A new bi-objective model of the urban public transportation hub network design under uncertainty

Firoozeh Kaveh, Reza Tavakkoli-Moghaddam, Chefi Triki*, Yaser Rahimi, Amin Jamili

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


This paper presents a new bi-objective multi-modal hub location problem with multiple assignment and capacity considerations for the design of an urban public transportation network under uncertainty. Because of the high construction costs of hub links in an urban public transportation network, it is not economic to create a complete hub network. Moreover, the demand is assumed to be dependent on the utility proposed by each hub. Thus, the elasticity of the demand is considered in this paper. The presented model also has the ability to compute the number of each type of transportation vehicles between every two hubs. The objectives of this model are to maximize the benefits of transportation by establishing hub facilities and to minimize the total transportation time. Since exact values of some parameters are not known in advance, a fuzzy multi-objective programming based approach is proposed to optimally solve small-sized problems. For medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective particle swarm optimization is applied and its performance is compared with results from the non-dominated sorting genetic algorithm. Our experimental results demonstrated the validity of our developed model and approaches. Moreover, an intensive sensitivity analyze study is carried out on a real-case application related to the monorail project of the holy city of Qom.

Original languageEnglish
Pages (from-to)131-162
Number of pages32
JournalAnnals of Operations Research
Issue number1-2
Publication statusPublished - Jan 2021
Externally publishedYes


  • Capacitated hub location problem
  • Elastic demand
  • Fuzzy multi-objective programming
  • Meta-heuristics
  • Urban transportation

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research


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