A Branch-and-Price-and-Cut Algorithm for the Integrated Scheduling and Rostering Problem of Bus Drivers

Dung Ying Lin, Chieh Ju Juan, Ching Chih Chang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In the transportation industry, crew management is typically decomposed into two phases: crew scheduling and crew rostering. Due to the complexity of scheduling and rostering, bus transportation is not an exception and many relevant studies do not consider both procedures simultaneously. However, such a decomposition can yield inferior schedules/rosters. To address this issue, this paper proposes an integrated scheduling and rostering model for bus drivers and devises a branch-and-price-and-cut (BPC) algorithm to solve the complex problem. The proposed solution framework is empirically applied to real-world instances with various problem sizes whose data is collected from H Bus Company located in southern Taiwan. To validate the effectiveness and evaluate the efficiency of the proposed solution framework, this paper compares the solution obtained from the BPC algorithm with that of a benchmark optimization package. The results show that the proposed BPC algorithm can solve problems with large real-world instances within a reasonable computational time. Moreover, in the numerical experiments, this paper finds that the scheduling and rostering results of the bus drivers are more sensitive to the rostering constraints. Also, the proposed integrated framework can yield a better solution than the solution from a conventional two-phase approach, which demonstrates the advantage of the integration in this paper. The proposed method provided can be employed to deal with the challenges in driver planning for bus companies.

Original languageEnglish
Article number3153201
JournalJournal of Advanced Transportation
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management


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