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Demand-driven scheduling of movies in a multiplex

Author

Listed:
  • Eliashberg, J.
  • Hegie, Q.
  • Ho, J.
  • Huisman, D.
  • Miller, S.J.
  • Swami, S.
  • Weinberg, C.B.
  • Wierenga, B.

Abstract

This paper describes a model that generates weekly movie schedules in a multiplex movie theater. A movie schedule specifies within each day of the week, on which screen(s) different movies will be played, and at which time(s). The model consists of two parts: (i) conditional forecasts of the number of visitors per show for any possible starting time; and (ii) an optimization procedure that quickly finds an almost optimal schedule (which can be demonstrated to be close to the optimal schedule). To generate this schedule we formulate the so-called movie scheduling problem as a generalized set partitioning problem. The latter is solved with an algorithm based on column generation techniques. We have applied this combined demand forecasting /schedule optimization procedure to a multiplex in Amsterdam where we supported the scheduling of fourteen movie weeks. The proposed model not only makes movie scheduling easier and less time consuming, but also generates schedules that would attract more visitors than the current ‘intuition-based’ schedules.

Suggested Citation

  • Eliashberg, J. & Hegie, Q. & Ho, J. & Huisman, D. & Miller, S.J. & Swami, S. & Weinberg, C.B. & Wierenga, B., 2007. "Demand-driven scheduling of movies in a multiplex," Econometric Institute Research Papers EI 2007-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:10093
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    References listed on IDEAS

    as
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    Cited by:

    1. Lewis, R. & Thompson, J., 2015. "Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem," European Journal of Operational Research, Elsevier, vol. 240(3), pages 637-648.
    2. Andrea Baldin & Trine Bille & Andrea Ellero & Daniela Favaretto, 2016. "Multiobjective optimization model for pricing and seat allocation problem in non profit performing arts organization," Working Papers 20, Department of Management, Università Ca' Foscari Venezia.
    3. Brinja Meiseberg & Thomas Ehrmann, 2013. "Diversity in teams and the success of cultural products," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 37(1), pages 61-86, February.
    4. Andrea Baldin & Trine Bille & Andrea Ellero & Daniela Favaretto, 2018. "Revenue and attendance simultaneous optimization in performing arts organizations," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 42(4), pages 677-700, November.

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    More about this item

    Keywords

    column generation; demand forecasting; integer programming; optimization of movie schedules;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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