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A Multi-Objective Location-Allocation Model for Preventive Healthcare Systems with Probabilistic Demand | ||
| نشریه پژوهش های مهندسی صنایع در سیستم های تولید | ||
| Volume 8, Issue 16, September 2020, Pages 15-37 PDF (1.13 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22084/ier.2020.19662.1877 | ||
| Authors | ||
| Fatemeh Hajisoltani1; mehdi Seifbarghy* 2 | ||
| 1Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran | ||
| 2Department of Industrial Engineering, Alzahra University | ||
| Abstract | ||
| Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. The level of participation in preventive health care programs is a critical factor in terms of their effectiveness and efficiency of these programs. This article presents a methodology for locating preventive health care facilities (PHCFL) in order to increase the accessibility to potential clients and thus maximize participation in preventive healthcare programs. Due to capacity constraints in preventive health care facilities and the importance of waiting time in queues, we assume that each facility acts as M/M/ / queuing system. We present an Integer nonlinear programming (INLP) model in order to maximize the participation rate and minimize the cost of building facilities and allocating medical equipments to open facilities. Because of the importance of other objective functions, supplements have been introduced to bring about equitable access to preventive health care centers and to balance the hours of servant unemployment.The model is known as NP-Hard models, hence we propose two meta-heuristics algorithms to solve this problem with multi-objective functions. Meta-heuristics algorithms are consists of: non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. In order to increase the speed and performance of the combinatorial optimization algorithms, new coding is used in these solution algorithms. Finally, we used Taguchi method to tune the parameters of two algorithms and test problems with different size were generated and analyzed. According to the results, non-dominated sorting genetic algorithm is better. | ||
| Keywords | ||
| Location-allocation; Preventive Healthcare; Non-linear integer programming; models; queuing system; Genetic algorithm | ||
| References | ||
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[1] Davari, S., Kilic, K., Naderi, S. (2016). “A heuristic approach to solve the preventive health care problem with budget and congestion constraints”, Applied Mathematics and Computation, 276:442-453.
[2] Walker, K. (1977). “Current issues in the provision of health care services”, Journal of Consumer Affairs, 11: 52-62.
[3] Zhang., Berman, oded., verter, vedat. (2009). “Incorporating congestion in preventive healthcare facility network design”, European Journal of Operational Research, 922-935.
[4] Davari, S. (2017). The Incremental Cooperative Design of Preventive Healthcare Networks, University of Hertfordshire, Hertfordshire Business School United Kingdom.
[5] Institute of Medicine. (1993). Access to Health Care in America, National Academy Press, Washington, DC.
[6] Baron, O., Berman, O., Krass, D. (2008). “Facility location with stochastic demand and constraints on waiting time”, Manufacturing Service Oper, Management, 10: 484-505.
[7] Kembe E. S., Onah S., Iorkegh. (2012). “A Study of Waiting and Service Costs of a MultiServer Queuing Model in a Specialist Hospital”, International Journal of Scientific Technology Research, Vol (1), Issue (8), 19-23.
[8] Weber, A. (1909). Uber den Standort der Industrien,1. Teil: Reine Theorie des Standorts. Verlag J.C.B. Mohr, Tübingen. [9] Owen, S.H., Daskin, M.S. (1998). “Strategic facility location: A review”, European Journal of Operational Research, 111: 423-447.
[10] Wesolowsky, G.O. (1973). “Dynamic facility location”, Management Science, 19 (11): 1241-1248.
[11] Farahani, R.Z., Hekmatfar, M. (2009), Facilities location: Concepts, models, algorithms and case studies. Heidelberg: Springer-Verlag.
[12] Adan, I.J., Adam, B.F., Boxma, O.J., Resing, J.A.C. (2001). “Queueing models with multiple waiting lines”, Queueing Systems, 37: 65-98.
[13] Chebat, J.C., Filiatrault, P. (1993). “The impact of waiting in line on consumers”, International Journal of Bank Marketing, 11: 35-40.
[14] Berman, O. (1995). “The maximizing market size facility location problem with congestion”, Socio-Economic Planning Science, 29: 39-46.
[15] Wang, Q., Batta, R., Rump, C. M. (2002). “Algorithms for a facility location problem with stochastic customer demand and immobile servers”, Annals of Operations Research, 111: 17-34.
[16] Berman, O. (2007). “Locating capacitated facilities to maximize captured demand”, Taylor and Francis, 15: 1015-1029.
[17] Huff, DL. (1963). Determination of intra-urban retail trade areas, Real Estate Research Program, UCLA
[18] Krohn, R., Müller, S., Haase, K. (2016). Preventive health care facility location planning with qualityconscious clients, Working paper.
[19] Marianov, V., Rios, M., Icaza, M.J. (2008). “Facility location for market capturewhen users rank facilities by shorter travel andwaiting times”, European Journal of Operational Research, 191: 32-44.
[20] Zarrinpoor, N., Siefbarghy, M., (2011). A competitive location model to obtain a specific market share while ranking facilities by shorter travel time, The International Journal of Advanced Manufacturing Technology.
[21] صفّاری، درّین، آقایی، عبدالله، روغنیان، عماد. (1397). "ارائه مدل مکانیابی- تخصیص چند سطحی در چارچوب شبکههای صف"، نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید، سال ششم شماره 12: 49-61. [22] Verter, V., Lapierre, S.D. (2002). “Location of preventive health care facilities”, Annals of Operations Rsearch, 110: 123-132.
[23] Daskin, M.S., Dean, L.K. (2004). Location of health care facilities. In: Brandeau, M.L., Sainfort, F., Pierskalla, W.P. (Eds.), Operations Research and Health Care: A Handbook of Methods and Applications. Kluwer’s International Series, 43-76.
[24] Javanmardi, S., Hosseininasab, H., Mostafaeipour, A. (2017). “An exact Method for Stochastic Maximal Covering Problem of Preventive Healthcare Facilities”, Journal of Industrial and Systems Engineering,10: 10-23
[25] Seifbarghy, M., Roshan, K. (2012). “A Bi-Objective Preventive Healthcare Facility Network Design with Incorporating Cost and Time Saving, World Academy of Science, Engineering and Technology”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering. 6)1(: 282-285.
[26] Roshan, K., Siefbarghy, M., Pishva, D. (2017). “Multi-objective Evolutionary Algorithms for a Preventive Healthcare Facility Network Design”, International Journal of Industrial Engineering and Production Research, 28(4): 403-427.
[27] Vidyarthi, N., Kuzgunkaya, O. (2014). The impact of directed choice on the design of preventive healthcare facility network under congestion, Springer Health Care Management science.
[28] Dogan, K., Karatas, M., Yakici, E. (2019). “A model for locating preventive health care facilities”. Central European Journal of Operations Research, 28: 1091-1121.
[29] Zhang, Y., Berman, O., Marcotte, P., Verter, V. (2010). “Abilevel model for preventive healthcare facility network design with congestion”, IIE Trans, 42: 865-880.
[30] Gu, Wei., Wang, Xin., McGregor, S. E. (2010). “Optimization of preventive health care facility locations”, International journal of health geographics, 9(17): 1-16.
[31] Zhang, Yue., Berman, Oded., Verter, Vedat. (2012). “The impact of client choice on preventive healthcare facility network design”, Springer OR Spectrum, 349-370.
[32] Davari, S., Kilic, K., Ertek, G. (2015). “Fuzzy bi-objective preventive health care network design”, Health Care Management Science, 18(3): 303-317.
[33] Aboolian, R., Berman, O., Verter, V. (2015). “Maximal accessibility network design in the public sector”, TranspSci, 50(1): 336-347.
[34] اردستانی سنسنی، آیدا. (1393). ارائه مدل دو هدفه برای مکانیابی-تخصیص سیستم های مراقبت سلامت پیشگیرانه با درنظرگرفتن هزینه و مشارکت، پایاننامه کارشناسیارشد، دانشگاه الزهرا (س). [35] حاجیسلطانی، فاطمه.، باقرینژاد، جعفر. (1396). تجزیه و تحلیل و ارائهی پیشنهاداتی برای بهبود سیستم صف در یک مرکز سونوگرافی، اولین کنفرانس ملی اندیشههای نوین در مدیریت کسب و کار، تهران. [36] Gunes, E.D., Chick, S.E., Zeynep, A.O. (2004). “Breast cancer screening services: Trade-offs in quality, capacity, outreach, and centralization”, Health Care Management Science, 7: 291-303.
[37] مدرس یزدی، محمد. (1371). نظریه صف، انتشارات مرکز نشر دانشگاهی، ایران. [38] گراس، دونالد. هریس،کارل. (1388). مبانی و اصول نظریه صف، دکتر سید محمدتقی فاطمی قمی (مترجم)، انتشارات دانشگاه امام حسین. [39] Glover, F. (1986). “Future paths for integer programming and links to artificial intelligence”, Computers & Operations Research, 13: 533-549.
[40] Blum, C., Roli, A. (2003). “Metaheuristics in combinatorial optimization: Overview and conceptual comparison”, ACM Comput, Surv,35(3): 268-308.
[41] Goldberg, D.E. (1989). Genetic Algorithm in search, Optimization and Machine Learning, Reading, MA; Addison Wesley.
[42] Srinivas, N., Deb, K. (1994). “Multi-objective function optimization using non dominated sorting genetic algorithms”, Evolutionary computation Journal, 2 (3): 221-248.
[43] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, 6(2): 182-197.
[44] Deb, K., Agrawal, S., Pratap, A., Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proceedings of the parallel problem solving from nature VI 9PPSN-VI Conference, 849-858.
[45] Al Jadaan, O., Rao, C.R., Rajamani, L. (2008). “Non-Dominated ranked genetic algorithm for solving Multi-Objective optimization problems: NRGA”. Journal of Theoretical and Applied Information Technology, 60-67.
[46] حسیننژادی، مصطفی، ایرجپور، علیرضا. (1396). "ارائهی مدل ترکیبی چند هدفه دوسطحی برای مساله مدیریت موجودی یک فروشنده و چند خردهفروش با استفاده از دو الگوریتم فراابتکاری چندهدفه مبتنی بر پارتو"، مجله مدیریت توسعه و تحول، شماره 29: 63-74. [47] Rahmati, S.H.A., Hajipour, V., Niaki, S.T.A. (2013). “A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem”, Applied Soft Computing, 13(4): 1728-1740.
[48] نجفی، امیرعباس.، ارجمند، مسعود. (1395). "ارائهی سه الگوریتم فراابتکاری توسعهیافته بهمنظور حل مسالهی هزینهی دسترسپذیری منابع با اهداف کمینهسازی زمان اتمام پروژه و مجموع هزینههای منابع بهصورت همزمان"، نشریهی تخصصی مهندسی صنایع، 50(3): 471-482. [49] Schaffer, J.D. (1985). “Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, in Proceedings of the 1st International Conference on Genetic lgorithms”, L. Erlbaum ssociates Incorporated, 93-100.
[50] Zitzler, E., Thiele, L. (1999). Multi objective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE transactions on Evolutionary Computation.
[51] Zitzler, E., Deb, K., Thiele, L. (2000). “Comparison of multiobjective evolutionary algorithms: Empirical results”, Evolutionary computation, 8(2): 173-195. | ||
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