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 () and Ali Emrouznejad ()
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Atefeh Taghavi: Ferdowsi University of Mashhad
Reza Ghanbari: Ferdowsi University of Mashhad
Khatere Ghorbani-Moghadam: Ferdowsi University of Mashhad
Alireza Davoodi: Islamic Azad University
Ali Emrouznejad: Aston University

Annals of Operations Research, 2022, vol. 309, issue 1, No 12, 259-276

Abstract: 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.

Keywords: Bus terminal location problem; Data envelopment analysis; Efficiency; Genetic algorithm; Multi objective programming (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-021-04244-4

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