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  • مکان یابی بهینه استقرار آمبولانس ها در شبکه جاده ای استان آذربایجان شرقی با رویکرد ترکیبی شبیه سازی عامل بنیان و الگوریتم ژنتیک

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کد مقاله : IMJ-2101-1525 (R2) بازدید : 60 صفحه: 172 - 187

10.30495/imj.2021.686493

نوع مقاله: پژوهشی

مکان یابی بهینه استقرار آمبولانس ها در شبکه جاده ای استان آذربایجان شرقی با رویکرد ترکیبی شبیه سازی عامل بنیان و الگوریتم ژنتیک

محورهای موضوعی : مدیریت صنعتی

Bijan Elmi 1 (PhD Candidate in Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Naghei Shoja 2 (Faculty of Basic Sciences, Islamic Azad University, Firuzkuh Branch, Tehran, Iran.)
Abbass Toloie Ashlaghi 3 (Professor of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Soleyman Iranzadeh 4 (Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran)

تاریخ دریافت : 1399/10/13 تاریخ پذیرش : 1400/07/23 تاریخ انتشار : 1400/08/30

کلید واژه: شبکه جاده‌ای, استقرار, مکانیابی, مدلسازی عامل بنیان,

چکیده مقاله :

میزان و نوع تخصیص منابع و اتخاذ استراتژیهای لازم برای تأمین مؤثر اقدامات اورژانسی با توجه به نقش حیاتی این مراکز در سیستم سلامت، و میزان توانایی این مراکز در پاسخ مؤثر به تماسهای اورژانسی یک عنصر مهم در تأمین و بازیابی سلامت بیماران محسوب میشود. بهینه‌ترین موقعیت مکانی اورژانسها با توجه به کنش متقابل بین عوامل، محدودیت‎های محیطی و ویژگیهای رفتاری متفاوت عوامل مختلف مفروض در مسئله را بیابیم. از لحاظ هدف کاربردی و توصیفی با رویکرد مدل‌سازی تبیینی که در ارائه مدل از نظرسنجی خبرگان استفاده و در اجرای مدل که مبتنی بر کاربست الگوریتم متاهیورستیکال (از نوع ژنتیک) میباشد، دادههای عینی و ذهنی ترکیب گردیده متغیرهای عامل و محیطی طی رویکرد ترکیبی شبیه‌سازی عامل بنیان و الگوریتم متاهیورستیک مدلیزه شده‌اند. زمان اولیه برای پیمایش یک ساختار اولیه برای ۴۰ نقطه حادثه‌خیز و ۵ ایستگاه برابر با 7860 بوده که پس از بهینه‌سازی به روش ژنتیک و تولید لیست جدید و نیز جهش آمبولانسها از یک ایستگاه به ایستگاه دیگر نتایج به عددی بین 2700 الی 4000 رسید. استفاده از این نوع بهینه‌سازی میتواند به‌سرعت فعالیتها و کاهش هزینه‌ها کمک شایانی نماید. با توجه به یکسان نبودن ترافیک نقاط، زمان رسیدن آمبولانس به نقاط حادثه‌خیز برابر نخواهد بود لذا با تغییر نقاط استقرار، بنا بر شرایط با جلو یا عقب رفتن و اختصاصی نمودن خصوصیات آمبولانس‌ها و نقاط حادثه‌خیز و ترکیب لیست نقاط میتوان زمان پیمایش کل نقاط حادثه‌خیز را کاهش داد.

چکیده انگلیسی:

The amount and type of resource allocation and adoption of necessary strategies to effectively provide emergency measures, given the vital role of these centers in the health system, and the ability of these centers to respond effectively to emergency calls is an important element in providing patients' health. Find the most optimal location of emergencies according to the interaction between factors, environmental constraints and different behavioral characteristics of different factors assumed in the problem. In terms of practical and descriptive purpose, with the explanatory modeling approach, which uses expert opinion polls in presenting the model and in implementing the model, which is based on the application of metaherstical algorithm. Combined objective and subjective data Factor and environmental variables are modeled through a combined approach of factor-based simulation and metaheuristic algorithm. The initial time to navigate an initial structure for 40 accident hotspots and 5 stations was 7860, which after genetic optimization and the production of a new list, as well as the jump of ambulances from one station to another, the results reached a number between 2700 and 4000. Using this type of optimization can help speed up activities and reduce costs. Due to the unequal traffic of the points, the time of arrival of the ambulance to the accident hotspots will not be equal, so by changing the deployment points, according to the conditions and specializing the characteristics of ambulances and accident hotspots and combining the list of points, the total travel time can be Reduced accident hotspo

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_||_

  1. Aringhieri, R., Bruni, M. E., Khodaparasti, S., & van Essen, J. (2017).  Emergency medical services and beyond: Addressing new challenges through a wide literature review.  Computers & Operations Research, 48 (1): 22-23.
    2.
  2. Bafandeh Zendeh, A., & Danaye Nemat Abad, N.  (2016). A Factor-based model for analyzing consumer preference for goods. Intrnational Conference on Industrial Engineering and Management, Tehran, Permanent Secretariat of the Conference, 2 (1): 38-47.
    3.
  3. Basar, A., Catay, B., &¨Unl¨uyurt, T. (2011).  A multi-period double coverage approach for locating the emergency medical service stations in Istanbul.  Journal of the Operational Research Society, 62 (12): 627–637.
    4.
  4. Batta, R., Dolan, J.  , & Krishnamurty, N. N.  (1998). The maximal expected covering location problem: Revisited.  Transportation Science, 23 (3): 277–287.
    5.
  5. Beraldi, P., Bruni, M.  , & Conforti, D.  (2004). Designing robust emergency medical service via stochastic programming.  European Journal of Operational Research, 158 (2): 183–193.
    6.
  6. Beraldi, P. , & Bruni, M.  (2009).  A probabilistic model applied to emergency service vehicle location. European Journal of Operational Research, 196 (2): 323–331.
    7.
  7. Cassco, (2001). Fuzzy thinking ،Mashhad Khajeh Nasir al-Din Tusi University, Press Second Edition, 17 (58), 310-317.
    8.
  8. Chanta, S., Mayorga, M.  , Kurz, M.  E., & McLay, L.  A. (2011).  The minimum p-envy locationproblem: a new model for equitable distribution of emergency resources.  IIE Transactions on Healthcare Systems Engineering, 1 (3): 101–115.
    9.
  9. Church, R.  , & ReVelle, C.  S. (1974).  The maximal covering location problem.  Papers of Regional Science Association, 32 (2): 101–118.
    10.
  10. Daskin, M.  , & Stern, E.  H. (1981).  A hierarchical objective set covering model for emergency medical service vehicle deployment.Transportation Science 15 (4): 137–152.
    11.
  11. Gendreau, M., Laporte, G., & Semet, F. (1997).  Solving an ambulance location model by tabu search. Location Science 5 (1): 75–88.
    12.
  12. J., Dietrich. R., Chen, J.  M., Mitwasi, M.  G., Valenzuela, T. , & Criss, E. (1990).  Validating and applying a model for locating emergency medical services in Tucson, AZ.  European Journal of Operational Research, 49 (2): 308–324.
    13.
  13. Hogan, K., & ReVelle, C.  (1986).  Concepts and application of backup coverage.  Management Science, 34 (3): 1434–1444.
    14.
  14. Litkoohi, S., Jahan Bakhsh, , & CHarkh CHian, M. (2014). Booklet of Location theories, Payame Noor University Press, 12 (71): 101-110.
    15.
  15. Macal, C., &   Sallach, M. (2010). North, eds., Chicago, IL, Oct.  7-9, available at, (pp.  185-204).
    16.
  16. Merrikh Bayat, F. (2014). Metaheuristic Optimization algorithms (with application in electrical engineering). Jihad Daneshgahi Publications, 67 (2): 91-98.
    17.
  17. McLay, L.  , & Mayorga, M.  E. (2013b).  A dispatching model for server-to-customer systems that balances efficiency and equity. Manufacturing & Service Operations Management, 15 (2): 205–220.
    18.
  18. McLay, L. A., & Mayorga, M. E. (2013c).  A model for optimally dispatching ambulances to emergency calls with classification errors in patient priorities.  IIE Transactions, 45 (2): 1–24.
    19.
  19. Marianov, V., & ReVelle, C.  (1994).  The queuing probabilistic location set covering problem and some extensions.  Socio-Economic Planning Sciences, 28 (1): 167–178.
    20.
  20. Marianov, V., & ReVelle, C.  (1995).  Siting emergency services.  In: Drezner, Z. (Ed.), Facility Location.  A survey of Applications and Methods.  Springer, New York, N. Y., (pp.  119–223).
    21.
  21. Marianov, V., & ReVelle, C.  (1996).  The queuing maximal availability location problem: A model for the siting of emergency vehicles.  European Journal of Operational Research, 93 (3): 110–120.
    22.
  22. Mason, A.  (2013).  Simulation and real-time optimised relocation for improving ambulance operations.  In: Denton, B. (Ed.), Handbook of Healthcare Operations: Methods and Applications.  Springer, New York, N. Y., (pp.  289–317).
    23.
  23. Nickel, S., Reuter-Oppermann, M., & da Gama, F.  (2016).  Ambulance location under stochastic demand: A sampling approach.  Operations Research for Health Care, 8 (2): 24–32.
    24.
  24. Rajagopalan, H.  , Saydam, C., & Xiao, J.  (2008). A multiperiod set covering location model for dynamic redeployment of ambulances.  Computers & Operations Research, 35 (4): 814–826.
    25.
  25. Repede, J.  , & Bernardo, J.  J. (1994).  Developping and validating a decision support system for location emergency medical vehicles in Louisville, Kentucky.  European Journal of Operational Research, 75 (2): 567–581.
    26.
  26. Reuter-Oppermann, M., van den Berg, P.  , & Vile, J.  L. (2017).  Logistics for emergency medical, Journal Health Systems, Volume 6, Issue 3, 187-208.
    27.
  27. ReVelle, C.  , & Hogan, K.  (1988). A reliability constrained siting model with local estimates of busy fractions.  Environment and Planning B, 15 (2): 143–152.
    28.
  28. ReVelle, C.  , & Hogan, K.  (1989). The maximum availability location problem.  Transportation Science 23, 192–200.
    29.
  29. Su, Q., Luo, Q., & Huang, H.  (2015). Cost-effective analyses for emergency medical services deployment: A case study in shanghai.  International Journal of Production Economics, 163 (12): 112–123.
    30.
  30. Tahan, M. (2015).  Emergency center Location model on city roads, Mashhad Ferdowsi University, 17 (54): 112-119.
    31.
  31. Toregas, C., Swain, R., ReVelle, C.  , & Bergman, L. (1971).  The location of emergency service facilities.  Operations Research, 19 (12): 1363–1373.
    32.
  32. Zhang, Z. H., & Li, K.  (2015). A novel probabilistic formulation for locating and sizing emergency medical service stations.  Annals of Operations Research, 229 (6): 813–835
    33.

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