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Strategic and operational decisions in restaurant revenue management

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  • Guerriero, Francesca
  • Miglionico, Giovanna
  • Olivito, Filomena

Abstract

The paper addresses restaurant revenue management from both a strategic and an operational point of view. Strategic decisions in restaurants are mainly related to defining the most profitable combination of tables that will constitute the restaurant. We propose new formulations of the so-called “Tables Mix Problem” by taking into account several features of the real setting. We compare the proposed models in a computational study showing that restaurants, with the capacity of managing tables as renewable resources and of combining different-sized tables, can improve expected revenue performances. Operational decisions are mainly concerned with the more profitable assignment of tables to customers. Indeed, the “Parties Mix Problem” consists of deciding on accepting or denying a booking request from different groups of customers, with the aim of maximizing the total expected revenue. A dynamic formulation of the “Parties Mix Problem” is presented together with a linear programming approximation, whose solutions can be used to define capacity control policies based on booking limits and bid prices. Computational results compare the proposed policies and show that they lead to higher revenues than the traditional strategies used to support decision makers.

Suggested Citation

  • Guerriero, Francesca & Miglionico, Giovanna & Olivito, Filomena, 2014. "Strategic and operational decisions in restaurant revenue management," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1119-1132.
    • Handle: RePEc:eee:ejores:v:237:y:2014:i:3:p:1119-1132
    DOI: 10.1016/j.ejor.2014.02.048
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    References listed on IDEAS

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    Cited by:

    1. Sierag, D.D. & Koole, G.M. & van der Mei, R.D. & van der Rest, J.I. & Zwart, B., 2015. "Revenue management under customer choice behaviour with cancellations and overbooking," European Journal of Operational Research, Elsevier, vol. 246(1), pages 170-185.
    2. Milad HajMirzaei & Koorush Ziarati & Alireza Nikseresht, 2022. "A customer type discovery algorithm in hotel revenue management systems," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 200-211, April.
    3. Mohit Tyagi & Nomesh B. Bolia, 2022. "Approaches for restaurant revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(1), pages 17-35, February.
    4. Byeongmo Kang & Seung Ho Yoo, 2024. "Sustainable Operations of Online and Offline Restaurants: Focusing on Multi-Brand Restaurants," Sustainability, MDPI, vol. 16(21), pages 1-20, November.

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