مکانیابی مراکز بیمارستان با استفاده از الگوریتم بهینه سازی ازدحام ذرات ترکیبی مطالعه موردی: منطقه دو تهران

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری سیستم اطلاعات مکانی، دانشکده مهندسی ژئودزی و ژئوماتیک، دانشگاه صنعتی خواجه نصیرالدین طوسی

2 دانشیار گروه سیستم اطلاعات مکانی- دانشکده مهندسی ژئودزی و ژئوماتیک، دانشگاه صنعتی خواجه نصیرالدین طوسی

10.22131/sepehr.2019.37493

چکیده

وجود مراکز بهداشتی و بیمارستانها در تمام جوامع ضروری است و مکانیابی و تخصیص جمعیت به آنها یک مسئله بهینهسازی مهم در برنامهریزی شهری میباشد. هدف از این پژوهش، مقایسه و ارزیابی عملکرد الگوریتم ژنتیک و الگوریتم بهینهسازی ازدحام ذرات ترکیبی برای تعیین مکان بهینه مراکز بیمارستان و تخصیص نقاط جمعیتی به آنها میباشد. به منظور محدود کردن فضای جستجو، از قابلیتهای تجزیهوتحلیل سیستم اطلاعات جغرافیایی (GIS) به همراه تحلیل سلسله مراتبیبرای انتخاب سایتهای نامزد استفاده شده است. سپس الگوریتمهای نام برده برای تعیین شش مکان بهینه و تخصیص بلوکهای نظیر به آنها پیادهسازی شدهاند. در این تحقیق هدف به حداقل رساندن مجموع تمام فاصلههای بین مراکز بیمارستانی و بلوکهای جمعیتی میباشد که برای این منظور از توسعه الگوریتم بهینهسازی ازدحام ذرات با تعریف جستجوی همسایگی برای ذره نخبه، استفاده شده است. برای کالیبره کردن پارامترهای هر یک از الگوریتمها، مجموعهای از دادههای شبیهسازی منظم بهکار رفته است. با در دست داشتن مقادیر مناسب برای پارامترها، الگوریتمها بر روی دادههای واقعی از منطقه مطالعاتی مورد آزمایش قرار گرفتند. نتایج نشان داده است که الگوریتم بهینهسازی ازدحام ذرات ترکیبی دارای عملکرد بهتری نسبت به الگوریتم ژنتیک میباشد. روند همگرایی الگوریتمازدحام ذرات ترکیبی، سریعتر از الگوریتم ژنتیک میباشد. هر دو الگوریتم سطوح بالایی از تکرارپذیری را نشان دادهاند؛ اما الگوریتم بهینهسازی ازدحام ذرات ترکیبی دارای ثبات بیشتری است. همچنین برای هر دو نوع داده شبیهسازی و واقعی، الگوریتم بهینهسازی ازدحام ذرات ترکیبی سریعتر از الگوریتم ژنتیک عمل میکند. سادگی و تکرارپذیری الگوریتمها از عوامل مهمی میباشند که ازنقطهنظر کاربر بسیار مهم است. بنابراین با توجه به این معیارها، بهینهسازی ازدحام ذرات ترکیبی مطلوبتر از ژنتیک بوده است.

کلیدواژه‌ها


عنوان مقاله [English]

Hospital site selection using hybrid PSO algorithm - Case study: District 2 of Tehran

نویسندگان [English]

  • Mehrdad Kaveh 1
  • Mohammad Saadi Mesgari 2
1 Ph.D. Student ,GIS Division, Faculty of Geomatics, K. N. Toosi University of Technology
2 Associate Professor, GIS Division, Faculty of Geomatics, K. N. Toosi University of Technology
چکیده [English]

 Extended Abstract
Introduction
Site selection for health centers and hospitals in proper locations and the allocation of population to them is an important issue in urban planning. The location and allocation of health and medical facilities including hospitals, have long been an important issue for urban planners that has become more complicated with the growth of population. Location and allocation of hospitals is basically planned to ensure the availability of proper and comprehensive health services as well as the reduction of the establishment costs. Improper planning of the health centers has created multiple problems for big cities in developing countries in recent years. In the present study, the Genetic Algorithm (GA), Hybrid Particle Swarm Optimization algorithm (HPSO), Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP) have been used for selecting proper sites of hospital and allocating the demanded locations to these centers in District 2 of Tehran.
 
Materials & Methods
The main goal of this research is to compare and evaluate the performance of the Genetic Algorithm (GA) and Hybrid Particle Swarm Optimization algorithm (HPSO) for determining the optimal locations of hospital centers and allocating the population blocks to them. In order to limit the search space, the analyzing capabilities of the Geospatial Information System (GIS) and Analytic Hierarchy Process (AHP) have been used to select the candidate sites satisfying the initial conditions and criteria. The locations of such candidate centers are the input of the optimization section. The accuracy of the entire process strongly depends on the selection of these candidate sites. Hence, in this paper, the Analytic Hierarchy Process (AHP) method has been used to select the candidate centers. Then, two optimization algorithms were applied in choosing six optimum sites from the candidate locations and allocating the population to them through minimizing the overall distances between the centers and their allocated blocks. In this study, to improve the Particle Swarm Optimization, a simple neighborhood search has been proposed for better exploitation of the elite particles. The main purpose of this neighborhood search is to increase the convergence rate of the algorithm without decreasing the random search. Since the neighborhood search has a specific definition proportional to each issue, and the issues of location and allocation are spatial issues as well, therefore, the geographic principle of appropriate distribution of the centers in space has been used to define the neighborhood search (the distance between the centers should not be less than a certain amount). In an elite particle, two centers with the lowest distance are selected and one of them is replaced by a new and randomly selected center. If such a change provides a better objective function, the newly created solution in the elite particle is replaced. To calibrate the algorithms parameters, a simulated data set has been used. Having proper values for those parameters, the algorithms were tested on the real data of the study area.
 
Results & Discussion
Given the results of algorithms on real data, the performances of both algorithms are highly dependent on the initial population and the allowed number of iterations. In general, lower numbers of iterations and more populations brings better results than the higher iterations and lower populations. The results show that the Hybrid Particle Swarm Optimization (HPSO) has better performance than the Genetic Algorithm (GA). The convergence rate of the Hybrid Particle Swarm Optimization (HPSO) algorithm is faster than the genetic algorithm (GA), which can be attributed to the particle’s motion toward the best personal and global experiences. Furthermore, the proposed neighborhood search has caused the HPSO algorithm to converge earlier. To evaluate the repeatability of the algorithms, they were performed 40 times for both simulated and real data. Both algorithms have displayed high levels of repeatability, but the Hybrid Particle Swarm Optimization (HPSO) algorithm is more stable. However, the use of Genetic Algorithm (GA) on simulated data has shown more stability than its use on real data. For both the simulated data and real data, the Hybrid Particle Swarm Optimization (HPSO) algorithm performs faster than the Genetic Algorithm (GA). 
 
Conclusion
Simplicity and repeatability of the algorithm are among the important factors which are very significant from the user’s point of view. In this research, the HPSO algorithm has not only been repeatable and simple, but has performed faster than the GA. Therefore, considering these criteria, regarding the special case of this research, the HPSO seems to be more promising than the GA.

کلیدواژه‌ها [English]

  • Hospital site selection
  • genetic algorithm
  • Hybrid Particle Swarm Optimization (HPSO) algorithm
  • Spatial Information System (GIS)
  • Analytical Hierarchy Analysis
1 - موسوی میرکلائی, س. م., کاوه, م., خویشه, م., & آقابابایی, م. (2018). Design and Implementation a Sonar Data Set Classifier using Multi-Layer Perceptron Neural Network Trained by Elephant Herding Optimization. فصلنامه علمی-پژوهشی دریا فنون, 5(1), 1-12.‎

2 - نوع, ا., سخنرانی, ن., کاوه, م., طالعی, م., دانشکده, ن. ب., & دانشگاه, ص. خ. ن. ط. عنوان: پهنه بندی خطر زمین لغزش با استفاده از روش های FuzzyAHP، AHP و سناریوهای مختلف FuzzyOWA در راستای توسعه و امنیت شهری (مطالعه موردی: استان تهران).‎

3 - Arnaout, J. P. (2013). Ant colony optimization algorithm for the Euclidean location-allocation problem with unknown number of facilities. Journal of Intelligent Manufacturing, 24(1), 45.

4 - Cooper, L. (1963). Location-allocation problems. Operations research, 11(3), 331-343.

5 - ElKady, S. K., & Abdelsalam, H. M. (2016). A modified particle swarm optimization algorithm for solving capacitated maximal covering location problem in healthcare systems. In Applications of Intelligent Optimization in Biology and Medicine (pp. 117-133). Springer International Publishing.

6 - Ghaderi, A., Jabalameli, M. S., Barzinpour, F., & Rahmaniani, R. (2012). An efficient hybrid particle swarm optimization algorithm for solving the uncapacitated continuous location-allocation problem. Networks and Spatial Economics, 12(3), 421-439.

7 - Hassan, R., Cohanim, B., De Weck, O., & Venter, G. (2005, April). A comparison of particle swarm optimization and the genetic algorithm. In 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference (p. 1897).

8 - Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.

9 - Kaveh, M., & Mesgari, M. S. (2019). Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances. Computers & Industrial Engineering, 135, 800-813.

10 - Kaveh, M., Khishe, M., & Mosavi, M. R. (2019). Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network. Analog Integrated Circuits and Signal Processing, 100(2), 405-428.

11 - Khishe, M., Mosavi, M. R., & Kaveh, M. (2017). Improved migration models of biogeography-based optimization for sonar dataset classification by using neural network. Applied Acoustics, 118, 15-29.

12 - Li, X., & Parrott, L. (2016). An improved Genetic Algorithm for spatial optimization of multi-objective and multi-site land use allocation. Computers, Environment and Urban Systems, 59, 184-194.

13 - Mahar, F., Ali, S. S. A., & Bhutto, Z. (2012, March). A Comparative Study on Particle Swarm Optimization and Genetic Algorithms for Fixed Order Controller Design. In International Multi Topic Conference (pp. 284-294). Springer Berlin Heidelberg.

14 - Mosavi, M. R., Kaveh, M., & Khishe, M. (2016, March). Sonar Data Set Classification using MLP Neural Network Trained by Non-linear Migration Rates BBO. In The Fourth Iranian Conference on Engineering Electromagnetic (ICEEM 2016) (pp. 1-5).

15 - Mosavi, M. R., Kaveh, M., Khishe, M., & Aghababaee, M. (2016). Design and Implementation a Sonar Data Set Classifier by using MLP NN Trained by Improved Biogeography-based Optimization. In Proceedings of the Second National Conference on Marine Technology (pp. 1-6).

16 - Önüt, S., Efendigil, T., & Kara, S. S. (2010). A combined fuzzy MCDM approach for selecting shopping center site: An example from Istanbul, Turkey. Expert Systems with Applications, 37(3), 1973-1980.

17 - Ramli, L., Sam, Y. M., & Mohamed, Z. (2016, October). A Comparison of Particle Swarm Optimization and Genetic Algorithm Based on Multi-objective Approach for Optimal Composite Nonlinear Feedback Control of Vehicle Stability System. In Asian Simulation Conference (pp. 652-662). Springer Singapore.

18 - Saaty, T. L. (1980). The Analytic Hierarchy Process: New York, NY, McGraw Hill, reprinted by RWS Publication, Pittsburgh.

19 - Saeidian, B., Mesgari, M. S., & Ghodousi, M. (2016). Evaluation and comparison of Genetic Algorithm and Bees Algorithm for location–allocation of earthquake relief centers. International Journal of Disaster Risk Reduction, 15, 94-107.

20 - Senvar, O., Otay, I., & Bolturk, E. (2016). Hospital Site Selection via Hesitant Fuzzy TOPSIS. IFAC-PapersOnLine, 49(12), 1140-1145.

21 - Shariff, S. R., Moin, N. H., & Omar, M. (2012). Location allocation modeling for healthcare facility planning in Malaysia. Computers & Industrial Engineering, 62(4), 1000-1010.

22 - Steiner, M. T. A., Datta, D., Neto, P. J. S., Scarpin, C. T., & Figueira, J. R. (2015). Multi-objective optimization in partitioning the healthcare system of Parana State in Brazil. Omega, 52, 53-64.

23 - Vahidnia, M. H., Alesheikh, A. A., & Alimohammadi, A. (2009). Hospital site selection using fuzzy AHP and its derivatives. Journal of environmental management, 90(10), 3048-3056.

24 - Wong, T. C., & Ngan, S. C. (2013). A comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize makespan for assembly job shop. Applied Soft Computing, 13(3), 1391-1399.

25 - Zhang, W., Cao, K., Liu, S., & Huang, B. (2016). A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong. Computers, Environment and Urban Systems, 59, 220-230.

26 - Zheng, Y. J., & Ling, H. F. (2013). Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach. Soft Computing, 17(7), 1301-1314.