IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v10y2019i2d10.1007_s13198-018-0723-z.html
   My bibliography  Save this article

Bi-level decision making models for advertising allocation problem under fuzzy environment

Author

Listed:
  • Syed Mohd Muneeb

    (Aligarh Muslim University)

  • Ahmad Yusuf Adhami

    (Aligarh Muslim University)

  • Zainab Asim

    (Aligarh Muslim University)

  • Syed Aqib Jalil

    (Aligarh Muslim University)

Abstract

This paper presents bi-level decision making models for advertising planning problem. Advertising planning process consists of multiple objectives and is generally decentralised involving various hierarchical levels of decision making. Considering the cost and impact related factors, long and short duration ads for a single product are made for telecasting. The models presented in the paper are designed so as to allocate the number of advertisements of each kind to different channels under different time zones of a day with the objectives of maximization of ads impact and minimization of net cost at two different levels. We present two models based on minimum impact value to be achieved by advertisement as a constraint considering that the budget available for advertising is uncertain. We extend and present a solution approach developed for fuzzy bi-level integer decision making model with fuzzy constraints. Finally, we provide a numerical illustration to discuss the applicability of the proposed models.

Suggested Citation

  • Syed Mohd Muneeb & Ahmad Yusuf Adhami & Zainab Asim & Syed Aqib Jalil, 2019. "Bi-level decision making models for advertising allocation problem under fuzzy environment," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(2), pages 160-172, April.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:2:d:10.1007_s13198-018-0723-z
    DOI: 10.1007/s13198-018-0723-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-018-0723-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-018-0723-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jeffrey H. Horen, 1980. "Scheduling of Network Television Programs," Management Science, INFORMS, vol. 26(4), pages 354-370, April.
    2. Wayne F. Bialas & Mark H. Karwan, 1984. "Two-Level Linear Programming," Management Science, INFORMS, vol. 30(8), pages 1004-1020, August.
    3. Sinha, Surabhi & Sinha, S. B., 2002. "KKT transformation approach for multi-objective multi-level linear programming problems," European Journal of Operational Research, Elsevier, vol. 143(1), pages 19-31, November.
    4. Remica Aggarwal & Udayan Chanda, 2017. "Optimal duration of advertising campaigns for successive technology generations using innovation diffusion theory," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 28(3), pages 415-428.
    5. Jan, Rong-Hong & Chern, Maw-Sheng, 1994. "Nonlinear integer bilevel programming," European Journal of Operational Research, Elsevier, vol. 72(3), pages 574-587, February.
    6. Tao Zhang & Tiesong Hu & Yue Zheng & Xuning Guo, 2012. "An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem," Journal of Applied Mathematics, Hindawi, vol. 2012, pages 1-13, April.
    7. Bhattacharya, U.K., 2009. "A chance constraints goal programming model for the advertising planning problem," European Journal of Operational Research, Elsevier, vol. 192(2), pages 382-395, January.
    8. Sankar Prasad Mondal, 2016. "Differential equation with interval valued fuzzy number and its applications," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(3), pages 370-386, September.
    9. Ahmad Yusuf Adhami & Syed Mohd Muneeb & Mohammad Asim Nomani, 2017. "A multilevel decision making model for the supplier selection problem in a fuzzy situation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(4), pages 5-26.
    10. Rik Pieters & Luk Warlop & Michel Wedel, 2002. "Breaking Through the Clutter: Benefits of Advertisement Originality and Familiarity for Brand Attention and Memory," Management Science, INFORMS, vol. 48(6), pages 765-781, June.
    11. Beltran-Royo, C. & Escudero, L.F. & Zhang, H., 2016. "Multiperiod Multiproduct Advertising Budgeting: Stochastic Optimization Modeling," Omega, Elsevier, vol. 59(PA), pages 26-39.
    12. Cinzia Colapinto & Davide La Torre, 2015. "Media planning and preferences: a fuzzy goal programming model," International Journal of Multicriteria Decision Making, Inderscience Enterprises Ltd, vol. 5(3), pages 167-181.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pérez-Gladish, B. & Gonzalez, I. & Bilbao-Terol, A. & Arenas-Parra, M., 2010. "Planning a TV advertising campaign: A crisp multiobjective programming model from fuzzy basic data," Omega, Elsevier, vol. 38(1-2), pages 84-94, February.
    2. Syed Aqib Jalil & Shakeel Javaid & Syed Mohd Muneeb, 2018. "A decentralized multi-level decision making model for solid transportation problem with uncertainty," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 1022-1033, October.
    3. Chi-Bin Cheng & Hsu-Shih Shih & Boris Chen, 2017. "Subsidy rate decisions for the printer recycling industry by bi-level optimization techniques," Operational Research, Springer, vol. 17(3), pages 901-919, October.
    4. Rik Pieters & Michel Wedel, 2012. "Ad Gist: Ad Communication in a Single Eye Fixation," Marketing Science, INFORMS, vol. 31(1), pages 59-73, January.
    5. Pieters, Rik & Wedel, Michel, 2020. "Heads up: Head movements during ad exposure respond to consumer goals and predict brand memory," Journal of Business Research, Elsevier, vol. 111(C), pages 281-289.
    6. C. Audet & G. Savard & W. Zghal, 2007. "New Branch-and-Cut Algorithm for Bilevel Linear Programming," Journal of Optimization Theory and Applications, Springer, vol. 134(2), pages 353-370, August.
    7. Cao, Dong & Chen, Mingyuan, 2006. "Capacitated plant selection in a decentralized manufacturing environment: A bilevel optimization approach," European Journal of Operational Research, Elsevier, vol. 169(1), pages 97-110, February.
    8. Sinha, Surabhi & Sinha, S. B., 2002. "KKT transformation approach for multi-objective multi-level linear programming problems," European Journal of Operational Research, Elsevier, vol. 143(1), pages 19-31, November.
    9. Xu, Jiuping & Song, Xiaoling & Wu, Yimin & Zeng, Ziqiang, 2015. "GIS-modelling based coal-fired power plant site identification and selection," Applied Energy, Elsevier, vol. 159(C), pages 520-539.
    10. Küçükaydin, Hande & Aras, Necati & Kuban AltInel, I., 2011. "Competitive facility location problem with attractiveness adjustment of the follower: A bilevel programming model and its solution," European Journal of Operational Research, Elsevier, vol. 208(3), pages 206-220, February.
    11. Kuo, R.J. & Lee, Y.H. & Zulvia, Ferani E. & Tien, F.C., 2015. "Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 1013-1026.
    12. Alicia L. Rihn & Chengyan Yue, 2016. "Visual Attention's Influence on Consumers’ Willingness‐to‐Pay for Processed Food Products," Agribusiness, John Wiley & Sons, Ltd., vol. 32(3), pages 314-328, July.
    13. Dariush Akbarian, 2020. "Overall profit Malmquist productivity index under data uncertainty," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-20, December.
    14. G. Hibino & M. Kainuma & Y. Matsuoka & T. Morita, 1996. "Two-level Mathematical Programming for Analyzing Subsidy Options to Reduce Greenhouse-Gas Emissions," Working Papers wp96129, International Institute for Applied Systems Analysis.
    15. Wen, U. P. & Huang, A. D., 1996. "A simple Tabu Search method to solve the mixed-integer linear bilevel programming problem," European Journal of Operational Research, Elsevier, vol. 88(3), pages 563-571, February.
    16. I. Nishizaki & M. Sakawa, 1999. "Stackelberg Solutions to Multiobjective Two-Level Linear Programming Problems," Journal of Optimization Theory and Applications, Springer, vol. 103(1), pages 161-182, October.
    17. Liu, Shaonan & Kong, Nan & Parikh, Pratik & Wang, Mingzheng, 2023. "Optimal trauma care network redesign with government subsidy: A bilevel integer programming approach," Omega, Elsevier, vol. 119(C).
    18. Muñoz, María M. & Abdelaziz, Fouad Ben, 2012. "Satisfactory solution concepts and their relations for Stochastic Multiobjective Programming problems," European Journal of Operational Research, Elsevier, vol. 220(2), pages 430-442.
    19. Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1215-1240, October.
    20. Jiemiao Chen & Xiaojing Yang & Robert E. Smith, 2016. "The effects of creativity on advertising wear-in and wear-out," Journal of the Academy of Marketing Science, Springer, vol. 44(3), pages 334-349, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:10:y:2019:i:2:d:10.1007_s13198-018-0723-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.