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A new bi-objective simultaneous model for timetabling and scheduling public bus transportation

Author

Listed:
  • Seyedeh Simin Mousavi

    (Ferdowsi University of Mashhad)

  • Alireza Pooya

    (Ferdowsi University of Mashhad)

  • Pardis Roozkhosh

    (Ferdowsi University of Mashhad)

  • Morteza Pakdaman

    (Climatological Research Institute (CRI))

Abstract

The efficient design of public transportation networks is critical in establishing optimal schedules and curtailing transport costs for both passengers and transportation organizations. This study focuses on advancing existing models for selecting timetables and optimal schedules within public transportation networks. Addressing these concerns as a bi-objective optimization problem, this paper aims to develop an effective method for simultaneous optimization, targeting the reduction of transportation costs and minimizing passengers’ waiting times. Initially, this study formulates the problem using appropriate mixed-integer linear programming. To tackle this challenging optimization problem, various algorithms, including the non-dominated sorting genetic algorithm (NSGA), bi-objective particle swarm optimization algorithm (bi-objective PSO), and bi-objective red deer algorithm (bi-objective RDA), are employed. The selection of these algorithms aims to explore different solution spaces and their abilities to produce Pareto-optimal solutions. To assess the effectiveness of these Pareto algorithms in addressing the problem, the epsilon constraint method is utilized. Additionally, a redesign method is introduced to confront optimization challenges in this specific research domain. Leveraging bi-objective problem estimator parameters and the response level method, the algorithms’ parameters are optimized, elucidating the best-case scenarios for each parameter. A comprehensive comparative analysis of algorithm performance is conducted, considering various criteria, including solution time, convergence, and diversity of solutions. Furthermore, sensitivity analyses are carried out on problem-sensitive parameters in a case study, culminating in significant managerial implications for addressing the proposed problem in real-world scenarios.

Suggested Citation

  • Seyedeh Simin Mousavi & Alireza Pooya & Pardis Roozkhosh & Morteza Pakdaman, 2025. "A new bi-objective simultaneous model for timetabling and scheduling public bus transportation," OPSEARCH, Springer;Operational Research Society of India, vol. 62(1), pages 198-229, March.
    • Handle: RePEc:spr:opsear:v:62:y:2025:i:1:d:10.1007_s12597-024-00807-8
    DOI: 10.1007/s12597-024-00807-8
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    References listed on IDEAS

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    1. Pardis Roozkhosh & Alireza Pooya & Renu Agarwal, 2023. "Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach," Operations Management Research, Springer, vol. 16(2), pages 705-725, June.
    2. Chunyan Tang & Avishai Ceder & Ying-En Ge, 2018. "Optimal public-transport operational strategies to reduce cost and vehicle’s emission," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-17, August.
    3. Dilek Tuzun Aksu & Samet Yılmaz, 2014. "Transit coordination with heterogeneous headways," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(5), pages 450-465, July.
    4. Saeedeh Bazari & Alireza Pooya & Omid Soleimani Fard & Pardis Roozkhosh, 2023. "Modeling and solving the problem of scheduling university exams in terms of new constraints on the conflicts of professors' exams and the concurrence of exams with common questions," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 877-915, June.
    5. Laporte, Gilbert & Ortega, Francisco A. & Pozo, Miguel A. & Puerto, Justo, 2017. "Multi-objective integration of timetables, vehicle schedules and user routings in a transit network," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 94-112.
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