تعداد نشریات | 161 |
تعداد شمارهها | 6,498 |
تعداد مقالات | 70,233 |
تعداد مشاهده مقاله | 123,448,590 |
تعداد دریافت فایل اصل مقاله | 96,673,700 |
ارائه نوعی مدل تصمیم جدید در برنامهریزی تبلیغات اینترنتی با استفاده از الگوریتم ژنتیک چندهدفه | ||
مدیریت بازرگانی | ||
دوره 13، شماره 4، 1400، صفحه 1001-1016 اصل مقاله (1.28 M) | ||
نوع مقاله: مقاله علمی پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/jibm.2022.337574.4299 | ||
نویسندگان | ||
محمدعلی فرقانی* 1؛ محمدرضا نامدار2؛ سید عبدالمجید جلائی3 | ||
1دانشیار، گروه مدیریت بازرگانی، دانشکده مدیریت و اقتصاد، دانشگاه شهید باهنر کرمان، کرمان، ایران. | ||
2کارشناسی ارشد، گروه مدیریت بازرگانی، دانشکده مدیریت و اقتصاد، دانشگاه شهید باهنر کرمان، کرمان، ایران. | ||
3استاد، گروه مدیریت بازرگانی، دانشکده مدیریت و اقتصاد، دانشگاه شهید باهنر کرمان، کرمان، ایران. | ||
چکیده | ||
هدف: پژوهش حاضر با هدف ارائة یک مدل تصمیم در برنامهریزی تبلیغات اینترنتی با استفاده از الگوریتم ژنتیک چندهدفه اجرا شده است. روش: پژوهش حاضر از حیث روش و ماهیت، در دستهی تحقیقات توصیفی جای میگیرد. به لحاظ اجرا، پیمایشی و از نظر هدف، کاربردی میباشد. در این پژوهش از آنجا که مدل ارائه شده، یک مدل بهینهسازی چندهدفه با ابعاد بالاست، از الگوریتم بهینهسازی ژنتیک چندهدفه برای حل استفاده شده است. یافتهها: در این پژوهش بر خلاف پژوهشهای پیشین، با در نظر گرفتن همزمان اهداف متضاد متقاضیان ارائة تبلیغات از طریق وب (کاهش هزینههای تبلیغات) و مدیران وبسایت (افزایش سود به دست آمده از ارائة خدمات)، دربارة چگونگی بهینهسازی بهتر تخصیص منابع تبلیغات به وب سایت بحث شد و مدل تصمیم جدیدی ارائه گردید که دو هدف متضاد را دربرداشت. در واقع، این مدل چندهدفه نه تنها درآمد وبسایت را به حداکثر میرساند بلکه هزینة متقاضی ارائة تبلیغات را نیز کمتر میکند؛ بنابراین، مدل یادشده میتواند مبنای کار این دو قرار گیرد. نتیجهگیری: نتایج شبیهسازی نشان داد مدل بهینهسازی و الگوریتم موجه و شدنی هستند. همچنین، مجموعه جواب بهینه پارتوی به دست آمده از حل مدل میتواند موجب رضایت مدیران وبسایت و متقاضیان ارائة تبلیغات شود. آنها با استفاده از این مدل به تعامل و سازش دست زده و سعی می-کنند منافع طرف دیگر را نیز در نظر بگیرند. با توجه به اینکه با حل مدل پیشنهادی برخلاف سایر مدلها منافع هر دو طرف مد نظر قرار گرفته است، مجموعه جواب در زمره راهبرد برد برد قرار میگیرد. | ||
کلیدواژهها | ||
الگوریتم ژنتیک چندهدفه؛ بهینهسازی چندهدفه؛ تبلیغات از طریق وب؛ قیمتگذاری ترکیبی | ||
عنوان مقاله [English] | ||
Providing a New Decision Model in Internet Advertising Planning Using Non-dominated Sorting Genetic Algorithm II | ||
نویسندگان [English] | ||
Mohammadali Forghani1؛ Mohammadreza Namdar2؛ Sayed Abdolmajid Jalaee3 | ||
1Associate Prof. Faculty of Management and Economics, Shahid Bahonar University of Kerman, Iran. | ||
2MSc., Faculty of Management and Economics, Shahid Bahonar University of Kerman, Iran. | ||
3Prof., Faculty of Management and Economics, Shahid Bahonar University of Kerman, Iran. | ||
چکیده [English] | ||
Objective The present study aimed to provide a decision model in Internet advertising planning using multi-objective genetic algorithm. The proposed model is a model for distributing advertising resources through the web to optimize the effect of advertising, based on research literature and according to the characteristics of advertising through the web. This model can simultaneously consider the interests of network managers and advertisers. Methodology The present study is in the category of descriptive research in terms of method and nature and is a survey in terms of implementation and also applied in terms of purpose. In this research, since the proposed model is a multi-objective optimization model with high dimensions, the multi-objective genetic optimization algorithm has been used to solve it. Findings In this study, unlike previous studies, by simultaneously considering the conflicting goals of applicants for advertising through the web (reducing advertising costs) and webmasters (increasing profits from the provision of services), about How to better optimize the allocation of advertising resources to the website was discussed and a new decision model was presented that had two conflicting goals. In fact, this multi-objective model not only maximizes website revenue but also reduces the cost to the applicant of advertising; therefore, the mentioned model can be the basis of the work of these two. On the other hand, based on the characteristics of advertising through the web and existing pricing strategies, a hybrid pricing strategy was created based on the variables "cost per thousand views" and "cost per click in this research". Then, a new multi-objective optimization decision model based on this strategy was proposed. In this model, the interests of webmasters and advertisers are considered. Finally, by providing a computational example and numerical results of the simulation, the effectiveness of the model and algorithm is proved. Conclusion The simulation results showed that the optimization model and algorithm are justified and feasible. Also, the set of optimal Pareto answers obtained from solving the model can satisfy the webmasters and applicants for advertising. Using this model, they interact and compromise and try to consider the interests of another person. Considering that by solving the proposed model, unlike other models, the interests of both stakeholders have been considered, the answer set is included in the win-win strategy. Therefore, since the validation of this model is done through simulation, in practice, network administrators can when coding ads on web pages by applying the mathematical relationships provided in the proposed model, the method of calculating the cost of applicants for advertising is logical. And provide a list of possible suggestions to the applicant. In this list, different combinations of simultaneous decision variables at the desired level, by maximizing the income of network managers, minimize the costs of each applicant according to their opinion, which leads to the adoption of more efficient pricing strategies. | ||
کلیدواژهها [English] | ||
Multi-objective Optimization, Web Advertising, Hybrid Pricing, Non-dominated Sorting Genetic Algorithm II (NSGA-II) | ||
مراجع | ||
Abe, N. & Nakamura, A. (1999). Learning to optimally schedule internet banner advertisements. ICML 99 Proceedings of the Sixteenth Internationa conference on Machine Learning, June 27 - 30, PP. 12-21. Amiri, A. & Menon, S. (2003). Efficient scheduling of Internet banner advertisements. ACM Transactions on Internet Technology (TOIT), 3 (4), 334-346. Bae, J.K. & Kim, J. (2010). Integration of heterogeneous models to predict consumer behavior. Expert Systems with Applications, 37 (3), 1821-1826. Baltas, G., Tsafarakis, S., Saridakis, C. & Matsatsinis, N. (2013). Biologically Inspired Approaches to Strategic Service Design Optimal Service Diversification Through Evolutionary and Swarm Intelligence Models. Journal of Service Research, 16 (2), 186-201. Biethahn, J. & Nissen, V. (1994). Combinations of simulation and evolutionary algorithms in management science and economics. Annals of Operations Research, 52 (4), 181-208. Chiu, C. (2002). A case-based customer classification approach for direct marketing. Expert Systems with Applications, 22 (2), 163-168. Chiu, CY., Chen, YF., Kuo, IT. & Ku, HC. (2009). An intelligent market segmentation system using k-means and particle swarm optimization. Expert Systems with Applications, 36 (3), 4558-4565. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. Vol. 16, USA: John Wiley & Sons. Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation. IEEE Transactions on, 6 (2), 182-197. Díaz, J.C.Z., Cruz-Chavez, M.A. & Cruz-Rosales, M.H. (2010). Mathematical Multi-objective Model for the selection of a portfolio of investment in the Mexican Stock Market. AISS, 2 (2), 67-76. Fan, T.K. & Chang, C.H. (2010). Sentiment-oriented contextual advertising. Knowledge and Information Systems, 23 (3), 321-344. Giuffrida, G., Reforgiato, D., Tribulato, G. & Zarba, C. (2011). A banner recommendation system based on web navigation history. In Computational Intelligence and Data Mining (CIDM), IEEE. Gruca, T.S. & Klemz, B.R. (2003). Optimal new product positioning: A genetic algorithm approach. European Journal of Operational Research, 146 (3), 621-633. Hurley, S., Moutinho, L. & Stephens, N. (1995). Solving marketing optimization problems using genetic algorithms. European Journal of Marketing, 29 (4), 39-56. Internet Advertising Bureau (IAB). (2014). IAB/PwC Internet Ad Revenue Report. Jianguo, Z. & Liang, Z. (2011). Multi-objective model for uncertain portfolio optimization problems. International Journal of Advancements in Computing Technology, 3 (8), 138-145. Jonker, J.-J., Piersma, N. & Van den Poel, D. (2004). Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Systems with Applications, 27 (2), 159-168. Langheinrich, M., Nakamura, A., Abe, N., Kamba, T. & Koseki, Y. (1999). Unintrusive customization techniques for Web advertising. Computer Networks, 31 (11), 1259-1272. Liu, H.-H. & Ong, C.-S. (2008). Variable selection in clustering for marketing segmentation using genetic algorithms. Expert Systems with Applications, 34 (1), 502-510. Meng, L. (2008). The Research on Pricing Strategies and the Pricing Model of Portal Web Advertisements, Jilin University, China. Moutinho, L., Bigné, E. & Manrai, A.K. (2014). The Routledge Companion to the Future of Marketing. Taylor & Francis. Nakamura, A. & Abe, N. (2005). Improvements to the linear programming based scheduling of web advertisements. Electronic Commerce Research, 5 (1), 75-98. Nakamura, A. (2002). Improvements in practical aspects of optimally scheduling web advertising. In Proceedings of the 11th international conference on World Wide Web. ACM. Honolulu, Hawaii, USA - May 07 – 11. Nissen, V. (1995). An overview of evolutionary algorithms in management applications. In Evolutionary algorithms in management applications, Springer, PP. 44-97. Qi, J. & Wang, D.-W. (2004a). Fuzzy Decision Model for Launching Web Advertising on Relevant Network. Journal of Northeastern University (Natural Science), 25(9), 837-839. Qi, J. & Wang, D.-w. (2004b). Particle swarm optimization algorithm for a model of optimally scheduling web advertising resources. Control and Decision, 19, 881-884. Sohn, S.Y., Moon, T.H. & Seok, K.J. (2009). Optimal pricing for mobile manufacturers in competitive market using genetic algorithm. Expert Systems with Applications, 36 (2), 3448-3453. Tomlin, J.A. (2000). An entropy approach to unintrusive targeted advertising on the Web. Computer Networks, 33 (1), 767-774. Venkatesan, R. & Kumar, V. (2002). A genetic algorithms approach to growth phase forecasting of wireless subscribers. International Journal of Forecasting, 18 (4), 625-646. Wang, Q. (2009). Application of Multi-objective Particle Swarm Optimization Algorithm in Integrated Marketing Method Selection. In Advances in Neural Networks. DOI: 10.1007/978-3-642-01510-6-65. Wei, PL., Huang, JH., Tzeng, GH. & Wu, SI. (2010). Causal modeling of web-advertising effects by improving SEM based on DEMATEL technique. International Journal of Information Technology & Decision Making, 9 (05), 799-829. Yang, DD., Jiao, LC., Gong, MG. & Yu, H. (2010). Clone selection algorithm to solve preference multi-objective optimization. Journal of Software, 21 (1), 14-33. Yan-min, L. (2011). MOPSO Based on Dynamic Neighborhood and Evolutionary Programming. Advances in Information Sciences and Service Sciences, 3 (10), 115-123. Yu, B., Yang, Z. & Cheng, C. (2007). Optimizing the distribution of shopping centers with parallel genetic algorithm. Engineering Applications of Artificial Intelligence, 20 (2), 215-223. Zhang, Z. & Wang, L. (2006). A decision model of optimally scheduling web advertising resources. Journal of Anshan University of Science and Technology, 29 (5), 510-519. Zhiping, W., Shengbao, Z., Junfang, G. & Zhongtuo, W. (2007). Supernetwork model for resource allocation of network-advertisement based on variational inequality. Journal of Dalian Maritime University, 33 (4), 69-72. Zhou, X. X. & Sun, P.Z. (2010). The Psychological Effect of Internet Advertising and Its Theoretical Discussion [J]. Advances in Psychological Science, 18(5), 790-799. | ||
آمار تعداد مشاهده مقاله: 626 تعداد دریافت فایل اصل مقاله: 550 |