ارائه رویکرد برنامه‌ریزی دوسطحی چندپیرو در حالت عدم همکاری برای موقعیت‌یابی از پیش‌انبارهای اضطراری بحران

نوع مقاله : مقاله پژوهشی

نویسندگان

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

2 استاد گروه مهندسی برق و کامپیوتر، دانشکده فنی و مهندسی، دانشگاه خوارزمی، تهران، ایران

3 دانشیار گروه مهندسی صنایع، دانشکده مهندسی صنایع، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران

چکیده

ساختار غیر متمرکز تصمیم‌گیری در طراحی شبکه انبارهای اضطراری بحران، استفاده از مدل‌های بهینه‌سازی کلاسیک را با چالش مواجه می‌کند. هدف این مقاله توسعه یک مدل جدید بهینه‌سازی دوسطحی چندپیرو برای مسأله مکان‌یابی و تخصیص انبارهای اضطراری در سطوح ملی و منطقه‌ای بوده است. مسأله به‌صورت عدم همکاری پیروها مدل‌سازی شده است. این نوع مدل‌سازی برای کشورهایی که طراحی شبکه انبارهای اضطراری بحران آن‌ها به‌صورت غیرمتمرکز است مناسب می‌باشد. پارامترهای مدل‌ها براساس داده‌های واقعی کشور ایران تنظیم شده است. با توجه به پیچیدگی‌های بالای حل، یک رویکرد تکاملی هم‌زمان برمبنای روش‌های تخصیص ابتکاری و الگوریتم ژنتیک جهت حل مسأله در سایزهای مختلف توسعه داده شده است. ساختار حل به‌گونه‌ای انعطاف‌پذیر طراحی شده و براساس تعداد پیروها و میزان قدرت آن‌ها قابلیت تنظیم را دارا می‌باشد. در نهایت، تحلیلی در خصوص تغییر تعداد تصمیم‌گیرندگان و قدرت آن‌ها درجذب تسهیلات و تأثیر بر توابع هدف مدل دوسطحی صورت گرفته است.

کلیدواژه‌ها


عنوان مقاله [English]

A multi-follower Bi-level Programming Approach in Uncooperative with Emergency Warehouses Pre-positioning

نویسندگان [English]

  • Ehsan Saghehei 1
  • Azizollah Memariani 2
  • Ali Bozorgi 3
1 Ph.D. in Industrial Engineering, Faculty of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2 Professor, Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
3 Associate Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Campus of Technical Colleges, University of Tehran, Tehran, Iran
چکیده [English]

The decentralized decision-making structure in the design of crisis emergency warehouse network challenges the use of classical optimization models. The aim of this paper is to develop a new multi-follower bi-level optimization model for the emergency warehouse location-allocation problem in terms of national and regional levels. This type of modeling is suitable for countries whose crisis warehouse network design is decentralized. The parameters of the models are based on real data in Iran. Due to the high complexity of the solution, a co-evolutionary approach based on innovative allocation methods and genetic algorithms has been developed to solve the problems with different sizes. The solution structure is designed to be flexible and can be adjusted based on the number of followers and their authority. Finally, an analysis has been done about the change in the number of decision makers and their power to absorb facilities on the objective functions of the bi-level model.

کلیدواژه‌ها [English]

  • Pre-positioning relief item
  • Disaster management
  • Multi Follower Bi-level programming
  • Co-Evolutionary algorithm
  • Emergency Warehouse Location-Allocation Problem
  • Disaster 2019: Year in Review, in Cred Crunch Newsletter. 2020.
  • Akkihal, A.R., Inventory pre-positioning for humanitarian operations. 2006, Massachusetts Institute of Technology.
  • Verma, A. and G.M. Gaukler, Pre-positioning disaster response facilities at safe locations: An evaluation of deterministic and stochastic modeling approaches. Computers & Operations Research, 2015. 62: p. 197-209.
  • Rawls, C.G. and M.A. Turnquist, Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Economic Planning Sciences, 2012. 46(1): p. 46-54.
  • Kongsomsaksakul, S., Y. Chao, and C. Anthony, Shelter location-allocation model for flood evacuation planning. Journal of the Eastern Asia Society for Transportation Studies, 2005. 6: p. 4237-4252.
  • Hua-li, S., W. Xun-qing, and X. Yao-feng, A Bi-level programming model for a multi-facility location-routing problem in Urban emergency system, in Engineering Education and Management. 2012, Springer. p. 75-80.
  • Li, A.C., et al., Shelter location and transportation planning under hurricane conditions. Transportation Research Part E: Logistics and Transportation Review, 2012. 48(4): p. 715-729.
  • Jing, W., et al. Multi-level emergency resources location and allocation. in 2010 IEEE International Conference on Emergency Management and Management Sciences. 2010. IEEE.
  • Li, K. and D. Yinhong. The algorithms for the bi-level programming location model based on the demand assigning. in 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). 2013. IEEE.
  • Camacho-Vallejo, J.-F., et al., A bi-level optimization model for aid distribution after the occurrence of a disaster. Journal of Cleaner Production, 2015. 105: p. 134-145.
  • Gutjahr, W.J. and N. Dzubur, Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transportation Research Part E: Logistics and Transportation Review, 2016. 85: p. 1-22.
  • Xu, J., et al., A new model for a 72-h post-earthquake emergency logistics location-routing problem under a random fuzzy environment. Transportation Letters, 2016. 8(5): p. 270-285.
  • Chen, Y.-x., et al., Supply allocation: bi-level programming and differential evolution algorithm for Natural Disaster Relief. Cluster Computing, 2017: p. 1-15.
  • Safaei, A.S., S. Farsad, and M.M. Paydar, Robust bi-level optimization of relief logistics operations. Applied Mathematical Modelling, 2018. 56: p. 359-380.
  • Haeri, A., et al., A bi-level programming approach for improving relief logistics operations: A real case in Kermanshah earthquake. Computers & Industrial Engineering, 2020: p. 106532.
  • Saghehei, E., A. Memariani, and A. Bozorgi-Amiri, A Bi-level Programming Approach for Pre-positioning Emergency Warehouses. International Journal of Engineering, 2021. 34(1): p. 128-139.
  • Simaan, M. and J.B. Cruz Jr, On the Stackelberg strategy in nonzero-sum games. Journal of Optimization Theory and Applications, 1973. 11(5): p. 533-555.
  • Sakawa, M. and I. Nishizaki, Cooperative and noncooperative multi-level programming. Vol. 48. 2009: Springer Science & Business Media.
  • Bracken, J. and J.T. McGill, Mathematical programs with optimization problems in the constraints. Operations Research, 1973. 21(1): p. 37-44.
  • Talbi, E.-G., Metaheuristics for bi-level optimization. Vol. 482. 2013: Springer.
  • Zhang, G., J. Lu, and Y. Gao, Multi-level decision making. 2015: Springer.
  • Liu, B., Stackelberg-Nash equilibrium for multilevel programming with multiple followers using genetic algorithms. Computers & Mathematics with Applications, 1998. 36(7): p. 79-89.
  • Lu, J., C. Shi, and G. Zhang, On bilevel multi-follower decision making: General framework and solutions. Information Sciences, 2006. 176(11): p. 1607-1627.
  • Du, C.-J. and H.-C. Li, Genetic algorithm for solving a class of multi-follower fractional bi-level programming problems. Jisuanji Yingyong/ Journal of Computer Applications, 2012. 32(11): p. 2998-3001.
  • Sinha, A., et al., Finding optimal strategies in a multi-period multi-leader–follower Stackelberg game using an evolutionary algorithm. Computers & Operations Research, 2014. 41: p. 374-385.
  • Gao, Y., Bi-level decision making with fuzzy sets and particle swarm optimisation. 2010.
  • Hansen, P., B. Jaumard, and G. Savard, New branch-and-bound rules for linear bilevel programming. SIAM Journal on scientific and Statistical Computing, 1992. 13(5): p. 1194-1217.