Please use this identifier to cite or link to this item:
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty
Author(s): Shen, Yingdong
Xie, Wenliang
Li, Jingpeng
Issue Date: 2021
Date Deposited: 1-Sep-2021
Citation: Shen Y, Xie W & Li J (2021) A MultiObjective Optimization Approach for Integrated Timetabling and Vehicle Scheduling with Uncertainty. Journal of Advanced Transportation, 2021, Art. No.: 3529984.
Abstract: The timetabling problem (TTP) and vehicle scheduling problem (VSP) are two indispensable problems in public transit planning process. They used to be solved in sequence; hence, optimality of resulting solutions is compromised. To get better results, some integrated approaches emerge to solve the TTP and VSP as an integrated problem. In the existing integrated approaches, the passenger comfort on bus and the uncertainty in the real world are rarely considered. To provide better service for passengers and enhance the robustness of the schedule to be compiled, we study the integrated optimization of TTP and VSP with uncertainty. In this paper, a novel multiobjective optimization approach with the objectives of minimizing the passenger travel cost, the vehicle scheduling cost, and the incompatible trip-link cost is proposed. Meanwhile, a multiobjective hybrid algorithm, which is a combination of the self-adjust genetic algorithm (SGA), large neighborhood search (LNS) algorithm, and Pareto separation operator (PSO), is applied to solve the integrated optimization problem. The experimental results show that the approach outperforms existing approaches in terms of service level and robustness.
DOI Link: 10.1155/2021/3529984
Rights: Copyright © 2021 Yindong Shen et al. This is an open access article distributed under the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Licence URL(s):

Files in This Item:
File Description SizeFormat 
3529984.pdfFulltext - Published Version1.66 MBAdobe PDFView/Open

This item is protected by original copyright

A file in this item is licensed under a Creative Commons License Creative Commons

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.