نوع مقاله : مقاله علمی - پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشکده مهندسی صنایع، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران

2 استاد، دانشکده مهندسی صنایع، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران.

3 دانشیار، گروه مهندسی صنایع، واحد کرج، دانشگاه آزاد اسلامی، کرج

4 دانشیار، دانشکده مهندسی صنایع، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران

چکیده

پاسخگویی سریع به نیازها و ارسال اقلام مورد نیاز به مناطق متأثر از بحران اولویت بسیار بالایی در زمان وقوع آن­‌‌ها دارد. به‌علاوه برنامه‌ریزی با توجه به ذات غیرقطعی شدت بحران و تعداد مناطق آسیب‌دیده برای این مواقع ضروری است. در این پژوهش فازهای آمادگی و پاسخ در چرخه مدیریت بحران به کمک مدل­های برنامه‌ریزی ریاضی چندهدفه تحت شرایط عدم قطعیت مدل‌سازی شده است. این رویکرد دارای دو گام اصلی است که در گام اول یعنی فاز آمادگی مکان بهینه مراکز توزیع امداد و همچنین مراکز درمانی، میزان موجودی کالاهای امدادی برای ذخیره­سازی از تأمین­کنندگان را تعیین کرده و در گام دوم یا فاز پاسخ میزان حمل کالاهای امدادی از نقاط تأمین به مراکز توزیع امداد و از مراکز توزیع به نقاط آسیب­دیده و میزان حمل مصدومان از نقاط آسیب­دیده به مراکز درمانی و بیمارستان­ها را توسط آمبولانس‌ها و حمل هوایی تعیین می‌شود. همچنین پارامترهای اساسی آن مانند تقاضا و تعداد مصدومان با توجه به ماهیت مسئله به‌صورت غیرقطعی مدنظر قرار می­گیرد. درنهایت نیز خرابی تسهیلات تأمین‌کننده و توزیع‌کننده در اثر وقوع بحران در نظر گرفته می‌شود که به‌طور مستقیم در ارائه خدمات آن‌ها تأثیرگذار است. جهت حل مدل ریاضی سه‌هدفه از الگوریتم‌های ژنتیک بر مبنای رتبه‌بندی نا مغلوب‌ها (NSGA-II) و گرگ خاکستری چندهدفه (MOGWO) استفاده می‌شود. با مقایسه نتایج الگوریتم‌های فرا ابتکاری با حل دقیق مشخص می‌شود که این الگوریتم‌ها در مدت‌زمان مناسب دارای عملکرد قابل قبولی هستند.

کلیدواژه‌ها

عنوان مقاله [English]

Robust Mathematical Modeling for a Multi-objective Location-Routing-Inventory Problem in Disaster under Demand Uncertainty and Facilities Reliability

نویسندگان [English]

  • Aliakbar Eshghi 1
  • Reza Tavakkoli-Moghaddam 2
  • Sadollah Ebrahimnezhad 3
  • Vahidreza Ghezavati 4

1 School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran

2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran.

3 Department of Industrial Engineering, Islamic Azad University, Karaj Branch, Karaj

4 School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran

چکیده [English]

At the time of natural disasters occurrence, prompt responding and providing required items to affected areas is the most urgent priority. Moreover, given a stochastic nature of the crisis severity and the number of the affected areas,effective planning is a crucial task. In this study, two major steps in the disaster management cycle,namely preparation and response phases, are formulated using a multi-objective mathematical model under uncertainty. In the preparation phase, the optimum location of relief distributions, medical centers and inventories of relief goods to storage items received from suppliers are determined. Also, in the second step or response phase, the amount of relief goods transported from supply points to relief distribution centers and from these centers to affected areas as well as the number of injured people transferred to medical centers and hospitals through ambulances and aerial transportationare determined. Moreover, regarding the problem nature, its key parameters (e.g., demand and the number of injured people) are considered to be uncertain. Furthermore, given that the failureof facilities in both supplier and distributor sections can adverselyaffect their service provision, this issueisconsidered in the model. To efficiently solve the model, the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective grey wolf optimizer (MOGWO) areused. Comparison of the results obtained from the proposed meta-heuristics with the exact solution method indicates that these algorithms can provide acceptable solutions in a reasonable amount of computational time.

کلیدواژه‌ها [English]

  • Robust mathematical model
  • Location-routing-inventory problem
  • Reliability
  • Multi-objective meta-heuristic algorithms
  • Disaster
  1. Halskau, O. (2014). Offshore Helicopter Routing in a Hub and Spoke Fashion: Minimizing Expected Number of Fatalities. Procedia Computer Science, 31, 1124-1132.‏
  2. 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.
  3. Nikoo, N., Babaei, M., & Mohaymany, A. S. (2018). Emergency transportation network design problem: Identification and evaluation of disaster response routes. International Journal of Disaster Risk Reduction, 27, 7-20.
  4. Knott, R., (1987), The logistics of bulk relief supplies, Disasters, 11, 113-115.
  5. Barbarosoğlu, G., Özdamar, L., and Cevik, A., (2002), An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations, European Journal of Operational Research, 140, 118-133.
  6. Özdamar, L., Ekinci, E., Küçükyazici, B., (2004), Emergency logistics planning in natural disasters, Annals of Operations Research, 129, 217-245.
  7. Doerner, K., Focke, A., Gutjahr, W.J., (2007), Multicriteria tour planning for mobile healthcare facilities in a developing country, European Journal of Operational Research, 179, 1078-1096.
  8. Nolz, P.C., Doerner, K.F., Gutjahr, W.J., Hartl, R.F., (2010), A bi-objective metaheuristic for disaster relief operation planning, Advances in multi-objective nature inspired computing, 272, 167-187.
  9. Huang, M., Smilowitz, K., Balcik, B., (2012), Models for relief routing: Equity, efficiency and efficacy, Transportation Research Part E: Logistics and Transportation Review, 48, 2-18.
  10. Rath, S., & Gutjahr, W. J., (2014), A math-heuristic for the warehouse location–routing problem in disaster relief, Computers & Operations Research, 42, 25-39.
  11. 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. Transportation Research Part B: Methodological, 91, 408-423.‏
  12. Fontem, B., Melouk, S. H., Keskin, B. B., & Bajwa, N. (2016). A decomposition-based heuristic for stochastic emergency routing problems. Expert Systems with Applications, 59, 47-59.‏
  13. Zokaee, S., Bozorgi-Amiri, A., & Sadjadi, S. J. (2016). A robust optimization model for humanitarian relief chain design under uncertainty. Applied Mathematical Modelling, 40(17), 7996-8016.
  14. Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498
  15. Manopiniwes, W., & Irohara, T. (2017). Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response. International Journal of Production Research, 55(4), 979-996.‏
  16. Rabbani, M., Zhalechian, M., & Farshbaf‐Geranmayeh, A. (2018). A robust possibilistic programming approach to multiperiod hospital evacuation planning problem under uncertainty. International Transactions in Operational Research , 25(1), 157-189.
  17. Rodríguez-Espíndola, O., Albores, P., & Brewster, C. (2018). Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods. European Journal of Operational Research, 264(3), 978-993
  18. Vahdani, B., Veysmoradi, D., Noori, F., & Mansour, F. (2018). Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. International Journal of Disaster Risk Reduction, 27, 290-306.
  19. Liu, Y., Lei, H., Zhang, D., & Wu, Z. (2018). Robust optimization for relief logistics planning under uncertainties in demand and transportation time. Applied Mathematical Modelling, 55, 262-280.
  20. Davoodi, S. M. R., & Goli, A. (2019). An integrated disaster relief model based on covering tour using hybrid Benders decomposition and variable neighborhood search: Application in the Iranian context. Computers & Industrial Engineering, 130, 370-380.
  21. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.
  22. Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
  23. Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106-119.