An Application of the Differential Evolution Algorithm for Solving the Problem of Size Selection and Location of Infectious Waste Incinerators for Community Hospitals in the Upper Part of Northeast Thailand
Keywords:
Location problem, Differential evolutionAbstract
This research was to solve the problem of choosing the size and location of infectious waste incinerators and construct vehicle routes for the waste collection at 109 community hospitals in the upper part of northeast Thailand (Kalasin, Khon Kaen, Bueng Kan, Mahasarakham, Loei, Sakon Nakhon, Nong Khai, Nong Bua Lam Phu, and Udon Thani provinces). The method of evolution using difference (Differential Evolution: DE) was employed to address the problem. The objective was to minimize the total system costs comprising the transportation infectious waste cost and the fixed costs of operation combined with the cost of operating the incinerator.DE developed by the researcher produces a vector that differs from the conventional method in that it generates vector 3 sets to find answers. The result was 569,562.66 baht, duration 154.44 seconds, with a better answer from using the Lingo program, representing 13.36%. The Lingo program to find answers takes 1 hour 30 minutes 51 seconds, the answer is 657,402.66 baht.
References
Bureau of Environmental Health. Situation of infectious waste management in 2020. Thailand: Ministry of public health; 2021.
Chummuel C, Pianthong N, Seabsiri K. Investigation of Community Hospitals Infectious Waste Disposal in Upper Northeastern Region. UBU Engineering Journal. 2009;2(1):26-34.
Sriburum A, Sindhuchao S. Solving the problem of the selection of the size and location of Infectious Waste Incinerators for community hospitals in the upper part of Northeast Thailand. Thai Journal of Operation Research. 2013;1(2):51-59.
Sresanpila P, Sindhuchao S. A Heuristic for Solving the Location and Incinerator Selection Problem: A Case Study of Elimination of Infectious Waste of Community Hospitals in the Upper Northeastern Part of Thailand. UBU Engineering Journal. 2016;11(2):13-24.
Krittika K. Size and location selection of incinerators and vehicle routing for infectious waste collection of community hospitals in the upper part of northeast Thailand[MEng thesis]. Ubon Ratchathani: Ubon Ratchathani University; 2017 Thai.
Owen S, Daskin M. Strategic facility location: A review. European Journal of Operational Research. 1998;111(3):423-447.
Hakimi S. Optimum locations of switching centers and the absolute centers and medians of a graph. Operations Research. 1964;12(3):450–459.
Storn R, Price K. Differential Evolution–a simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization. 1997;11:341-359.
Qin A, Huang V, Suganthan P. Differential evolution algorithm with strategy adaptation for global numerical optimization. Evolutionary Computation. 2009;13(2):398-417.
Chiang C, Lee W, Heh J. A 2-Opt based differential evolution for global Optimization. Applied Soft Computing. 2010;10(4):1200-1207.
Pitakaso R, Parawech P, Jirasirierd G. Comparisons of Different Mutation and Recombination Processes of the DEA for SALB-1. Proceedings of the Institute of Industrial Engineers Asian Conference; 2013 July 18-20; Taipei, Taiwan. Springer Singapore; 2013.
Zhu W, Tang Y, Fang J, Zhang W. Adaptive population tuning scheme for differential evolution. Information Sciences. 2013;223:164-191.
Sethanan K, Pitakaso R. Improved differential evolution algorithms for solving generalized assignment problem. Expert Systems with Applications. 2016;45:450-459.
Sedki A, Quazar D. Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems. Advanced Engineering Informatics. 2012;26(3):582-591.
Epitropakis M, Plagianakos V, Vrahatis M. Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution: A hybrid approach. Information Sciences. 2012;216: 50-92.
Miranda V, Alves R. Differential Evolution Particle Swam Optimization (DEEPSO): a successful hybrid. The 11th Brazilian Congress on Computational Intelligence; 2013 Sep 8-13; Ipojuca, Brazil. IEEE; 2014.
Thongdee T, Pitakaso R. Differential Evolution Algorithms Solving a Multi-Objective, Source and Stage Location-Allocation Problem. Industrial Engineering and Management Systems. 2015;14(1): 11-21.
Olenšek J, Tuma T, Puhan J, Burmen Á. A new asynchronous parallel global optimization method based on simulated annealing and differential evolution. Applied Soft Computing. 2011;11(1): 1481-1489.
Zhao X, Lin W, Yu C, Chen J, Wang S. A new hybrid differential evolution with simulated annealing and self-adaptive immune operation. Computer & Mathematics with Applications. 2013;66(10): 1948-1960.
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