مروری بر مقالات مکان‌یابی تسهیلات لجستیک بشردوستانه

نوع مقاله : مقاله مروری

نویسندگان

1 دانشجوی دکتری، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.

2 دانشیار، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.

3 استاد، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.

10.48308/jimp.14.1.57

چکیده

مقدمه و اهداف: علی‌­رغم پیشرفت‌های قابل‌توجه فناوری، دنیای امروز هنوز هم درگیر انواع بلایای طبیعی و انسان‌ساز (مانند زلزله، سیل، طوفان، بهمن، جنگ، تروریسم، ناآرامی‌های سیاسی و غیره) است که نه‌تنها روند توسعه پایدار را کاهش می‌دهد، بلکه در صورت غفلت می‌­تواند صدمات فاجعه‌بار و گاه جبران‌ناپذیری به رفاه یک جامعه وارد کند. این موضوع به یک سیستم یکپارچه لجستیک که به‌صورت علمی و جامع طراحی‌شده است، نیاز دارد تا نیازهای مدیریت بحران را برطرف کند؛ سیستمی با فرآیندهای واضح و از­پیش‌­تعریف‌شده که در آن‌ همه اجزا دارای عملکردهای از‌پیش‌­تعیین‌شده هستند. امدادرسانی به آسیب‌دیدگان یکی از مهم‌ترین مراحل مدیریت بحران است که باید پیش از رخداد حادثه برای آن برنامه‌ریزی کرد؛ زیرا امدادرسانی به‌موقع و بهره‌ور خسارات جانی و مالی را به‌شدت کاهش خواهد داد. برای عدم‌­غافلگیری در زمان بحران‌های طبیعی، باید برنامه‌ریزی‌های مناسب از قبل و در شرایط معمول انجام گیرد. یکی از مسائل حیاتی در زمان بحران، کمک‌­رسانی سریع و به‌موقع است. تدارکات بشردوستانه یکی از مهم‌ترین مسائل عملیات و مدیریت بلایا محسوب می­شود. این در حالی است که عملیات موردنیاز برای تدارکات بشردوستانه باید به‌ اندازه کافی پایدار باشد تا تحت ماهیت نامشخص و پیچیده فاجعه و بحران به‌خوبی عمل کند. بسیاری از مشکلات در مراحل قبل و بعد از بحران، خسارات انسانی و اقتصادی را به همراه دارد و درعمل اطمینان از طراحی کارآمد عملیات تدارکات بشردوستانه ضروری به نظر می­‌رسد. در این پژوهش، با توجه به اهمیت برنامه‌­ریزی پیش از بحران و همچنین فرآیند بهینه‌سازی ریاضی برای مکان­یابی تسهیلات و مراکز امداد، مقالات منتشرشده بین سال‌های 2004 تا 2023 برای بررسی مدل‌های بهینه‌سازی در زمینه تدارکات بشردوستانه بررسی شده است. هدف پژوهش آشنایی با روند پژوهش­‌های فعلی تدارکات بشردوستانه، به‌ویژه مسئله بهینه‌سازی مورداستفاده برای دستیابی به اهداف مختلف بخش‌ مکان‌یابی تسهیلات تدارکات بشردوستانه و ارائه برای پژوهش‌­های آتی است.
روش‌ها: برای دستیابی به یک دید کلی در حوزه‌های پژوهشی و اطلاع از اینکه چه مقالاتی در این زمینه وجود دارد و چه کسانی در این حوزه پژوهش داشته‌اند و اطلاعات جامع‌­تر دیگر، با استفاده از سایت Web of Science، کلیدواژه‌هایی که در این پژوهش مدنظر است، جست­‌وجو شد. در مورد مسائل مکان­یابی تسهیلات، تمامی انواع مسئله مکان­یابی تسهیلات موردمطالعه قرار گرفته و در این مطالعه سعی شده است که مقالات موردبررسی در دو دسته مسائل با مدل­‌های قطعی و غیرقطعی طبقه­‌بندی شوند.
یافته‌ها: در جدول مدل‌­های قطعی، نوع تابع هدف، متغیرهای تصمیم‌­گیری، نوع مدل و روش‌­های حل گنجانده‌شده است. در مدل‌های غیرقطعی، این مطالعه بیشتر رویکردهای برنامه‌ریزی تصادفی و بهینه‌سازی قوی را تحت پوشش قرار می‌دهد؛ همچنین در جدول مدل‌­های غیرقطعی، نوع تابع هدف، متغیرهای تصمیم‌­گیری، پارامترهای غیرقطعی مدل، نوع عدم­‌قطعیت، نوع مدل و روش‌­های حل گنجانده‌ شده است. با توجه به مقالات مرورشده و همچنین با مراجعه به جدول مرور مبانی نظری ارائه‌شده می‌­توان به نکاتی که به آن‌ها پرداخته نشده یا کمتر پرداخته‌ شده است، دست ‌یافت.
نتیجه‌گیری: درنهایت می‌توان نتیجه گرفت که این مطالعه می‌­تواند برای پژوهشگران در درک روند فعلی مسئله بهینه‌سازی در تدارکات بشردوستانه و تکنیک‌­های مدل‌سازی مفید باشد. پژوهشگران می‌توانند به‌راحتی خلأ پژوهش را دریابند و از طریق پژوهش‌­های خود به جامعه کمک کنند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Review of Articles on the Location of Humanitarian Logistics Facilities

نویسندگان [English]

  • Karim Arasteh 1
  • Rouzbeh Ghousi 2
  • Ahmad Makui 3
1 PhD student, Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
2 Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
3 Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
چکیده [English]

Introduction: Despite significant technological developments in today’s world, it is still plagued by all manners of natural and man-made disasters (such as earthquakes, floods, hurricanes, avalanches, war, terrorism, political unrest, etc.) which not only slow the process of sustainability development but also, when neglected, can inflict terrible and sometimes irreparable damage to a community’s well-being and prosperity. This issue requires an integrated logistics system that is scientifically and comprehensively designed to meet the needs of crisis management; a system with clear and predefined processes in which all components have predefined functions. Providing aid to the injured is one of the most important stages of crisis management, which must be planned before the accident because timely and efficient aid will greatly reduce human and financial losses. In order not to be surprised during natural crises, proper planning should be done in advance and under normal conditions.  Humanitarian logistics (HL) is considered one of the most important issues of disaster operations and management. However, the operations required for humanitarian logistics must be sustainable enough to function well under the uncertain and complex nature of disasters and crises. Many difficulties in the pre-disaster and post-disaster phases bring both human and economic losses, and practically ensuring the efficient design of humanitarian logistics operations seems essential. In this study, considering the importance of planning before disasters and also the mathematical optimization model for locating facilities and relief centers, articles published between the years 2004 and 2023 to examine optimization models in the field of humanitarian logistics have been studied. The purpose of the research is to familiarize with the current research process of humanitarian logistics, especially the optimization problem used to achieve various goals of the location of humanitarian logistics facilities and to provide future research directions.
Methods: In order to get an overview of the research fields and to know what articles exist in this field and who has done research in this field and other more comprehensive information, using the Web of Science website, the keywords that are considered in this research, have been searched. Regarding facility location issues, all types of resource location problems are included in this study, and the papers under study are classified into two categories based on deterministic and non-deterministic models.
Results and discussion: In the deterministic models table, the type of objective function, decision variables, model type, and solution methods are included. In non-deterministic models, this study primarily covers the stochastic programming approach and robust optimization. Also, in the non-deterministic model’s table, the type of objective function, decision variables, non-deterministic parameters of the model, type of uncertainty, type of model, and solution methods are included. According to the reviewed articles and also by referring to the presented literature review table, it is possible to find the points that have not been addressed or have been addressed less.
Conclusions: Finally, this study can be useful for researchers in understanding the current trend of the optimization problem in humanitarian logistics and modeling techniques. Researchers can easily find out the research gap and help society through their research.

کلیدواژه‌ها [English]

  • Earthquake
  • Humanitarian logistics
  • Location
  • Location Facility
  • Transfer Point

مراجع

  1. Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective location-transportation problem for disaster response. Comput. Oper. Res, 41, 83–93.
  2. Alinaghian, M., Aghaie, M., & Sabbagh, M.S. (2019). A mathematical model for location of temporary relief centers and dynamic routing of aerial rescue vehicles. IFAC-Papers on Line, 49(12), 520–525.
  3. Alizadeh, M., Amiri-Aref, M., & Ma, J. (2020). A Multi-period Stochastic Casualty Evacuation Network Design Problem. https://www.researchgate.net/ publication: 340491643.
  4. Allahyari, S., Salari, M., Talebian Sharif, M., & Eshtehadi, R. (2013). The design of a transport model in the field of crisis. The Journal of Supply Chain Management, 41, 24-31.
  5. An, S., Cui, N., Bai, Y., Xie, W., Chen, M., & Ouyang, Y. (2015). Reliable emergency service facility location under facility disruption, en-route congestion and in-facility queuing. Transp. Res. Part E Logist. Transp, 82, 199–216.
  6. Arabani, A.B., & Farahani, R. Z. (2012). Facility location dynamics: An overview of classifications and applications. Comput. Ind. Eng, 62, 408–420.
  7. Barzinpour, F., & Esmaeili, V. (2014). A multi-objective relief chain location distribution model for urban disaster management. Int. J. Adv. Manuf. Technol, 70, 1291–1302.
  8. Berman, O. Drezner, Z. & Wesolowsky, G.O. (2007). The transfer point location problem. European Journal of Operational Research, 179, 978-989.
  9. Berman, O., Drezner, Z., & Wesolowsky, G. O. (2005). The facility and transfer points location problem. Intl.Trans. In Op. Res, 12, 387-402.
  10. Berman, O. Drezner, Z. & Wesolowsky, G, O. (2008). Themultiple location of transfer points. Journal of the Operational Research Society, 59, 805 811.
  11. Bertsimas, D., Sim, M. (2004). The Price of Robustness. Oper. Res: 52, 35–53.
  12. Boostani, A., Jolai, F., & Bozorgi-Amiri, A. (2020). Designing a sustainable humanitarian relief logistics model in pre- and postdisaster management. International Journal of Sustainable Transportation: https://doi.org/10.1080/15568318.2020.1773975.
  13. Bozorgi-Amiri, A., Khorsi, M. (2018). A dynamic multi-objective location–routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters. Int. J. Adv. Manuf. Technol, 85, 1633–1648.
  14. Bozorgi-Amiri, A., Tavakoli, S., Mirzaeipour, H., & Rabbani, M. (2017). Integrated locating of helicopter stations and helipads for wounded. American Journal of Emergency Medicine, 35, 410-417.
  15. Campbell, J.F., Ernst, A.T., & Krishnamoorthy, M. (2002). Transfer point location problems. In: Drezner, Z., Hamacher, H.W. (Eds) Facility Location: Applications and Theory.Springer, Berlin.
  16. Caunhye, A.M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socioecon. Plann. Sci, 46, 4–13.
  17. Chang, M.S., Tseng, Y.L., & Chen, J.W. (2007). A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transp. Res. Part E Logist. Transp. Rev, 43, 737–754.
  18. Chen, A.Y., Yu, T.Y. (2016). Network based temporary facility location for the Emergency Medical Services considering the disaster induced demand and the transportation infrastructure in disaster response. Transp. Res. Part B Methodol, 91, 408–423.
  19. Chu, H., Chen, Y. (2021). A Novel Hybrid Algorithm for Multiobjective Location-Allocation Problem in Emergency Logistics. Computational Intelligence and Neuroscience: Article ID 1951161, 12 pages.
  20. Cotes, N. Cantillo, V. (2019). including deprivation costs in facility location models for humanitarian relief logistics. Socioecon. Plann. Sci, 65, 89–100.
  21. Daneshvar, M., Dominik Jena, S., & Rei, W. (2023). A two-stage stochastic post-disaster humanitarian supply chain network design problem. Computers & Industrial Engineering: https://doi.org/10.1016/j.cie.2023.109459.
  22. Flanigan, M., Blatt, A. Batta, R., Lin, Y.H., & Rogerson, P.A. (2011). A logistics model for emergency supply of critical items in the aftermath of a disaster. Socioecon. Plann. Sci, 45, 132–145.
  23. GAO, X. Nayeem, M.K. & Hezam, I.M. (2019). A robust two-stage transit-based evacuation model for large-scale disaster response. Meas. J. Int. Meas, 145, 713–723.
  24. Gökçe, M.A. Ercan, E. (2019). Multi-Period Vehicle Routing & Replenishment Problem of Neighbourhood Disaster Stations for Pre-Disaster Humanitarian Relief Logistics. ScienceDirect. IFAC Papers On Line: 52(13), 2614–2619.
  25. Guha-Sapir, D., Hoyois, P., & below. R. (2012). Annual Disaster Statistical Review 2012 – The Numbers and Trends. Brussels, Belgium: Université Catholique de Louvain.
  26. Hashemi, S. E., Jabbari, M., & Yaghoubi, P. (2022). A mathematical optimization model for location Emergency Medical Service (EMS) centers using contour lines. Healthcare Analytics: www.elsevier.com/locate/health.
  27. Hezam, I. M., Nayeem, M. K. (2021). A Systematic Literature Review on Mathematical Models of Humanitarian Logistics. The Attraction Paradigm. Academic Press, New York: Symmetry 2021, 13, 11.
  28. Hoke, M., Yalcinkaya, S. (2021). Municipal solid waste transfer station planning through vehicle routing problem-based scenario analysis. Waste Management & Research, 39(1,) 185–196.
  29. Hosseinijou, S., Bashiri, M. (2009). New stochastic models for minimax transfer point location problem. International Conference on Computers & Industrial Engineering, 1231-1236.
  30. Hosseinijou, S, A., Bashiri M. (2011). Stochastic models for transfer point location problem. Int J Adv Manufm Techn: DOI 10.100/s 00170-011-3360-0.
  31. Hu, S.L., Han, C.F., & Meng, L.P. (2017). Stochastic optimization for joint decision making of inventory and procurement in humanitarian relief. Comput. Ind. Eng, 111, 39–49.
  32. Jabalameli, M.S., Arasteh, K., & Bozorgi-Amiri, A. (2013). The Multiple Locations of Facilities and Transfer Points in Disaster. Journal of research in operations and its applications.
  33. Jenkins, P.R., Lunday, B.J., & Robbins, M.J. (2019). Robust, multi-objective optimization for the military medical evacuation locationallocation problem. Omega, 97, 1–12.
  34. Jia, H., Ordóñez, F., & Dessouky, M. (2007). A modeling framework for facility location of medical services for large-scale emergencies. IIE Trans, 39, 41–55.
  35. Jia, H., Ordóñez, F., & Dessouky, M.M. (2007). Solution approaches for facility location of medical supplies for large-scale emergencies. Comput. Ind. Eng, 52, 257–276.
  36. Kalantari, H., Yousefli, A., Ghazanfari, M., & Shahanaghi, K. (2013). Fuzzy transfer point location problem: a possibilistic unconstrained nonlinear programming approach. Int J Adv Manuf Technol, 70, 1043-1051.
  37. Kalantari, H., Badiee, A., & Ghazanfari, M. (2014). A New Heuristic Model for Fuzzy Transfer Point Location Problem. Journal of Uncertain Systems, 8, 31-43.
  38. Khayal, D., Pradhananga, R., Pokharel, S., & Mutlu, F. (2015). A model for planning locations of temporary distribution facilities for emergency response. Socioecon. Plann. Sci, 52, 22–30.
  39. Kınay, Ö.B., Kara, B.Y., Saldanha-da-Gama, F., & Correia, I. (2017) modeling the shelter site location problem using chance constraints: A case study for Istanbul. Eur. J. Oper. Res, 270, 132–145.
  40. Kongsomsaksakul, S., Chen, A., & Yang, C. (2005). Shelter Location-Allocation Model for Flood Evacuation Planning. J. East. Asia Soc.Transp. Stud, 6, 4237–4252.
  41. Kouchaki Tajani, T., Mohtashami, A., Amiri, M., & Ehtesham Rasi, R. (2021). Presenting a Robust Optimization Model to Design a Comprehensive Blood Supply Chain under Supply and Demand Uncertainties. Industrial Management Perspective, 11, 81-115. (In Persian)
  42. Kung, E., Seaton, E.S., Ramnarayan, P., & Pagel, C. (2021). Using a genetic algorithm to solve a non-linear location allocation problem for specialized children’s ambulances in England and Wales. Health Systems, 11(3), 161-171.
  43. Li, H., Zhang, B., & Ge, X. (2022). Modeling Emergency Logistics Location-Allocation Problem with Uncertain Parameters. Systems, 10(51), https://doi.org/10.3390/ systems 10020051.
  44. Lin, Y.H. Batta, R. Rogerson, P.A. Blatt, A. & Flanigan, M. (2012). Location of temporary depots to facilitate relief operations after an earthquake. Socioecon. Plann. Sci: 46, 112–123.
  45. Liu, M., Lin, T., Chu, F., & Zheng, F. (2022). A New Stochastic Bi-level Optimization Model for Post-disaster Relief Scheduling Problem in Sustainable Humanitarian Supply Chains with Uncertain Relief Supplies and Demands. IFAC PapersOnLine, 55-10, 2481–2486.
  46. Mahmudian, M. Keivani, A. Davoudpour, H. & Ardestani Jaafari, A. (2010). Two Iterative Algorithms for Transfer Point Location Problem. Journal of American Science. 6(9), 827-830.
  47. Memari, P. Tavakkoli-Moghaddam, R. Navaz, F. & Jolai, F. (2020). Air and ground ambulance locationallocation- routing problem for designing a temporary emergency management system after a disaster. Journal of Engineering in Medicine, 234(8):812-828. sagepub.com/journals-permissions DOI: 10.1177/0954411920925207.
  48. Merakl, M., & Yaman, H. (2016). Robust intermodal hub location under polyhedral demand uncertainty. Transportation Research Part B, 86 66–85.
  49. Mohamadi, A. & Yaghoubi, S. (2017). A bi-objective stochastic model for emergency medical services network. International Journal of Disaster Risk Reduction, 23, 204-217.
  50. Mohammadi, S. Avakh, S. Vahdani, B. & Alinezhad, A. (2018). 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.
  51. Mokhtarzadeh, M. Tavakkoli-Moghaddam, R. Triki, C. & Rahimi, Y. (2021). A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities. Engineering Applications of Artificial Intelligence, 98,104121.
  52. Monzón, J. Liberatore, F. (2020). A Mathematical Pre-Disaster Model with Uncertainty and Multiple Criteria for Facility Location and Network Fortification. Mathematics, 8, 529.
  53. Moreno, A. Alem, D. Ferreira, D. & Clark, A. (2018). An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains. Eur. J. Oper. Res: 269, 1050–1071.
  54. Muggy, L. & Stamm, J.L.H. (2017). Dynamic, robust models to quantify the impact of decentralization in post-disaster health care facility location decisions. Oper. Res. Heal, 12, 43–59.
  55. Mulvey, J.M. Vanderbei, R.J. & Zenios, S.A. (2004). Robust Optimization of Large-Scale Systems. Oper. Res: 43, 264–281.
  56. Ni, W. Shu, J. & Song, M. (2018). Location and Emergency Inventory Pre-Positioning for Disaster Response Operations: Min-Max Robust Model and a Case Study of Yushu Earthquake. Prod. Oper. Manag, 27, 160–183.
  57. Nikjoo, N. Javadian, N. (2021). A Multi-Objective Robust Optimization Logistics Model in Times of Crisis under Uncertainty. The Journal of Industrial Management Perspective, 32, 121–147. (In Persian)
  58. Oksuz, M.K. Satoglu, S.I. (2021). A two-stage stochastic model for location planning of temporary medical centers for disaster response. Int. J. Disaster Risk Reduct, 44, 101426.
  59. Panjehaein, F. Bozorgi Amiri, A. & Khalifeh, M. (2023). A Multi-Objective Location-Allocation-Routing Optimization with Time Window for Transferring Personnel from Residence to Work: A Case Study of Bistoon Power Plant. Iranian Journal of Management Studies (IJMS) 16(1), 25-42.
  60. Pérez-Galarce, F. Canales, L.J. Vergara, C. & Candia-Véjar, A. (2017). An optimization model for the location of disaster refuges. Socioecon. Plann: Sci., 59, 56–66.
  61. Praneetpholkrang, P., Huynh, V., & KanjanawattanabaJapan, S. (2021). A multi-objective optimization model for shelter location-allocationin response to humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 37, 149–156.
  62. Rabbani, M. OladZad Abbas Abadi, N. & Akbarian, N. (2022). Ambulance routing in disaster response considering variable patient condition: NSGA-II and MOPSO algorithms. Journal of industrial. Management optimization: 18(2).
  63. Rath, S. Gutjahr, W.J. (2014). A math-heuristic for the warehouse location-routing problem in disaster relief. Comput. Oper. Res, 42, 25–39.
  64. Rawls, C.G. Turnquist, M.A. (2012). Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socioecon. Plann: Sci. 46, 46–54.
  65. Renkli, Ç. Duran, S. (2015). Pre-Positioning Disaster Response Facilities and Relief Items. Hum. Ecol. Risk Assess 21, 1169–1185.
  66. Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Bozorgi-Amiri, A. (2016). An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Comput. Ind. Eng: 94, 201–215.
  67. Sajedi, S, Sarfaraz, A.H., Bamdad, SH. & Khalili-Damghani, K. (2021). Mathematical Model of Location, Multi-Commodity and Multi-Period in Sustainable Closed-Loop Supply Chain Considering Risk and Demand and Quality Uncertainty (A case Study). The Journal of Industrial Management Perspective, 11, 271-304. (In Persian)
  68. Sasaki, M. Furuta, T. & Suzuki, A. (2008). Exact optimal solutions of the minisum facility and Transfer points location Problems on a network.Intl.Trans.in Op. Res: 15, 295-306.
  69. 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 157, 102578.
  70. Xu, J., Yin, X., Chen, D., An, J. & Nie, G. (2016). Multi-criteria location model of earthquake evacuation shelters to aid in urban planning. Int. J. Disaster Risk Reduct, 20, 51–62.
  71. Zarepor Ashkezari, A. Mosalman Yazdi, H. (2021). Location Allocation of Earthquake Relief Centers in Yazd City Based on Whale Optimization Algorithm. International Journal of Engineering Applications, 34(05), 1184-1194.
  72. Zhang, B. Yang, R. (2018). Location-allocation problem in the emergency logistics system considering lateral transshipment strategy. https://ssrn.com/abstract 4199989.
  73. Zhang, J. Long, D. & Li, Y. (2023). A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands. Transportation Research Part: E 172, 103087.
  74. Wang, Y. Wang, X. Fan, J. Wang, z. & Zhen, L. (2023). Emergency logistics network optimization with time window assignment. Expert Systems with Applications, 214, 119145.
  75. Wang, S. L. Sun, B.Q. (2023). Model of multi-period emergency material allocation for large-scale sudden natural disasters in humanitarian logistics: Efficiency, effectiveness and equity. International Journal of Disaster Risk Reduction, 85, 103530.
  76. Wang, Q. Nie, X. (2023). A location-inventory-routing model for distributing emergency supplies. Transportation Research Part E: Logistics and Transportation Review, 175. 103-156.
  77. Yahyaei, M. Bozorgi-Amiri, A. (2018). Robust reliable humanitarian relief network design: An integration of shelter and supply facility location. Ann. Oper. Res: 283. 897–916.
  78. Yang, Y. Yin, Y. Wang, D. Ignatius, J. Cheng, T.C.E. & Dhamotharan, L. (2023). Distributionally robust multi-period location-allocation with multiple resources and capacity levels in humanitarian logistics. European Journal of Operational Research: 305(3). 1042-1062
  79. Ye, F. Zhao, Q., Xi, M., & Dessouky, M. (2015). Chinese national emergency warehouse location research based on VNS algorithm. Electron. Notes Discret. Math., 47, 61–68.
  80. Yılmaz, H. and Ö. Kabak. (2016). A Multiple Objective Mathematical Program to Determine Locations of Disaster Response Distribution Centers. IFAC-Papers on Line, 13(12), 220–225.
  81. Yin, Y., Yang, Y., Yu, Y., Wang, D., & Cheng, T.C.E. (2023). Robust vehicle routing with drones under uncertain demands and truck travel times in humanitarian logistics. Transportation Research Part B: 174, 102.

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