Revisiting the richness of integrated vehicle and crew scheduling

Abstract

The last decades have seen a considerable move forward regarding integrated vehicle and crew scheduling in various realms (airline industry, public transport). With the continuous improvement of information and communication technology as well as general solvers it has become possible to formulate more and more rich versions of these problems. In public transport, issues like rostering, delay propagation or days-off patterns have become part of these integrated problems. In this paper we aim to revisit an earlier formulation incorporating days-off patterns and investigate whether solvability with standard solvers has now become possible and to which extent the incorporation of other aspects can make the problem setting more rich and still keep the possible solvability in mind. This includes especially issues like delay propagation where in public transport delay propagation usually refers to secondary delays following a (primary) disturbance. Moreover, we investigate a robust version to support the claim that added richness is possible. Numerical results are provided to underline the envisaged advances.

Robustness and disturbances in public transport

“… 2018a ; Ge et al. 2022 ). Rather than answering the question whether to wait, this also relates to incorporating fixed as well as variable buffer times upfront as part of the various planning stages.…”

“…( 2018 ), the integrated vehicle and crew scheduling problem with days off patterns in Ge et al. ( 2022 ), the robust version of the periodic event scheduling problem in Goerigk ( 2015 ), and alike.…”

“…Using a simple means of robustness, Ge et al. ( 2022 ) incorporate delay propagation ideas from Amberg et al. ( 2019 ) into the model of Mesquita et al.…”

“…( 2019 ) Bi-level rescheduling MIP model, case study for rescheduling Dutch railway traffic Ge et al. ( 2022 ) Integrated vehicle and crew scheduling Delay propagation as well as replication studies Lai and Leung ( 2018 ) Integrated vehicle and crew scheduling Stochastic and time-dependent travel times Maenhout and Vanhoucke ( 2018 ) Integrated personnel shift and task rescheduling problem Heuristic rescheduling procedure Potthoff et al. ( 2010 ) Set covering formulation for the problem of crew rescheduling Nested neighborhoods, MIP model Veelenturf et al.…”

Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT?

“…This will eventually lead to subsequent stochastic problems with an uncertain number of vehicles. Related research in that context is specified per se and in terms of ChatGPT usage in the following works: [13,56,57]. Q: "Is it possible to motivate a stochastic vehicle problem with an uncertain number of vehicles through bus bunching?"…”