طراحی یک مدل ریاضی به‌منظور بهبود کیفیت پاسخگویی به تقاضا در مراکز فوریت‌های پزشکی در یک زنجیره تأمین بشردوستانه

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

2 دانشجوی دکتری مدیریت تولید و عملیات، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

چکیده

ایران جزو کشورهایی به شمار می‌رود که نرخ وقوع فجایع طبیعی به‌ویژه زلزله در آن بسیار بالا بوده و ازاین‌رو، مدیریت زنجیره تأمین بشردوستانه قبل، حین و بعد از حادثه از اهمیت خاصی برخوردار است. یکی از اجزای این زنجیره، مراکز فوریت‌های پزشکی هستند. مراکز فوریت‌های پزشکی با رسیدگی به‌موقع به بیماران باعث کاهش مرگ‌ومیر و زیان‌های جبران‌ناپذیر ناشی از جراحات و صدمات حوادث می‌شوند. بنابراین، محل استقرار این مراکز نقش اساسی در کاهش زمان پاسخ به تقاضا دارد و جانمایی این مراکز از اهمیت بسیار زیادی برخوردار است. هدف اصلی این پژوهش بررسی نحوه جانمایی صحیح مراکز فوریت‌های پزشکی با هدف حداکثر کردن پوشش تقاضا و کاهش زمان امداد و نجات در یک زنجیره تأمین بشردوستانه می‌باشد. به‌منظور پاسخ به مسئله بهبود کیفیت پاسخگویی به تقاضا در مراکز فوریت‌های پزشکی یک مدل ریاضی ارائه شد که با درنظرگرفتن برخی شرایط برای مسئله، اقدام به یافتن بهینه‌ترین تعداد مکان‌ها برای بهبود کیفیت پاسخ‌گویی نموده است. برای حل این مسئله، یک الگوریتم ابتکاری که در آن از دو الگوریتم فرا ابتکاری استفاده شده است، ارائه شد. سپس تعداد 16 سناریو با استفاده از الگوریتم ارائه‌شده حل شد که با توجه به‌دقت پاسخ‌های به دست آمده و سرعت بالای همگرایی الگوریتم پیشنهادی می‌توان از آن در شبکه‌های بزرگ و پیچیده‌ای که در آن‌ها رسیدن به جواب دقیق در زمان معقول امکان‌پذیر نیست، استفاده کرد.

کلیدواژه‌ها


عنوان مقاله [English]

A Mathematical Model to Improve the Quality of Demand Responding in Emergency Medical Centers in a Humanitarian Supply chain

نویسندگان [English]

  • mahamadreza sadeghi moghadam 1
  • iman ghasemian sahebi 2
1 Assistant Professor of Industrial Management Department, Faculty of Management, University of Tehran, Tehran, Iran
2 PhD student of Production and Operation Management, Faculty of Management, University of Tehran, Tehran, Iran
چکیده [English]

In Iran the rate of natural disasters, especially the earthquake, is very high, hence the humanitarian supply chain management before, during and after the disaster occurrence is very important. In other hand emergency medical centers are main part of this chain. Emergency medical centers with timely treatment of injuries reduced the irreversible damage of disaster. Therefore, the location of these centers has a major role in reducing the response time to affected people and the locating of these centers is very important. The main objective of this study is to determine the location of emergency medical centers in order to maximize the demand coverage and reduce the rescue time in a humanitarian supply chain. In order to solve the emergency medical centers location problem a mathematical model was presented, considering some conditions for the problem and sought to find the optimal locations for the establishment of these centers. To solve this problem, an innovative algorithm was presented in which two meta-heuristic algorithms were used. Then, 16 scenarios were solved by using the proposed algorithm. Due to the accuracy of the obtained responses and the high speed of the convergence of the proposed algorithm, it can be used in large and complex problems that cannot reach the exact answer in a reasonable time.

کلیدواژه‌ها [English]

  • Humanitarian supply chain
  • Locating
  • Emergency Medical Centers
  • Simulated Annealing Algorithm

[1]   A. Mohaghar, I. G. Sahebi, and A. Arab, “Appraisal of Humanitarian Supply Chain Risks Using Best-Worst Method,” International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering., vol. 11, no. 2, pp. 292–297, 2017.

[2]   I. Ghasemian Sahebi, A. Arab, and M. R. Sadeghi Moghadam, “Analyzing the barriers to humanitarian supply chain management: A case study of the Tehran Red Crescent Societies,” International  Journal of  Disaster Risk Reduction., vol. 24, pp. 232–241, 2017.

[3]   I. ghasemian Sahebi, “Needs assessment and demand forecasting in the lifecycle of disaster in the humanitarian supply chain,”Master Thesis, University of Tahran, 2015.

[4]   N. Altay and W. G. Green, “OR/MS research in disaster operations management,” European journal of operational research, vol. 175, no. 1, pp. 475–493, 2006.

[5]   V. Schmid and K. F. Doerner, “Ambulance location and relocation problems with time-dependent travel times,” European journal of operational research., vol. 207, no. 3, pp. 1293–1303, 2010.

[6]   K. F. Doerner, W. J. Gutjahr, R. F. Hartl, M. Karall, and M. Reimann, “Heuristic solution of an extended double-coverage ambulance location problem for Austria,” Central European Journal of Operations Research, vol. 13, pp. 325–340, 2005.

[7]   L. Brotcorne, G. Laporte, and F. Semet, “Ambulance location and relocation models,” European journal of operational research., vol. 147, no. 3, pp. 451–463, 2003.

[8]   Z. Drezner, G. O. Wesolowsky, and T. Drezner, “The gradual covering problem,” Naval Research Logistics., vol. 51, no. 6, pp. 841–855, 2004.

[9]   M. S. Daskin, A. Ingolfsson, S. Budge, and E. Erkut, “Optimal ambulance location with random delays and travel times,” Health Care management science., vol. 17, no. 1, pp. 48–70, 1983.

[10] J. B. Goldberg, “Operations Research Models for the Deployment of Emergency Services Vehicles,” Health Care management science, vol. 1, no. 1, pp. 20–39, 2004.

[11] H. Aytug and C. Saydam, “Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study,” European Journal of Operational Research., vol. 141, no. 3, pp. 480–494, 2002.

[12] J. C. Dibene, Y. Maldonado, C. Vera, M. de Oliveira, L. Trujillo, and O. Schütze, “Optimizing the location of ambulances in Tijuana, Mexico,” Computers in biology and medicine., vol. 80, no. November 2016, pp. 107–113, 2017.

[13] K. Hogan and Charles Revelle, “Concepts and Applications of Backup Coverage,”. Management science. vol. 32, no. 11, pp. 1434–1444, 2016.

[14] H. K. Rajagopalan, C. Saydam, E. Sharer, and H. Setzler, “Ambulance Deployment and Shift Scheduling: An Integrated Approach,” Journal of Service Science and Management., vol. 4, no. 1, pp. 66–78, 2011.

[15] P. Woznowski, A. Burrows, T. Diethe, “SPHERE: A Sensor Platform for Healthcare in a Residential Environment: Designing, Developing, and Facilitating Smart Cities”, Springer International Publishing, pp. 315–333, 2017

[16] M. Notash, M. Zandieh, and B. Dorri Nokorani, “Using a Genetic Algorithm Approach for Designing Multi-objective Supply Chain Network,” Modares Journal of  Management. Research in Iran, vol. 18, no. 4, pp. 183–203,2014.

[17] H. Safari, M. Ajali, and I. Ghasemiyan Sahebi, “Determining the strategic position of an educational institution in the organizational life cycle with fuzzy approach (Case Study: Social Sciences Faculty of Khalij Fars University),” Modern Researches in Decision Making., vol. 1, no. 2, pp. 117–138, Jul. 2016.

[18] A. Toghyani, A. Rajabzadeh, and A. Anvari Rostamy, “Designing of Decision Making Model in Uncertainty Conditions,” Modern Researches in Decision Making, vol. 1, no. 1, pp. 189–216, Jul. 2016.