DESIGNING A VACCINE COLD CHAIN NETWORK IN NORTHERN THAILAND USING THE MAX-MIN ANT SYSTEM
Keywords:
Metaheuristic approaches, max-min ant system, vaccine cold chain, full factorial designAbstract
Metaheuristic approaches have become one of the most popular methods for big data of the optimization problem. This paper applies a nature inspired algorithm called the max-min ant system (MMAS) to design a vaccine cold chain network in northern Thailand. The research scope is focused on the Office of Disease Prevention & Control Area 1. This area office transports vaccines to provincial health offices and to hospitals within its geographical area of responsibility. This research aimed to rearrange routes to minimize the distances involved. The optimal parameters of the MMAS employ statistical experiment theory, namely a full factorial design. Statistical tools to design the experiment and for the analysis were adopted to investigate the factors affecting the performance of this algorithm.
References
Alatas, B. (2011). ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst. Appl., 38(10):13,170-13,180.
Dorigo, M. and Gambardella, L.M. (1997). Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE T. Evolut. Comput., 1(1):53-66.
Dorigo, M., Maziezzo, V., and Colorni, A. (1996). The ant system: optimization by a colony of cooperating agents. IEEE T. Syst. Man Cy. B, 26(1):29-41.
Dorigo, M. and Stutzle, T. (2004). Ant Colony Optimization. Bradford Books, MIT Press, Cambridge, MA, USA, 305p.
Formato, R.A. (2007). Central force optimization: a new metaheuristic with applications in applied electro-magnetics. Prog. Electromagn. Res., 77:425-491.
Goldberg, D.E. (1989). Genetic Algorithm in Search, Optimization and Machine Learning. 1st ed. Addison-Wesley Longman Publishing, Reading, MA, USA, 372p.
Holland, J.H. (1992). Adaptation in Natural and Artificial Systems. 2nd ed. University of Michigan Press, Ann Arbor, MI, USA, 211p.
Kennedy, J. and Eberhart, R.C. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks; November 27- December 1, 1995; Perth, WA, Australia, p. 1,942-1,948.
Lee, K.S. and Geem, Z.W.(2005). A new meta-heuristic algorithm for continuous engineering optimization: Harmony search theory and practice. Comput. Method. Appl. M., 194(36):3,902-3,933.
Montgomery, D.C. (2001). Design and Analysis of Experiments. 5th ed. John Wiley and Sons, Hoboken, NJ, USA, 684p.
Stutzle, T. and Hoos, H.H. (1997). The max-min ant system and local search for traveling salesman problem. Proceedings of the IEEE International Conference on Evolutionary Computation; April 13-16, 1997; Indianapolis, IN, USA, p. 309-314.
Stutzle, T. and Hoos, H.H. (2000). Max-min ant system. Future Gener. Comp. Sy., 16(8):889-914.
Sujaree, K. (2017). Blood vehicle routing network using artificial chemical reaction optimization algorithm. Proceedings of the 2017 Technology Innovation Management and Engineering Science International Conference; November 20-21, 2017; Nakhon Phathom, Thailand, p. 189-195.
Sujaree, K. (2018). Vaccine cold chain network problem using hybrid central force optimization. Academic J. Chulachomklao Royal Military Academy, 16:63-76.
Thachathawat, S. (2012). Vaccines and cold chain system, Available from: https://scm.gpo.or.th/vmi/ document/Flu-NHSO/07.ppt. Accessed date: Jan 16, 2018.
World Health Organization. (2015).The vaccine cold chain. Available from: http://www.who.int/ immunization/documents/IIP2015Module2.pdf. Accessed date: Jan 14, 2018.
Yamane, T. (1973). Statistics: an Introductory Analysis. 3rd ed. Harper and Row, New York, NY, USA, 1,130p.
Yang, X-S. and Deb, S. (2009). Cuckoo search via Lévy flights. Proceedings of the World Congress on Nature Biologically Inspired Computing; December 9-11, 2009; Coimbatore, India, p. 210–214.
