Optimization Models for Shift Scheduling for Train Dispatchers
About this ebook
The aim of this thesis is to create a scheduling framework, based on mathematical optimization models, that supports shift planners in their work. The framework helps in automatizing the scheduling process and improves the quality of the resulting shifts. Our initial approach is to use our models to figure out the required staffing levels and produce baseline schedules, and then improve these by increasing their attractiveness. To achieve this goal, we conduct three related studies considering the case of Malm¨o dispatching center. The studies result in different optimization models for shift scheduling of train dispatchers. We present the complete results in the following three papers:
In the first paper, we build an optimization model for one-day shift scheduling, where the objective function is to minimize the number of needed dispatchers. Moreover, we analyze the impact on the computation time of different parameters, such as the number of geographical areas and how these are combined. The results of the experiments are obtained within acceptable run times for real-world size instances. In the second paper, we suggest a stronger formulation of the previous model; in addition, we focus on improving the quality of the shifts. The improvements consider avoiding undesirable start times (between 00 and 05 am) and too short shifts (shorter than 6h). Our major contribution in this paper is giving four approaches for defining and modeling area-dispatcher-assignment switches, called handovers, which occur when a dispatcher changes the assigned controlled areas during a shift. Two of these approaches gave promising results for solving real-world size instances to optimality within acceptable run times.
In the third paper, we increase the time horizon from one day to a week. We do this by combining the resulting shifts from some instances in the second paper where we consider a list of legal constraints, such as resting time between shifts, weekly rest and the total weekly working hours. In addition, we analyze the impact of the variability in shift lengths on the quality of the weekly schedules.
