سامانه مدیریت نشریات علمی دانشگاه شهید بهشتی

آراسته, کریم, قوسی, روزبه, ماکوئی, احمد. (1403). مروری بر مقالات مکان‌یابی تسهیلات لجستیک بشردوستانه. , 14(1), 57-90. doi: 10.48308/jimp.14.1.57
کریم آراسته; روزبه قوسی; احمد ماکوئی. "مروری بر مقالات مکان‌یابی تسهیلات لجستیک بشردوستانه". , 14, 1, 1403, 57-90. doi: 10.48308/jimp.14.1.57
آراسته, کریم, قوسی, روزبه, ماکوئی, احمد. (1403). 'مروری بر مقالات مکان‌یابی تسهیلات لجستیک بشردوستانه', , 14(1), pp. 57-90. doi: 10.48308/jimp.14.1.57
آراسته, کریم, قوسی, روزبه, ماکوئی, احمد. مروری بر مقالات مکان‌یابی تسهیلات لجستیک بشردوستانه. , 1403; 14(1): 57-90. doi: 10.48308/jimp.14.1.57

مروری بر مقالات مکان‌یابی تسهیلات لجستیک بشردوستانه

چشم‌انداز مدیریت صنعتی
دوره 14، شماره 1 - شماره پیاپی 53، 1403، صفحه 57-90    اصل مقاله (1.6 M)
نوع مقاله: مقاله مروری
شناسه دیجیتال (DOI): 10.48308/jimp.14.1.57
نویسندگان
کریم آراسته1؛ روزبه قوسی* 2؛ احمد ماکوئی3
1دانشجوی دکتری، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.
2دانشیار، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.
3استاد، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.
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
مکان­یابی؛ مکان‌­یابی تسهیلات؛ نقاط انتقال؛ زلزله؛ تدارکات بشردوستانه
عنوان مقاله [English]
A Review of Articles on the Location of Humanitarian Logistics Facilities
نویسندگان [English]
Karim Arasteh1؛ Rouzbeh Ghousi2؛ Ahmad Makui3
1PhD student, Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
2Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
3Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
چکیده [English]
Introduction: Despite significant technological advancements, today's world still grapples with various natural and man-made disasters, such as earthquakes, floods, hurricanes, avalanches, wars, terrorism, and political unrest. These events not only impede sustainable development but can also cause severe and sometimes irreparable damage to the well-being and prosperity of communities. This necessitates an integrated logistics system, scientifically and comprehensively designed to meet crisis management needs. Such a system must have clear, predefined processes where all components function according to predetermined roles. Providing aid to disaster victims is a crucial stage of crisis management that must be planned before the occurrence of an event. Timely and efficient aid significantly reduces human and financial losses. Therefore, appropriate pre-crisis planning is essential to avoid being caught unprepared during natural disasters. Humanitarian logistics (HL) is one of the most critical issues in disaster operations and management. HL operations must be sustainable enough to function effectively under the uncertain and complex nature of disasters and crises. Many challenges in pre- and post-disaster phases lead to human and economic losses, making efficient design of HL operations essential. This study reviews articles published between 2004 and 2023 to examine optimization models for locating humanitarian logistics facilities and centers. The purpose is to understand current research trends in HL, particularly the optimization methods used for facility location, and to provide directions for future research.
Methods: To gain an overview of the research landscape and identify relevant articles and key researchers, the Web of Science database was used to search for pertinent keywords. This study includes all types of facility location problems and classifies the reviewed articles into deterministic and non-deterministic models. In the deterministic models table, the type of objective function, decision variables, model type, and solution methods are detailed. For non-deterministic models, the study focuses on stochastic programming and robust optimization approaches. The non-deterministic models table includes the type of objective function, decision variables, non-deterministic parameters, type of uncertainty, model type, and solution methods.
Results and discussion: The review identified 19 factors contributing to the decline of companies, from the 22 factors previously identified in the literature. Factors such as "market monopoly status," "oversupply," and "lack of cooperation culture among employees and managers" were excluded due to their lower importance, as determined by experts. For these identified factors, 32 localized and new strategies were determined and categorized into six groups: financial and economic strategies; marketing and customer orientation; human resources; knowledge-based strategies; structure and interactions; and production and operations efficiency. The hierarchical interpretive structural model of revitalization strategies indicates that these strategies are interdependent, helping and facilitating each other. Effective implementation should start from the lowest level of the model. Notably, although the strategies are categorized, the model shows that it is unnecessary to focus on all strategies within a single category simultaneously. Instead, the hierarchy clearly demonstrates the priority order from the bottom up and across different categories.
Conclusions: Reviving declining and bankrupt small and medium-sized companies, particularly in the food industry, is not a one-dimensional process and does not have a single strategy. Instead, it requires a combination of six strategy categories: financial and economic; marketing and customer orientation; human resources; knowledge-based strategies; structure and interactions; and production and operations efficiency. The revival process must be systematic, continuous, and gradual. This study can help researchers understand current optimization trends in HL and identify research gaps to contribute to societal well-being 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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