Metaheuristics approaches to solve the employee bus routing problem with clustering-based bus stop selection

Sinem Büyüksaatçi Kiriş*, Tuncay Özcan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)

Abstract

Vehicle routing problem (VRP) is a complex problem in the Operations Research topic. School bus routing (SBR) is one of the application areas of VRP. It is also possible to examine the employee bus routing problem in the direction of SBR problem. This chapter presents a case study for capacitated employee bus routing problem with data taken from a retail company in Turkey. A mathematical model was developed based on minimizing the total bus route distance. The number and location of bus stops were determined using k-means and fuzzy c-means clustering algorithms. LINGO optimization software was utilized to solve the mathematical model. Then, due to NP-Hard nature of the bus routing problem, simulated annealing (SA) and genetic algorithm (GA)-based approaches were proposed to solve the real-world problem. Finally, the performances of the proposed approaches were evaluated by comparing with classical heuristics such as saving algorithm and nearest neighbor algorithm. The numerical results showed that the proposed GA-based approach with k-means performed better than other approaches.

Original languageEnglish
Title of host publicationArtificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering
PublisherIGI Global
Pages217-239
Number of pages23
ISBN (Electronic)9781799803034
ISBN (Print)9781799803010
DOIs
Publication statusPublished - 29 Nov 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 by IGI Global. All rights reserved.

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