Campus Units

Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

9-17-2020

Journal or Book Title

Fuzzy Sets and Systems

Research Focus Area(s)

​Operations Research

DOI

10.1016/j.fss.2020.09.007

Abstract

As a critical component of sustainable development, a transportation system should be designed such that it has a positive impact on the economic, environmental, and social sustainability of the served region. In response, this study introduces the concept of passenger dissatisfaction with additional walking and waiting as an indicator of social sustainability and uses the concept while optimizing the transit network for economic, environmental, and social perfectives. Due to a lack of knowledge about the actual value of different passenger dissatisfaction levels and uncertainty in demand, a multi-objective robust possibilistic programming approach (RPP⁎) is proposed and solved by using an interactive fuzzy programming approach. Different from other robust possibilistic approaches, RPP⁎ optimizes not only the mean of the objective function and chance constraint violations but also the risk value inherited by uncertain parameters through considering the absolute deviation of the objective function. Both the advantage of RPP⁎ versus the deterministic model and its superiority against several robust possibilistic approaches are demonstrated in the numerical studies. Furthermore, the outcomes of the numerical study demonstrate that the transportation network should be designed in a decentralized way as the risk coefficients, i.e., risk-taking attitude, increase.

Comments

This is a manuscript of an article published as Günay, Elif Elçin, Gül E. Okudan Kremer, and Atousa Zarindast. "A Multi-objective Robust Possibilistic Programming Approach to Sustainable Public Transportation Network Design." Fuzzy Sets and Systems (2020). DOI: 10.1016/j.fss.2020.09.007. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

Elsevier B.V.

Language

en

File Format

application/pdf

Available for download on Saturday, September 17, 2022

Published Version

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