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

TL;DR

The paper tackles short-term, one-day railway crew rescheduling when drivers are absent, aiming to minimize train cancellations by reassigning unassigned tasks to remaining on-duty and standby drivers. It introduces a tabu-search-based heuristic and benchmarks it against an adapted column-generation approach that shares the same objective and operational restrictions, using real data from Mälartåg. The two main contributions are (i) a tabu-search method with multiple neighborhood strategies and deadheading options, and (ii) an adapted column-generation framework for fair comparison and performance assessment. Empirically, the tabu-search approach demonstrates faster runtimes and lower memory usage than column generation while achieving comparable assignment rates, including robust performance as the number of absent drivers grows, which supports practical deployment for real-time disruption management in rail systems.

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 Mälartåg.

Figures (10)

• Figure 1: Generate neighbouring solutions
• Figure 2: Strategies of finding neighbouring solutions. (a): only assign unassigned tasks to one available driver; (b): assign the unassigned tasks while unassigning some assigned tasks
• Figure 3: Example of Strategy I. (a): initial schedule; (b): new schedule with Strategy I
• Figure 4: Example of Strategy II. (a): initial schedule; (b): new schedule with Strategy II
• Figure 5: The process of the adapted column-generation approach