Random Fuzzy Cost-Profit Equilibrium Model for Locating a Discrete Service Enterprise

Hongfei Jia, Qiang Li, Guangdong Tian, Mengchu Zhou, Zhiwu Li

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


A transportation (automotive service) facility location problem is important in urban infrastructure planning and construction. To handle it, researchers have proposed a number of stochastic/random models for locating an automotive service enterprise. However, most of them fail to describe all kinds of uncertainty, e.g., data imprecision. By considering regional constraints, this work proposes a new random fuzzy cost-profit equilibrium model by using uncertainty data and management methods. It presents a hybrid algorithm integrating stochastic fuzzy simulation and particle swarm optimization to solve the location problem of an automobile service enterprise. In addition, since risk factors can impact a decision, this work conducts a risk performance analysis when locating an automotive service enterprise. A practical example is given to illustrate the proposed model and algorithm.

Original languageEnglish (US)
Pages (from-to)4387-4394
Number of pages8
JournalIEEE Access
StatePublished - Nov 23 2017

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering


  • Data management
  • discrete event systems
  • facility location allocation
  • modeling and simulation
  • optimization algorithm
  • particle swarm optimization
  • stochastic fuzzy simulation
  • uncertainty data


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