Abstract
This article addresses a particular bus Crew Scheduling Problem (CSP) that arises in a public transport company in Spain. The classical CSP problem tries to create bus driver schedules covering all the bus schedules published by the company. The problem posed does not require satisfying any restriction due to the lack of drivers. This relaxed restriction emerged in the time of Covid-19 for different reasons. This is why the need arose in the company for a tool that allows optimizing the daily work of the drivers. The objective function of the problem requires servicing the greatest number of passengers possible, instead of minimizing the schedule cost. A model and strategies for solving the problem exactly are introduced: clustering, re-optimization, etc. Additionally, a re-optimization model is proposed using prior feasible solutions to speed up the resolution of the problem. The integration and practical use of the solutions obtained and their corresponding monitoring in the decision-making process of the company are described.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Raw data were generated at Transportes Interurbanos de Tenerife S.A. (TITSA). Derived data supporting the ndings of this study are available from the corresponding author E.G. Guillermo on request.