IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v20y2020i4d10.1007_s10660-018-9315-x.html
   My bibliography  Save this article

Allocating resources for a restaurant that serves regular and group-buying customers

Author

Listed:
  • Tianhua Zhang

    (Beijing Jiaotong University)

  • Juliang Zhang

    (Beijing Jiaotong University)

  • Fu Zhao

    (Purdue University
    Purdue University)

  • Yihong Ru

    (Beijing Jiaotong University)

  • John W. Sutherland

    (Purdue University)

Abstract

Restaurants in China have recently begun offering group-buying (GB) options on the internet as a marketing and advertising tool to attract customers. GB coupon is available to parties of any size and the groups do not need to go together to the restaurants. Such restaurants serve two types of customers: GB customers with GB coupons and regular customers without coupons. One common practice for these restaurants is to set a maximum number of tables for each type of customer in advance and ask customers to wait in separate queues when all the tables for that customer type are occupied. As a result, restaurants are interested in finding optimal table allocation to serve the two types of customers to maximize profits. Since customers come follow a stochastic and discrete process, eat on a table that can be regarded as being served by a server in a queueing system, and wait in the queue when the restaurant is full, the dining process of customers and operating process of the restaurant is suitable to be described by queueing system. So, this study applies queueing theory to examine the table allocation problem. The effects of customer related parameters such as arrival rate and patience degree on the optimal allocation are discussed. The simulation model is extended to consider customers arriving in parties of different and serving tables of different sizes. We find that for a specific type of customer, if the arrival rate increases, the number of tables allocated for them increases. Patience degree has opposite influences on table allocation for the two types of customers: if regular customers are not patient, more tables should be allocated to them; while if GB customers are not patient, less tables should be allocated to them. If considering different customer party sizes and table sizes, as the arrival rate of regular customer increases, number of GB table decreases, number of tables for large (small) regular party first decreases (increases) and then increases (decreases).

Suggested Citation

  • Tianhua Zhang & Juliang Zhang & Fu Zhao & Yihong Ru & John W. Sutherland, 2020. "Allocating resources for a restaurant that serves regular and group-buying customers," Electronic Commerce Research, Springer, vol. 20(4), pages 883-913, December.
  • Handle: RePEc:spr:elcore:v:20:y:2020:i:4:d:10.1007_s10660-018-9315-x
    DOI: 10.1007/s10660-018-9315-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-018-9315-x
    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/s10660-018-9315-x?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. Sahut, Jean-Michel & Hikkerova, Lubica & Pupion, Pierre-Charles, 2016. "Perceived unfairness of prices resulting from yield management practices in hotels," Journal of Business Research, Elsevier, vol. 69(11), pages 4901-4906.
    2. Jean-Michel Sahut & Lubica Hikkerova & Pierre-Charles Pupion, 2016. "Perceived unfairness of prices resulting from yield management practices in hotels," Post-Print hal-02614853, HAL.
    3. Jean-Michel Sahut & Lubica Hikkerova & Sandra Camus, 2014. "Perceived unfairness of prices resulting from yield management practices," Working Papers 2014-166, Department of Research, Ipag Business School.
    4. Krishnan S. Anand & Ravi Aron, 2003. "Group Buying on the Web: A Comparison of Price-Discovery Mechanisms," Management Science, INFORMS, vol. 49(11), pages 1546-1562, November.
    5. Tong Che & Zeyu Peng & Zhongsheng Hua, 2016. "Characteristics of online group-buying website and consumers intention to revisit: the moderating effects of visit channels," Electronic Commerce Research, Springer, vol. 16(2), pages 171-188, June.
    6. Yina Lu & Andrés Musalem & Marcelo Olivares & Ariel Schilkrut, 2013. "Measuring the Effect of Queues on Customer Purchases," Management Science, INFORMS, vol. 59(8), pages 1743-1763, August.
    7. Colin E. Bell, 1980. "Optimal Operation of an M / M /2 Queue with Removable Servers," Operations Research, INFORMS, vol. 28(5), pages 1189-1204, October.
    8. Marco Alderighi & Marcella Nicolini & Claudio A. Piga, 2015. "Combined Effects of Capacity and Time on Fares: Insights from the Yield Management of a Low-Cost Airline," The Review of Economics and Statistics, MIT Press, vol. 97(4), pages 900-915, October.
    9. Xiaoqing Jing & Jinhong Xie, 2011. "Group Buying: A New Mechanism for Selling Through Social Interactions," Management Science, INFORMS, vol. 57(8), pages 1354-1372, August.
    10. Huang, Shui-Mu & Su, Jack C.P., 2013. "Impact of product proliferation on the reverse supply chain," Omega, Elsevier, vol. 41(3), pages 626-639.
    11. Stolletz, Raik & Manitz, Michael, 2013. "The impact of a waiting-time threshold in overflow systems with impatient customers," Omega, Elsevier, vol. 41(2), pages 280-286.
    12. Ben Vinod, 2016. "Evolution of yield management in travel," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 203-211, July.
    13. Dimitris Bertsimas & Romy Shioda, 2003. "Restaurant Revenue Management," Operations Research, INFORMS, vol. 51(3), pages 472-486, June.
    14. Shaler Stidham, 1970. "On the Optimality of Single-Server Queuing Systems," Operations Research, INFORMS, vol. 18(4), pages 708-732, August.
    15. U. Narayan Bhat & S. Subba Rao, 1972. "A Statistical Technique for the Control of Traffic Intensity in the Queuing Systems M / G /1 and GI / M /1," Operations Research, INFORMS, vol. 20(5), pages 955-966, October.
    16. René Caldentey & Lawrence M. Wein, 2003. "Analysis of a Decentralized Production-Inventory System," Manufacturing & Service Operations Management, INFORMS, vol. 5(1), pages 1-17, November.
    17. Frederick S. Hillier, 1963. "Economic Models for Industrial Waiting Line Problems," Management Science, INFORMS, vol. 10(1), pages 119-130, October.
    18. Wang, Jeff Jianfeng & Zhao, Xin & Li, Julie Juan, 2013. "Group Buying: A Strategic Form of Consumer Collective," Journal of Retailing, Elsevier, vol. 89(3), pages 338-351.
    19. Knight, V.A. & Harper, P.R. & Smith, L., 2012. "Ambulance allocation for maximal survival with heterogeneous outcome measures," Omega, Elsevier, vol. 40(6), pages 918-926.
    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. Qijun Qiu & Ying-Ju Chen & Benjamin P.-C. Yen, 2018. "The Implications of Group-Buying as a Channel Option Under Capacity Constraint," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(04), pages 1-32, August.
    2. Tommy Cheung & Wai Hung Wong & Ricky S. Wong & Jia Zhu, 2016. "Does Online Group Buying Benefit or Destroy Retail Businesses?," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 15(1), pages 1-16, June.
    3. Sun, Shuxing & Zhang, Bin, 2022. "Operation strategies for nanostore in community group buying," Omega, Elsevier, vol. 110(C).
    4. Tong Che & Zeyu Peng & Zhongsheng Hua, 2016. "Characteristics of online group-buying website and consumers intention to revisit: the moderating effects of visit channels," Electronic Commerce Research, Springer, vol. 16(2), pages 171-188, June.
    5. Zhang, Guoquan & Shang, Jennifer & Yildirim, Pinar, 2016. "Optimal pricing for group buying with network effects," Omega, Elsevier, vol. 63(C), pages 69-82.
    6. Yi Cui & Jian Mou & Yanping Liu, 2018. "Knowledge mapping of social commerce research: a visual analysis using CiteSpace," Electronic Commerce Research, Springer, vol. 18(4), pages 837-868, December.
    7. Li, Jianfei & Li, Bei & Shen, Yang & Tang, Kun, 2022. "Study on the steady state of the propagation model of consumers’ perceived service quality in the community group-buying," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    8. Bangwool Han & Minho Kim, 2019. "Hofstede’s Collectivistic Values and Sustainable Growth of Online Group Buying," Sustainability, MDPI, vol. 11(4), pages 1-15, February.
    9. Longyuan Du & Ming Hu & Jiahua Wu, 2022. "Contingent stimulus in crowdfunding," Production and Operations Management, Production and Operations Management Society, vol. 31(9), pages 3543-3558, September.
    10. Bi, Gongbing & Geng, Botao & Liu, Lindong, 2019. "On the fixed and flexible funding mechanisms in reward-based crowdfunding," European Journal of Operational Research, Elsevier, vol. 279(1), pages 168-183.
    11. Clauzel, Amélie & Guichard, Nathalie & Riché, Caroline, 2019. "Dining alone or together? The effect of group size on the service customer experience," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 222-228.
    12. Yuecheng Yu & Alexander Pelaez & Karl R. Lang, 2016. "Designing and evaluating business process models: an experimental approach," Information Systems and e-Business Management, Springer, vol. 14(4), pages 767-789, November.
    13. Simone Marinesi & Karan Girotra & Serguei Netessine, 2018. "The Operational Advantages of Threshold Discounting Offers," Management Science, INFORMS, vol. 64(6), pages 2690-2708, June.
    14. Oksana Loginova & Andrea Mantovani, 2019. "Price competition in the presence of a web aggregator," Journal of Economics, Springer, vol. 126(1), pages 43-73, January.
    15. Jiahua Wu & Mengze Shi & Ming Hu, 2015. "Threshold Effects in Online Group Buying," Management Science, INFORMS, vol. 61(9), pages 2025-2040, September.
    16. O. Loginova & A. Mantovani, 2015. "Information and Online Reviews," Working Papers wp996, Dipartimento Scienze Economiche, Universita' di Bologna.
    17. Shuqair, Saleh & Costa Pinto, Diego & Cruz-Jesus, Frederico & Mattila, Anna S. & da Fonseca Guerreiro, Patricia & Kam Fung So, Kevin, 2022. "Can customer relationships backfire? How relationship norms shape moral obligation in cancelation behavior," Journal of Business Research, Elsevier, vol. 151(C), pages 463-472.
    18. Rong Zhang & Bin Liu, 2017. "Group buying decisions of competing retailers with emergency procurement," Annals of Operations Research, Springer, vol. 257(1), pages 317-333, October.
    19. Simović, Olivera & Rađenović, Žarko & Perović, Djurdjica & Vujačić, Vesna, 2020. "Tourism in the Digital Age: E-booking Perspective," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, pages 616-627, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    20. Zhuoxin Li & Jason A. Duan, 2014. "Dynamic Strategies for Successful Online Crowdfunding," Working Papers 14-09, NET Institute.

    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:elcore:v:20:y:2020:i:4:d:10.1007_s10660-018-9315-x. 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.