Evolutionary food quality and location strategies for restaurants in competitive online-to-offline food ordering and delivery markets: An agent-based approach

Zhou He, Guanghua Han, T. C.E. Cheng, Bo Fan, Jichang Dong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)


In the booming online-to-offline (O2O) food ordering and delivery market, numerous independent restaurants are competing for orders placed by customers via online food ordering platforms. The food quality and location decisions are deemed to be the two principal considerations of restaurants in this emerging market. To investigate the evolutionary food quality and location behaviours of restaurants, we propose an agent-based O2O food ordering model (AOFOM) that consists of three types of agents, namely customers, restaurants, and the online food ordering platform. We explicitly model their adaptive behaviours by optimizing the agents' benefits. We find that customers' behaviours have significant impacts on the restaurants' food quality decisions. Besides, the relationship between the restaurant's location decisions and customers' waiting time is less significant in the O2O food ordering market due to the presence of an equalizing delivery service provided by the online platform. We also examine the characters of best restaurants, as well as the impacts of different delivery policies on the food quality and location decisions of restaurants.

Original languageEnglish
Pages (from-to)61-72
Number of pages12
JournalInternational Journal of Production Economics
Publication statusPublished - Sep 2019


  • Agent-based model
  • Food delivery
  • Food ordering
  • Location
  • Online-to-offline

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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