A Time- and Space-Efficient Heuristic Approach for Late Train-Crew Rescheduling

TL;DR

This paper introduces a heuristic method combining tabu search and column generation to efficiently reschedule train crew duties after driver absences, minimizing task cancellations and changes.

Contribution

It presents a novel, time- and space-efficient heuristic approach for train crew rescheduling, outperforming traditional methods in computational efficiency.

Findings

Tabu-search approach requires less computation time.

The heuristic reduces task cancellations compared to benchmarks.

Performance tested on real-world Swedish train data.

Abstract

In this paper, we reschedule the duties of train drivers one day before the operation. Due to absent drivers (e.g., because of sick leave), some trains have no driver. Thus, duties need to be rescheduled for the day of operation. We start with a feasible crew schedule for each of the remaining operating drivers, a set of unassigned tasks originally assigned to the absent drivers, and a group of standby drivers with fixed start time, end time, start depot, and end depot. Our aim is to generate a crew schedule with as few canceled or changed tasks as possible. We present a tabu-search-based approach for crew rescheduling. We also adapt a column-generation approach with the same objective function and equivalent restrictions as the benchmark for comparing the results, computational time, and space usage. Our tabu-search-based approach needs both less computation time and space than the column-generation approach to compute an acceptable result. We further test the performance of our approach under different settings. The data used in the experiments originated from a regional passenger-train system around Stockholm, Sweden and was provided by M\"alart\r{a}g.