Incorporating Service Reliability in Multi-depot Vehicle Scheduling

TL;DR

This paper presents a new method for multi-depot vehicle scheduling that incorporates service reliability using chance-constrained programming, ensuring on-time performance and fairness, with an exact solution approach and heuristics for large instances.

Contribution

Introduces a CCP-based model for MDVSP that guarantees service reliability and fairness, along with an exact B&C solution scheme and a Lagrangian heuristic for large-scale problems.

Findings

Stochastic approach improves OTP guarantees and cost-effectiveness.

Proposed algorithms outperform existing methods in computational efficiency.

Method effectively handles real-world transit scheduling complexities.

Abstract

The multi-depot vehicle scheduling problem (MDVSP) is a critical planning challenge for transit agencies. We introduce a novel approach to MDVSP by incorporating service reliability through chance-constrained programming (CCP), targeting the pivotal issue of travel time uncertainty and its impact on transit service quality. Our model guarantees service reliability measured by on-time performance (OTP), a primary metric for transit agencies, and fairness across different service areas.We propose an exact branch-and-cut (B&C) scheme to solve our CCP model. We present several cut-generation procedures that exploit the underlying problem structure and analyze the relationship between the obtained cut families. Additionally, we design a Lagrangian-based heuristic to handle large-scale instances reflective of real-world transit operations. Our approach partitions the set of trips, each subset leading to a subproblem that can be efficiently solved with our B&C algorithm, and then employs a procedure to combine the subproblem solutions to create a vehicle schedule that satisfies all the planning constraints of the MDVSP. Our empirical evaluation demonstrates the superiority of our stochastic variant in achieving cost-effective schedules with reliable OTP guarantees compared to alternatives commonly used by practitioners, as well as the computational benefits of our methodologies.