A genetic algorithm for solving bus terminal location problem using data envelopment analysis with multi-objective programming

Atefeh Taghavi; Reza Ghanbari; Khatere Ghorbani-moghadam; Alireza Davoodi; Ali Emrouznejad

doi:10.1007/s10479-021-04244-4

A genetic algorithm for solving bus terminal location problem using data envelopment analysis with multi-objective programming

Atefeh Taghavi, Reza Ghanbari, Khatere Ghorbani-moghadam, Alireza Davoodi^*, Ali Emrouznejad

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Due to the urban expansion and population increasing, bus network design is an important problem in the public transportation. Functional aspect of bus networks such as the fuel consumption and depreciation of buses and also spatial aspects of bus networks such as station and terminal locations or access rate to the buses are not proper conditions in most cities. Therefore, having an efficient method to evaluate the performance of bus lines by considering both functional and spatial aspects is essential. In this paper, we propose a new model for the bus terminal location problem using data envelopment analysis with multi-objective programming approach. In this model, we want to find efficient allocation patterns for assigning stations terminals, and also we investigate the optimal locations for deploying terminals. Hence, we use a genetic algorithm for solving our model. By using the simultaneous combination of data envelopment analysis and bus terminal location problem, two types of efficiencies are optimized: Spatial efficiency as measured by finding allocation patterns with the most serving amount and the terminals’ efficiency in serving demands as measured by the data envelopment analysis efficiency score for selected allocation patterns. This approach is useful when terminals’ efficiency is one of the important criteria in choosing the optimal terminals location for decision-makers.

Original language	English
Pages (from-to)	259-276
Number of pages	18
Journal	Annals of Operations Research
Volume	309
Issue number	1
Early online date	29 Aug 2021
DOIs	https://doi.org/10.1007/s10479-021-04244-4
Publication status	Published - Feb 2022

Bibliographical note

© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use [https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms], but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10479-021-04244-4

Funding: The authors thank the research council of Ferdowsi University of Mashhad and Optimization Laboratory of Ferdowsi University of Mashhad for supporting this work.

Keywords

Bus terminal location problem
Data envelopment analysis
Efficiency
Genetic algorithm
Multi objective programming

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10.1007/s10479-021-04244-4

Genetic Algorithm for Solving Bus Terminal Location Problem
© Springer Nature B.V. 2021. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-021-04244-4.
Accepted author manuscript, 335 KB

Cite this

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abstract = "Due to the urban expansion and population increasing, bus network design is an important problem in the public transportation. Functional aspect of bus networks such as the fuel consumption and depreciation of buses and also spatial aspects of bus networks such as station and terminal locations or access rate to the buses are not proper conditions in most cities. Therefore, having an efficient method to evaluate the performance of bus lines by considering both functional and spatial aspects is essential. In this paper, we propose a new model for the bus terminal location problem using data envelopment analysis with multi-objective programming approach. In this model, we want to find efficient allocation patterns for assigning stations terminals, and also we investigate the optimal locations for deploying terminals. Hence, we use a genetic algorithm for solving our model. By using the simultaneous combination of data envelopment analysis and bus terminal location problem, two types of efficiencies are optimized: Spatial efficiency as measured by finding allocation patterns with the most serving amount and the terminals{\textquoteright} efficiency in serving demands as measured by the data envelopment analysis efficiency score for selected allocation patterns. This approach is useful when terminals{\textquoteright} efficiency is one of the important criteria in choosing the optimal terminals location for decision-makers.",

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A genetic algorithm for solving bus terminal location problem using data envelopment analysis with multi-objective programming

Abstract

Bibliographical note

Keywords

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