بهبود کیفیت انتقال مصدومان هنگام وقوع بلایای طبیعی از نقاط مختلف جغرافیایی

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استادیار دانشکدة مهندسی مواد و صنایع، دانشگاه سمنان

2 کارشناس ارشد MBA- مدیریت عملیات دانشکدة مهندسی مواد و صنایع، دانشگاه سمنان

چکیده

جلوگیری از تبدیل بلایای طبیعی به فجایع طبیعی مستلزم افزایش توان مدیریتی و برنامه‌ریزی‌های مدیریت بحران است. به‌این‌منظور، سیستم حمل‌ونقل مصدومان ‌هنگام تخریب زیرساخت‌های شهری نقش غیرقابل‌انکاری ایفا می‌کند؛ بنابراین، وجود یک سیستم حمل‌ونقل اضطراری مبتنی‌بر علوم مهندسی و کاربردی در چنین شرایطی ضروری به نظر می‌رسد. در این پژوهش، سیستم حمل‌ونقل مصدومان به مراکز درمانی با رویکرد انتقال دومرحله‌ای و حمل‌ونقل اشتراکی معرفی شده است. در این رویکرد، مصدومان در مرحلة اول به‌طور مستقیم به مراکز امدادی واسط و در مرحلة دوم به‌صورت اشتراکی و با استفاده از امداد هوایی به مراکز درمانی اصلی منتقل می‌شوند. هدف تعیین نحوة تخصیص مصدومان به وسایل نقلیة امدادی، تعیین اولویت حمل آن‌ها و مسیریابی وسایل نقلیة امدادی به نحوی است که زمان انتظار مصدومان به‌منظور رسیدن به مراکز درمانی اصلی کمینه شود. پیچیدگی این مسئله از نوع NP-hard است، درنتیجه حل بهینة آن در زمان معقول با استفاده از روش‌های دقیق ممکن نیست. به همین منظور، از الگوریتم ژنتیک برای حل مسائل استفاده و سپس نتایج در سه سناریوی مختلف تحلیل شده است. سناریوی اول انتقال مستقیم مصدومان به مراکز درمانی اصلی، سناریوی دوم انتقال مستقیم مصدومان به مراکز امدادی واسط و حمل زمینی و غیراشتراکی آن‌ها به سوی مراکز درمانی اصلی و سناریوی سوم رویکرد پیشنهادی پژوهش است. به‌منظور بررسی عملکرد هریک از سناریوها، 216 مسئله برای پوشش طیف متنوعی از شرایط اضطرار درنظر گرفته شده‌اند. نتایج نشان می‌دهد سناریوی سوم تأخیر کمتری در رسیدن مصدومان به مراکز درمانی اصلی در پی دارد که به‌طور مستقیم موجب بهبود کیفیت در انتقال مصدومان می‌شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Injured transportation quality enhancement during natural disaster from the various geographical zones

نویسندگان [English]

  • Mohammad Ali Beheshti Nia 1
  • Mostafa Moghimi 2
2 Semnan University
چکیده [English]

- Introduction
A hazard is a situation that poses a level of threat to life, health, property, or environment. Most hazards are dormant or potential, with only a theoretical risk of harm; however, once a hazard becomes "active", it can create an emergency. Preventing harmful results of natural disasters such as flood, earthquake, tsunami, storm and etc requires crisis management planning. One of the most important parts of fatality during this events is caused by weakness in transportation system, especially in injured transfer system to vital treatment. This paper proposes an injured transportation system which is one of the most important subjects in decreasing human fatality during crisis. In this system at first, casualties must be taken to a primary medical center by road relay vehicles (like ambulances, or personal vehicles), then be transferred to master medical centers by air relay vehicles (like helicopters) considering required treatment. On the other hand, due to the high price and limitation of air transportation, it is supposed that casualties will be transferred using integrated mode to the master medical centers. The most important reason of using air transportation is that when the natural disasters happen, roads are usually blocked as result of damages caused by crisis effects. Consequently, we have heavy traffic or blocking on the streets, highways and roads. Additionally, in proposed system every primary medical center can use common vehicles instead of independent transportation to move injures.
Importance of this subject is that it is possible to transfer wounded who are located in the same geographic area by using integrated transportation due to high damaging probability in foundations and usual paths when natural evens occur. Fast integrated transportation is considered in second stage. Finally, we compare our proposed transportation system with two other systems in which the first system uses direct transportation of injuries to the master medical centers and the second one uses two-stage transportation system by road vehicles. The aim is determining the assignment of injures to the air relay vehicles and determining the priority of injures for transferring to the master medical centers. The objective is minimizing the total waiting time of injures to receive to the master medical centers.

- Methodology
This research is applicable and is done with analytical approach. To find the best parameters for alternatives of problems that are solved in this paper, we considered to 8 crisis engagement specialists and emergency section of master hospitals. Then to maximum coverage of problem ranges, 216 random problems are produced to consider several condition of problems in crisis. A genetic algorithm with variable chromosomes structure is proposed to solve and optimize these problems. In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a met heuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.
After codding the proposed algorithm, this problem is ran on several random problems. Structure of this random problems includes the various range of problems. All of the computational programs are written on Matlab R2013a and ran by a computer with Intel, corei5, 2.6GHz Processor.
After several runs and by experimenting, we understand that amounts 100 for populations, 0.05 for mutation rate, 0.5 for best parameter, and 50 for termination parameter, result to the best answers in a reasonable solving-time.

- Result and discussion
Three scenarios have been proposed to survey in this problem, including: direct transfer of injuries to the master medical centers, integrated two-stage transfer by road vehicles and integrated two-stage transfer by a combination of road vehicles (in the first stage) and air vehicles (in the second stage),. All three scenarios are compared with each other. For this, 216 random test problems with different structures to have maximum coverage of crisis conditions is created and they have been solved by proposed genetic algorithm. Considering destruction of urban infrastructures when a disaster occurs, we considered a fast-air-transportation in part 2 of transportation system to deliver injures to master hospitals. Results is shown that the third scenario is the best injured transportation system scenario to minimize waiting injured time arriving to the master hospital (considering her sore). The numerical results represents this matter that using the system that is proposed in this paper have the less amount of waiting times comparison to 2 other scenarios that studied.

- Conclusion
One of the most effective issues on number of fatality resulted by natural disasters, is transportation systems to convey wounded to medical centers. In this essay we tried to survey ways of reducing waiting time of casualties to master medical centers using integrated transportation system and two-stage transfer. After problem representation a genetic algorithm to solve this problem is proposed. To survey results, we created several random problems in special structure that they show various range of problems. Then these random problems were solved by proposed algorithm. According to obtained numerical results, the proposed system have considerable effects on reduction of injures waiting time to master medical centers. Hence, it propose that stables a central data receiver-distributer organization to receive injures data from primary medical centers and transportation system condition, combine and solve them in proposed model and give transportation missions to system to minimize injures waiting time and saving more lives.
At the end, usage of integrated air transportation system may impose more expenses to system, but it has advantages like less injures waiting time to arrive to master medical centers, more reliability, less number of round trip, less number of vehicle and…, that can justify this expensiveness.

کلیدواژه‌ها [English]

  • natural disaster
  • urban infrastructure
  • Cumulative transportation
  • Two stage transferring system
  • Genetic Algorithm
  1. آراسته، کریم، بزرگی امیری،علی و محمد سعید جبل عاملی، 1394، مکانیابی چندگانه تسهیلات و نقاط انتقال مصدومین در زمان بحران، مجلة تحقیق در عملیات و کاربردهای آن، سال دوازدهم، شمارة 1، صص 21- 31.
  2. اسمیت، کیت، 1382، مخاطراتمحیطی، ترجمة ابراهیم مقیمی و شاپور گودرزی‌نژاد، انتشارات سمت، صص 110- 116.
  3. احمدی، مرتضی، سیفی، عباس و علیرضا قرهی، 1392، مدل لجستیک امدادرسانی برای کاهش تلفات بعد از زلزله در ابعاد بسیار بزرگ واقعی، دوفصلنامة علمی-پژوهشی مدیریت بحران، شمارة 2، صص 51- 64.
  4. امیری، مجتبی، نوروزی، شهناز و علیرضا نجاری، 1394، بهینهسازی مدیریت شبکه حمل‌ونقل اضطراری کلان‌شهر تهران پس از سوانح طبیعی با رویکرد آینده‌پژوهی، پژوهش‌های جغرافیای انسانی، دورة چهل‌و‌هفتم، شمارة 1، صص 143- 157.
  5. بزرگی امیری، علی، جبل عاملی، محمدسعید، حیدری، مهدی و زکریا کریمی راد، 1389، ارائةیک رویکردبرنامه‌ریزی امکانیتک‌هدفهجهتمدلسازی لجستیکبشردوستانه، نشریة علمی پژوهشی مدیریت فردا، شمارة 25، صص 83- 96.
  6. پورمحمدی، محمدرضا، ملکی، کیومرث، شفاعتی، آرزو، حیدری فر، محمدرئوف و محمدرضا کرمی، 1394، پدافند غیرعامل و ضرورت ایجاد کاربری‌های چندمنظوره: رویکردی جدید در آینده‌نگری توسعه و امنیت پایدار شهری با تأکید بر زلزله‌خیزی شهر تبریز، پژوهش‌های جغرافیای انسانی، دورة چهل‌و‌هفتم، شمارة 2، صص 209- 231.
  7. جبل عاملی، محمدسعید، بزرگی امیری، علی و مهدی حیدری، 1390، ارائة مدل برنامه‌ریزی امکانی چندهدفه برای مسائل لجستیک امداد، نشریة بین‌المللی مهندسی صنایع و مدیریت تولید، دورة 22، شمارة 1، صص 66- 76.
  8. نجفی، مهدی، عشقی، ساسان و کوروش عشقی، 1393، ارائۀ مدلی یکپارچه جهت پاسخگویی به زلزله و جایگاه و اهمیت لجستیک در آن، ماهنامة علمی تخصصی لجستیک و زنجیرة تأمین، سال سوم، شمارة 36، صص 8- 19.

 

  1. Ahmadi, M., Seifi, A. and Gharahi, A., 2013, Relief logestic model in real large scale for die reduction after earthquake, Journal of Emergency Management, Vol. 2, No. 2, PP. 51-64. (In Persian)
  2. Arasteh, K., Bozorgi Amiri, A. and Jabal Ameli, M., 2015, Multi facilities and injured transfer points locating in crisis, Journal of Operation Researches in ITS Applications, Vol. 12, No. 1, PP. 21- 31. (In Persian)
  3. Amiri, M., Noruzi, Sh. and Najari, A., 2015, Optimization of Emergency Transportation Network Management of Tehran Metropolis after Natural Hazards with Future Research Approach, Human Geography Research Quarterly, Vol. 47, No. 1, PP. 143- 157. (In Persian)
  4. Barbarosolu, G., Ozdamar, L. and Cevik, A., 2002, An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations, European Journal of Operational Research, Vol. 140, No. 1, PP. 118- 133.
  5. Berman, O. and Huang, R., 2004, The mini-sum collection depots location problem with multiple facilities on networks, Journal of Operational Research Society, No. 55, PP. 769– 779.
  6. Bozorgi Amiri, A., Jabal Ameli, M., Heidari, M. and Karimi-Rad, Z., 2010, A humanitarian lo model to minimize losses aftermath of an earthquake in large-scale and actual size, Modiriate Farda Journal, No. 25, PP. 83- 96. (In Persian)
  7. Douglas, L., 1997, Logistics for Disaster Relief, IIE Solutions, PP. 26- 29.
  8. Esmit, K., 2003, Environmental Hazards, Translated by: Moghimi, E., Gudarzinejad, Sh., Samt Publication, PP. 110- 116. (In Persian)
  9. Furuta, T. and Tanaka, K., 2013, Minisum and Minimax Location Models For Helicopter EmergencyMedical Service Systems, The Operations Research Society of Japan, Vol.56, No. 3, PP. 221- 242.
  10. Garey, M. R., Johnson, D. S. and Sethi, R., 1976, The complexity of flow shop and job shop scheduling, Mathematics of Operation Research, Vol. 1, No. 2, PP. 117– 129.
  11. Gong, Q. and Batta, R., 2007, Allocation and reallocation of ambulances to casualty clusters in disaster relief operation, IIE Transactions (Institute of Industrial Engineers) Vol. 39, No.1, PP. 27- 39.
  12. Hale, T. and Moberg, C. R., 2005, Improving supply chain disaster preparedness: A decision process for secure site location, International Journal of Physical Distribution & Logistics Management, Vol. 35, No. 3, PP. 195- 207.
  13. Holland, J., Goldberg, D. and Booker, L., 1989, Classifier systems and genetic algorithms, Artificial Intelligence, Vol. 40, No. 1-3, PP. 235– 282.
  14. Jabal Ameli, M., Bozorgi Amiri, A. and Heidari, M., 2011, A Multi-Objective Possibilistic Programming Model for Relief Logistics Problem, International Journal Of Industrial Engineering And Production Management, Vol. 22, No. 1, PP. 66- 76. (In Persian)
  15. Najafi, M., Eshghi, S. and Eshghi, K., 2014, An intergrated model for responsing to earthquake and the importance of logistics in it, Scientific Monthly Newsletter of Logistics & Supply Chain, Vol. 3, No. 36, PP. 8- 19. (In Persian)
  16. Overstreet, R. E. et al., 2011, Research in humanitarian logistics, Journal of Humanitarian Logistics and Supply Chain Management, Vol. 1, No. 2, PP. 114- 131.
  17. Purmohammadi, M., Maleki, K., Shafaati, A., Heidarifar, M. and Karami, M., 2015, New look at the future of passive defense and multipurpose uses: New approach in urban sustainable development and security with emphasis on earthquake susceptibility of Tabriz city, Human Geography Research Quarterly, Vol. 47, No. 2, PP. 209- 231. (In Persian)
  18. Yi, W. and Ozdamar , L., 2007, A dynamic logistics coordination model for evacuation and support in disaster response activities, European Journal of Operational Research, Vol. 179, No. 3, PP. 1177- 1193.
  19. Ozdamar, L., Ekinci , E. and Kkyazici, B., 2004, Emergency logistics planning in natural disasters, Annals of Operations Research, Vol. 129, No. 1, PP. 217- 245.