مدل بهینه سازی مکانیابی تخصیص تسهیلات قابل اطمینان تحت - ریسک اختلال در تسهیلات

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشیار گروه مهندسی صنایع دانشگاه الزهرا، تهران، ایران

2 کارشناس ارشد مهندسی صنایع، دانشگاه الزهرا، تهران، ایران

چکیده

در مدل های کلاسیک مکانیابی تسهیلات به طور ضمنی فرض می شود که تسهیلات انتخاب شده همواره طبق
برنامه کار خواهند کرد، در حالی که، در دنیای واقعی تسهیلات همواره در معرض ریسک اختلال هستند و
گاهی این اختلالات اثر بلند مدت روی شبکه زنجیره تأمین گذاشته و آن را با بحران مواجه می کند. در این
مقاله، یک مدل برنامه ریزی عدد صحیح مختلط جهت تعیین نحوه خدمت رسانی به مشتریان در زمان اختلال
مراکز توزیع در یک زنجیره تأمین دو سطحی شامل توزیع کنندگان و مشتریان ارائه شده است. این مدل
مکان هایی را برای توزیع کننده ها انتخاب می کند که علاوه بر حداقل نمودن هزینه های معمول زنجیره تأمین،
هزینه های حمل و نقل بعد از مختل شدن توزیع کننده ها نیز حداقل شوند. در واقع سعی می شود انتخاب محل
توزیع کنندگان با کم ترین هزینه و بیشترین قابلیت اطمینان صورت گرفته و ضمناً تخصیص مشتریان به آنها
انجام شود. با استفاده از رویکرد لاگرانژ مسأله آزاد سازی شده و به دو زیر مسئله تقسیم می شود. با بررسی
شرایط بهینگی زیر مسئله ها، حل ابتکاری برای زیر مسأله اول و الگوریتم ژنتیک برای زیر مسأله دوم به منظور
حل مسائل با ابعاد بزرگ استفاده شده است. در پایان، عملکرد و کارایی مدل و روش پیشنهادی در قالب
مثال های عددی مورد بررسی قرار می گیرند .

کلیدواژه‌ها


عنوان مقاله [English]

Reliable Facility Location-Allocation Optimization Model under the Risk of Disruptions Mehdi Seifbarghy,* Shima Zangeneh**

نویسندگان [English]

  • Mehdi Seifbarghy 1
  • Shima Zangeneh 2
چکیده [English]

In the classic models of facility location, it is assumed that the selected facilities always work based on the schedule while, in the real world, facilities are always exposed to disruption risk and sometimes these disruptions have long-term effects on the supply chain network and cause a lot of problems. In this paper, a mixed integer programing (MIP) model presented in order to determine how to serve the customers at the time of disruption in distribution centers in a two-echelon supply chain, including distribution centers and customers. This model selects potential places that minimize traditionally supply chain costs and also the transportation cost after distribution centers disruptions. In fact, the model tries to choose the distribution centers facilities with lowest cost and highest reliability and also allocate them to customers. The problem divided into two sub-problems using Lagrangian relaxation approach. By examining sub-problems optimal conditions, a heuristic solution is used for the first sub-problem and a genetic algorithm is used for the second sub-problem to solve large-scale problems. Finally, numerical examples are presented to examine the performance and efficiency of the proposed model and approach

کلیدواژه‌ها [English]

  • Location-Allocation
  • Supply Chain Management
  • Disruptions
  • Genetic Algorithm
  • Lagrangian Relaxation
 
Arianezhad, M. B., & Jabbarzadeh, A. (2009). An integrated model for location-inventory problem with random disruptions, Computers & Industrial Engineering, CIE 2009. International Conference on, 2009. IEEE, pp. 791-796.
Asl-Najafi, J., Zahiri, B., Bozorgi-Amiri, A. & Taheri-Moghaddam, A. (2015). A dynamic closed-loop location-inventory problem under disruption risk, Computers & Industrial Engineering, Volume 90, December 2015, Pages 414–428
Azad, N., Saharidis, G. K., Davoudpour, H., Malekly, H., & Yektammaram, S. A. (2012). Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach, Annals of Operations Research, 210(1), pp. 125-163.
Berman, O., Krass, D. & Menezes, M. B., (2009). Locating Facilities in the Presence of Disruptions and Incomplete Information, Decision Sciences, 40(4), pp. 845-868.
Bradley, J.R., (2014). An improved method for managing catastrophic supply chain disruptions, Business Horizons, 57(4), pp. 483–495.
Cui, J., Zhao, M., Li, X., Parsafard, M. & An, S. (2016). Reliable design of an integrated supply chain with expedited shipments under disruption risks, Transportation Research Part E: Logistics and Transportation Review, Volume 95, November 2016, Pages 143–16.
Cui, T., Ouyang, Y. & Shen, Z.-J. M. (2010). Reliable facility location design under the risk ofdisruptions, Operations Research, 58(4), pp. 998-1011
Daskin, M. S. (1995). Network and Discrete Location: Models, Algorithms and Applications, New York, Wiley
Drezner, Z. (1987). Heuristic solution methods for two location problems with unreliable facilities, Journal of the Operational Research Society, 38(6), pp. 509-514.
Jabbarzadeh, A., Fahimnia, B., Jiuh-Biing, S. & Shahmoradi Moghadam, H. (2016), Designing a supply chain resilient to major disruptions and supply/demand interruptions, Transportation Research Part B: Methodological, Volume 94, December 2016, Pages 121–149
Jabbarzadeh, A., Jalali Naini, S. G., Davoudpour, H., & Azad, N. (2012). Designing a supply Chain network under the risk of disruptions, Mathematical Problems in Engineering.
Kumar Paul, S., Sarker, R. &  Essam, D. (2017), A quantitative model for disruption mitigation in a supply chain Production, Manufacturing and Logistics, European Journal of Operational Research, Volume 257, Issue 3, 16 March 2017, Pages 881–895.
Leonard, D. (2005), The only lifeline was the Wal-Mart", Fortune, 152, PP. 74-80.
Li, Q. & Savachkin, A. (2013). A heuristic approach to the design of fortified distribution networks, Transportation Research Part E: Logistics and Transportation Review, 50, pp. 138-148.
Li, Q., Zeng, B., & Savachkin, A. (2012). Reliable facility location design under disruptions, Computers & Operations Research, 40(4), pp. 901-909.
Lim, M., Daskin, M. S., Bassamboo, A. & Chopra, S. (2010). A facility reliability problem: Formulation, properties, and algorithm”, Naval Research Logistics (NRL), 57(1), pp. 58-70
Maliszewski, P. J., Kuby, M. J. & HORNER, M. W. (2012). A comparison of multi-objective spatial dispersion models for managing critical assets in urban areas”, Computers, Environment and Urban Systems, 36, pp. 331-341.
Peng, P., Snyder, L. V., Lim A. & Liu, Z., (2011), “Reliable logistics networks design with facility disruptions”, Transportation Research Part B: Methodological, 45, 1190-1211.
Shen, Z. J. M., Zhan, R. L., & Zhang, J., (2007), “The Reliable Facility Location Problem: Formulations, Heuristics, and Approximation Algorithms”, Informs journal of computing, 23(3) pp. 470 – 482.
Snyder, L. V., & Daskin, M. S., (2005), “Reliability models for facility location: the expected failure cost case”, Transportation Science, 39, pp. 400-416.
Snyder, L. V., (2003), “Supply chain robustness and reliability”, Models and algorithms. Northwestern University.
Yun, L., Qin, Y., Fan, H., Ji, C., Li, X., & Jia, L., (2015), “A reliability model for facility location design under imperfect information”, Transportation Research Part B,81(2), pp. 596–615.
Zhan, R. L., (2007), “Models and algorithms for reliable facility location problems and system reliability optimization”, University of Florida.
Zhang, Y., Qi, M., Lin, W. & Miao, L., (2015). “A metaheuristic approach to the reliable location routing problem under disruptions”. Transportation Research Part E, 83, pp. 90-110.
Zhang, Y., Snyder, L., Ralphs, T. & Xue, Z. (2016). The competitive facility location problem under disruption risks, Transportation Research Part E: Logistics and Transportation Review, Volume 93, September 2016, Pages 453–473.