Optimal Localization of Shopping Centers Using Metaheuristic Genetic Algorithm
Subject Areas : Journal of Physical & Theoretical Chemistry
Mahmoud
Samadi
1
(Department of Management Science, Abhar Branch, Islamic Azad University, Abhar, Iran.)
Mahmoud
Nouraei
2
(Department of Management Science, Abhar Branch, Islamic Azad University, Abhar, Iran.)
Mohammad Mahdi
Mozaffari
3
(Faculty of Social Science, Imam Khomeini International University, Qazvin, Iran.)
Babak
Haji Karimi
4
(Department of Management Science, Abhar Branch, Islamic Azad University, Abhar, Iran.)
Keywords: Genetic Algorithm, Localization, shopping centers,
Abstract :
Efficiency and effectiveness is of importance for selection and localization. There should be regular methodology for targeting in the market by several methods. There is a necessity to have clear study for selection. In the current research, it has been studied the optimal localization at shopping centers. If there is not accuracy and validity, there will be achieved negative results for these centers such as high costs. Nowadays, these centers have turned into a part of consumer life. Today, they have penetrated consumers behavior and impacted on marketing mix. We can understand the importance of them from real shopping to window -shopping. As a meta-heuristic algorithm that inspired by natural systems, genetic algorithm has been used for problem salving as a mathematic model. The nature of genetic algorithm, which has created a relationship between humanity science and mathematics, is the reason for using it in the research. Given developed indices, Selected Iranian cities were selected for this study. Findings of the research showed that we can achieve accurate results with metaheuristic methods. The research is an applied research in terms of purpose, which is to develop applied knowledge in a certain field.
Al Ameri, A., Nichita, C., Riouch, T., & El-Bachtiri, R. (2015, July). Genetic algorithm for optimal sizing and location of multiple distributed generations in electrical network. In Modern Electric Power Systems (MEPS), 2015 (pp. 1-7). IEEE
Beasley, D., R. Bull, D., Ralph, M. (1993). An Overview of Genetic Algorithms, Inter University of Committee on Computing (Part1 and 2).
Benis, B.C. (2004). Reseatch Methods: A Tool For life, Boston: Pearson.
Desoer, C. & Vidyasagar, M. (1975)." Feedback System: Input-Output Properties" . Academic Press. New York.
Ehrgott, M., Wiecek, M. (2004). Multi Objective Programming, Department of Engineering Science, Chapter 17,667- 722.
El Hedhli, K., Jean, Chebat, C., Joseph, M. (2013). Shopping well-being at the mall: Construct, antecedents,and consequences. Journal of Business Research 66, 856–863.
El-Adly, M. (2006). Shopping centers attractiveness:asegmentation approach. International Journal of Retail & Distribution Management. 35(11).pp: 936-950.
Fleming, P. J.& Purshous, R. C. (2002) “Evolutionary algorithms in control systems engineering; a survey”. Control Engineering Practice Lo, pp. 1223-1241, 2002
Genetic Algorithm”. 14th Annual (International) Conference on Mechanical Engineering- May (2006) Isfahan University of Technology.
Goldberg, D,E (1989). “Genetic Algorithms in Search, Optimization, and Machine Learning” Addison,Wesley .
Karras, J. (2015). The 10 Traits of VIBRANT Cities, Urban scale. Available from: http://urbanscale.com/ebook/ (Accessed 17 March 2017)
Khare, A. (2011). Mall shopping behaviour of Indian small town consumers. Journal of Retailing and Consumer Services. 18. pp: 110-117.
Kotler, P. Keller, K. (2006). "Marketing Management", 12th edition, Prentice Hall.
Lee Taylor, S., & Cosenza, R. M. (2002). Profiling later aged female teens: mall shopping behavior and clothing choice. Journal of consumer marketing, 19(5), 393-408.
Michon, R., & Chebat, J. C. (2004). Cross-cultural mall shopping values and habitats: A comparison between English-and French-speaking Canadians. Journal of Business Research, 57(8), 883-892.
Okoli, c., & Pawwlowski, S.D.(2004) "The Delphi Method as a Research Tool: An Example, Design Considerations and Applications" , Information & Management , 42(1).
Porter, B. & Jones, A. H. (1989).“Genetic tuning of digital PID controllers”. Electric Letters. 28(9). pp. 843-844. 1992.
Ursem, R. (2003).“Models for evolutionary algorithms and their application on system identification and control optimization”. PhD Dissertation, Department of Computer Science University of Aarhus, Denmark, 2003.
Arimoto, S. & Miyazaki, F. (1985). "Stability and Robustness of PID Feedback Control for Robot Manipulators of Sensory Capability" . Third International Symposium of Research ,Gouvieux .France.
Sadeh, farhad. (2013). Driving Factors and Effectiveness of Sales Promotionin Shopping centers in Iran. Proceedings of 6th International Business and Social Science Research Conference. Novotel Hotel World Trade Centre,Dubai, UAE.
Samadi, M. (2009). Optimal sliding mode control for 2R robot using genetic algorithm, Master thesis , Islamic Azad University Takestan Branch
Schiffman, G. leon, Kanuk, Leslie, Lazar,(2002) "Consumer Behavior" 4thEd, Prentice-Hall, Interna-tional, Inc.
Talebi, R. (2010). Optimum Setting of Parking Places in Tehran City; Case Study: Seven Area of Tehran, Urban Management, 26, 119-132.
Tendai, M. & Crispen, C. (2009). In-store shopping environment and impulsive buying, African Journal of Marketing Management, 1(4): 102-108
Wesley, S., LeHew, M. & Woodside, G. (2006). Consumer decision-making styles and mall shopping behavior: Building theory using exploratory data analysis and the comparative method, Journal of Business Research, 59(5), May 2006, Pages 535-548
Wesley, S., LeHew, M., & Woodside, A. G. (2006). Consumer decision-making styles and mall shopping behavior: Building theory using exploratory data analysis and the comparative method. Journal of Business Research, 59(5), 535-548.
Zadeh, N., Alasti, A., Jamali, A. & Hajiloo . A. (2006). Multipurpose design of robust controllers