Recent changes in the broadcasting industry and emerging digital media technologies have “disturbed” the traditional economic models supporting the media industry over the last decade, with viewers migrating from traditional media outlets to digital ones causing a severe drop in revenues. Consequently, the competition for viewers’ ratings has intensified dramatically over recent years, with new economic models being introduced and others still under development.
In this context, the research presented in this thesis describes in detail an innovative computer model for optimising television programming and scheduling to maximise revenues under given constraints. The research methodology combines academic work along with practitioners’ experiences to build an integer programming model that helps expert programme schedulers to place television programmes in time slots where they achieve optimum ratings within the limitations of the resources available.
In building the model, an extensive literature review and media industry experts’ interviews and focus groups discussions were conducted. The value of the model was demonstrated by applying it to a real case as well as hypothetical scenarios for a television station and showing that the model increased potential viewership, on average, between 38% and 63%.
The software package used to solve the model should enable the media industry to solve large scale optimisation models using thousands of variables and constraints. This should help media planners and decision makers to plan for months, if not years, ahead.