. C., & Wang, X. (2015). Relief supplies allocation and optimization by interval and fuzzy number approaches. Information Sciences, 303, 15-32. [18] Tofighi, S., Torabi, S. A., & Mansouri, S. A. (2016). Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, 250(1), 239-250. [19] Yadav, D. K., & Barve, A. (2016). Modeling post-disaster challenges of humanitarian supply chains: A TISM approach. Global Journal of Flexible Systems Management, 17(3), 321-340. [20] Cantillo, V., Serrano, I., Macea, L. F., & Holguín-Veras, J. (2018). Discrete choice approach for assessing deprivation cost in humanitarian relief operations. Socio-Economic Planning Sciences, 63, 33-46. [21] Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Cheikhrouhou, N., & Taheri-Moghaddam, A. (2016). An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management. Transportation research part E: logistics and transportation review, 93, 485-509. [22] Shamsi Gamchi, N. and Torabi, A., (2018). Application of option contract in Epidemic control using vaccination. Advances in Industrial Engineering, 52(4), pp.609-620. [23] Tavana, M., Abtahi, A. R., Di Caprio, D., Hashemi, R., & Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre-and post-disaster management considerations. Socio-Economic Planning Sciences, 64, 21-37. [24] Cotes, N., & Cantillo, V. (2019). Including deprivation costs in facility location models for humanitarian relief logistics. Socio-Economic Planning Sciences, 65, 89-100. [25] Rivera-Royero, D., Galindo, G., & Yie-Pinedo, R. (2020). Planning the delivery of relief supplies upon the occurrence of a natural disaster while considering the assembly process of the relief kits. Socio-Economic Planning Sciences, 69, 100682. [26] Abazari, S.R., Aghsami, A. and Rabbani, M., 2020. Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters. Socio-Economic Planning Sciences, p.100933. [27] Ringuest, J. L. (1997). Lp-metric sensitivity analysis for single and multi-attribute decision analysis. European Journal of Operational Research, 98(3), 563-570. [28] Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: theory and application. Advances in Engineering Software, 105, 30-47. [29] Neve, A. G., Kakandikar, G. M., & Kulkarni, O. (2017). Application of grasshopper optimization algorithm for constrained and unconstrained test functions. International Journal of Swarm Intelligence and Evolutionary Computation, 6(165), 2. [30] Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons. | ||
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