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Optimizing Commercial Scheduling on TV Channels: Mathematical Model and Metaheuristic Algorithms

Aghababaei, Homa | 2024

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57234 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Varmazyar, Mohsen
  7. Abstract:
  8. This thesis investigates the scheduling of television advertisements, which is critical for optimizing the efficiency and profitability of marketing campaigns for TV networks. Since its inception in the 1930s, television has been a cornerstone of advertising, enabling businesses to reach broad and diverse audiences. The evolution of TV advertising strategies, particularly with the rise of digital media competition, underscores the importance of effective advertisement scheduling to maintain relevance and effectiveness. To address this challenge, a comprehensive mixed-integer linear programming (MILP) model was developed. This model incorporates various hypotheses to manage real-world constraints such as advertiser-specified reach, budget constraints, frequency preferences, commercial compatibility, and varying prices of commercial positions within breaks. The model also integrates precise scheduling based on release times and due dates, aiming to optimize the allocation of TV commercial slots. By doing so, it seeks to maximize network profitability while simultaneously minimizing both the tardiness of commercials and the associated costs for TV networks. The complexity and NP-hard nature of the advertisement scheduling problem necessitated the use of advanced metaheuristic algorithms to find near-optimal solutions. The study employed algorithms such as Genetic Algorithm (GA), Tabu Search (TS), and Simulated Annealing (SA). These algorithms are designed to handle the multifaceted nature of the problem efficiently, providing robust tools for managing and optimizing advertising slots. The results of this study demonstrated the effectiveness of the proposed algorithms in solving the advertisement scheduling problem. Among the algorithms tested, the Genetic Algorithm (GA) achieved the highest performance, consistently obtaining first-rank solutions. This indicates its superior capability in handling the intricate balancing act required in TV advertisement scheduling. The Tabu Search and Simulated Annealing algorithms also performed well, providing valuable insights and alternative solutions. This research is noteworthy for its dual perspective, considering both the needs of advertisers and TV networks. By balancing these often competing priorities, the study ensures that advertisements are delivered in a manner that maximizes their impact and value while also optimizing network operations. In conclusion, the integration of the MILP model with advanced metaheuristic algorithms like GA, TS, and SA presents a significant advancement in the field of advertisement scheduling. This approach not only enhances the operational efficiency of TV networks but also ensures that advertisements are delivered in a manner that maximizes their impact and value. The findings from this research are poised to make a substantial contribution to the field, offering practical solutions that can be implemented to improve the scheduling and effectiveness of television advertisements
  9. Keywords:
  10. Television Advertising ; Scheduling ; Mixed Integer Linear Programming ; Genetic Algorithm ; Tabu Search Algorithm ; Simulated Annealing Method ; Profit Maximization

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