Article
BILLBOARD ADVERTISING MODELING BY USING NETWORK COUNT LOCATION PROBLEM
DOI: 10.7708/ijtte.2014.4(2).02
4 / 2 / 146-160 Pages
Author(s)
Hamid Reza Lashgarian Azad - Industrial Engineering Department, Isfahan University of Technology, Isfahan, Iran -
Abstract
Many applications in engineering and science rely on the optimization of computationally cost functions. A successful approach in such states is to couple an evolutionary algorithm with a mathematical model which replaces the cost function. By considering of importance of advertising in the business declaration, especially billboard advertising, in this paper we have formulated billboard selection decision making, by using network count location approach which determine informative links in a network, to optimize cost of advertising and billboard visiting. Then, opposition based colonial competitive algorithm, which originally inspired by imperialistic competition, is used to solve mathematical model. Also, we implement proposed model on Sioux Falls city network.
Number of downloads: 4651
Keywords:
optimization;
billboard advertising;
opposition based colonial competitive algorithm;
network count location problem;
References:
Atashpaz-Gargari, E.; Lucas, C. 2007. Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition, IEEE Congress on Evolutionary Computation, 4661-4667.
Azad, H.R.L. 2014. An Application of Opposition Based Colonial Competitive Algorithm to Solve Network Count Location Problem, International Journal of Intelligent Systems and Applications. DOI: http://dx.doi.org/10.5815/ijisa.2014.01.04, 01: 29-35.
Azad, H.R.L.; Boushehri, N.S.; Mollaverdi, N. 2012. Investigating the application of opposition concept to colonial competitive algorithm, International Journal of Bio-Inspired Computation. DOI: http://dx.doi.org/10.1504/IJBIC.2012.049897, 4(5): 319-329.
Berman, O.; Hodgson, M.J.; Krass, D. 1995. Flow-interception problems, Facility location. In: Z. Drezner (Ed) A survey of applications and methods, New York: Springer-Verlag. 389-426.
Berman, O.; Larson, R.C.; Fouska, N. 1992. Optimal location of discretionary service facilities, Transportation Science, 26(3): 201-211.
Ehlert, A.; Bell, M.G.H.; Grosso, S. 2006. The optimisation of traffic count locations in road networks, Transportation Research Part B: Methodological. DOI: http://dx.doi.org/10.1016/j.trb.2005.06.001, 40(6): 460-479.
Entrepreneur. 2011. Small Business Encyclopedia: Advertising. Retrieved November 20, 2011.
Fei, X.; Eisenman, S.M.; Mahmassani, H.S. 2007. Sensor coverage and location for real-time prediction in large-scale networks. In Proceedings of the 86th TRB Annual meeting, January, Washington D.C.
Fogel, D.B. 1997. The advantages of evolutionary computation. In Proceedings of the Biocomputing and emergent computation, 1-11.
Hodgson, M.J. 1990. A flow-capturing location-allocation model, Geographical Analysis. DOI: http://dx.doi.org/10.1111/j.1538-4632.1990.tb00210.x, 22(3): 270-279.
Merriam-Webster Dictionary. 2011. Retrieved October 30, 2011.
Murty, K.G. 2003. Optimization Models for Decision Making, Junior Level Web-Book.
O’Barr, W.M. 2010. A Brief History of Advertising in America, Advertising & Society Review. DOI: http://dx.doi.org/10.1353/asr.0.0046, 11(1).
Rahnamayan, S. 2007. Opposition-based differential evolution. PhD thesis, Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada.
Savitz, J.; Shipp, R. 2009. Optimizing Advertising Budgets, Savitz Research Solutions.
Taylor, C.R.; Franke, G.R.; Bang, H. 2006. Use and Effectiveness of Billboards: Perspectives from Selective-Perception Theory and Retail-Gravity Models, Journal of Advertising. DOI: http://dx.doi.org/10.2753/JOA0091-3367350402, 35(4): 21-34.
Taylor, J.R. 1978. How to start and succeed in a business of your own, Indiana University. 293 p.
Teodorović, D.; Šelmić, M. 2013. Locating flow-capturing facilities in transportation networks: a fuzzy sets theory approach, International Journal for Transport and Traffic Engineering. DOI: http://dx.doi.org/10.7708/ijtte.2013.3(2).01, 3(2): 103-111.
Tizhoosh, H.R. 2005. Opposition-based learning: a new scheme for machine intelligence. In Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automation, Vienna, Austria, 695-701.
Wu, T.H.; Lin, J.N. 2003. Solving the competitive discretionary service facility location problem, European Journal of Operational Research. DOI: http://dx.doi.org/10.1016/S0377-2217(01)00391-5, 144(2): 366-378.
Yang, H.; Zhou, J. 1998. Optimal Traffic Counting locations for Origin-destination matrix estimation, Transportation Research Part B: Methodological. DOI: http://dx.doi.org/10.1016/S0191-2615(97)00016-7, 32(2): 109-126.
Yang, J.; Zhang, M.; He, B.; Yang, C. 2009. Bi-level programming model and hybrid genetic algorithm for flow interception problem with customer choice, Computers & Mathematics with Applications. DOI: http://dx.doi.org/10.1016/j.camwa.2008.10.035, 57(11-12): 1985-1994.
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