Late Train-Crew Rescheduling: A Tabu-Search-Based Approach Using Restricted DFS

Abstract

In this paper, we reschedule the duties of train drivers one day before the operation. Because several drivers are absent (for example, 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 set 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 tasks as possible. We present a tabu-search-based approach with restricted graph search for crew rescheduling. We also adopt a column-generation approach as a benchmark to reflect the same restrictions and objective function as our tabu-search-based approach. We use it to compare the quality of the results, computational time, and other performance indicators. Our tabu-search-based approach needs less computational time than the column-generation approach to compute an acceptable result. We further test the performance of our approach under different settings. One of the datasets used in the experiments originated from a regional passenger-train system around Stockholm, Sweden, and was provided by Mälartåg. The other dataset is generated from the open-source crew scheduling data from the ROMSOC project.