Tactical crew planning at Turkish state railways

Official URL: http://192.168.1.20/record=b1293774 (Table of Contents)

Tactical crew planning problem at Turkish State Railways (TCDD) involves finding the minimum crew capacity in a crew region required to operate a predetermined set of train duties assigned to the region by the headquarters. The problem is to be solved for each crew region by satisfying various rules and constraints associated with the requirements of the company. One of the most important constraints is the day-off requirement which makes the problem computationally intractable. In this study, we use a space-time network flow representation to solve the tactical capacity planning problem with day-off requirement. To solve the problem, we develop two solution approaches: the sequential approach and the integrated approach. In the sequential approach we mimic the current practice at TCDD and solve the problem in two stages. In the first stage, we solve a minimum flow problem over a space-time network by relaxing the day-off requirement. After obtaining the tentative schedules of crew members, we solve an assignment problem to fill-in the days-off in the tentative schedules by using additional substitute crew members. In the integrated approach, we solve a minimum flow problem with side constraints using a layered space-time network representation of the problem. We present the computational study on a real-life data set acquired from TCDD. We, then, study a higher level crew capacity planning problem. In this tactical-to-strategic level capacity planning problem, we minimize the total crew capacity of all regions by simultaneously considering multiple regions. We do this by re-assigning duties to different regions or allowing two neighboring crew regions share the train duties in different settings. For tactical-to-strategic capacity planning problem, we present the mathematical formulation of single-region and two-region models with various crew exchange policies. Given the large scale of the search space, we choose to employ a neighborhood search heuristic in order to solve the problem. The neighborhood search heuristic uses the minimum flow problem in the sequential approach as a subprocedure. We present the computational study again for the TCDD data set.