Optimal public transport timetabling and vehicle scheduling with multi-depot multi-type ba

A research dataset by Zhichao Cao, last updated May 2026, presents a model for optimizing public transport operations with a heterogeneous fleet of battery-electric vehicles. The model minimizes costs related to charger count, recharging queue times, and fleet size, reporting a 35.37% total cost reduction and a 36.11% decrease in fleet size compared to a benchmark. The underlying data likely contains parameters and results for the mixed integer linear programming model and Lagrangian relaxation algorithm.

Use Cases

• Benchmarking electric vehicle scheduling algorithms based on the described cost-minimization model.
• Analyzing the trade-offs between charger investment and fleet size in multi-depot operations.
• Simulating public transport timetables that incorporate deadheading trips and heterogeneous vehicle batteries.
• Evaluating the impact of Lagrangian relaxation and heuristic enhancements on computational convergence for large-scale scheduling problems.

Strengths

• The model achieved a reported 35.37% total cost reduction and a 36.11% decrease in fleet size.
• Includes algorithmic enhancements like domain reduction, valid cuts, and multiple chains’ identification.
• Explicitly models multi-depot operations with heterogeneous battery capacities and load capacities.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small (211.9 KB), suggesting limited scope or a focus on model parameters rather than extensive operational data.

Provenance

Source

Zhichao Cao via figshare

Collection Method

Derived from a mixed integer linear programming model and solved with a Lagrangian relaxation-based algorithm.

Freshness

Last updated 2026-05-15 05:10:00; freshness should be verified.