Tactical Network Flow and Discrete Optimization Models and Algorithms for the Empty Railcar Transportation Problem

Prior to 1980, the practice in multilevel autorack management was to load the railcars at various origin points, ship them to the destination ramps, unload them, and then return each car to the loading point where it originated. Recognizing the inefficiency of such a practice with respect to the fleet size that had to be maintained, and the associated poor utilization due to the excessive empty miles logged, a consolidation of the railcars was initiated and completed by February 1982. Under this pooling program, a central management was established to control the repositioning of three types of empty railcars for eight principal automobile manufacturers. Today, the practice is to consolidate the fleets of all automobile manufacturers for each equipment type, and to solve the distribution problem of reposi-tioning empty multilevel autoracks of each type from points at which they are unloaded to automobile assembly facilities where they need to be reloaded. Each such problem is referred to in the railroad industry as a repositioning scenario. In this dissertation, we present two tactical models to assist in the task of centrally managing the distribution of empty railcars on a day-today basis for each repositioning scenario. These models take into account various practical issues such as uncertainties, priorities with respect to time and demand locations, multiple objectives related to minimizing different types of latenesses in delivery, and blocking issues. It is also of great practical interest to the central management team to have the ability to conduct various sensitivity analyses in its operation. Accordingly, the system provides for the capability to investigate various what-if scenarios such as fixing decisions on running a specified block of cars (control orders) along certain routes as dictated by business needs, and handling changes in supplies, demands, priorities, and transit time characteristics. Moreover, the solution methodology provides a flexible decision-making capability by permitting a series of runs based on a sequential decision-fixing process in a real-time operational mode. A turnaround response of about five minutes per scenario (on a Pentium PC or equivalent) is desired in practice. This dissertation begins by developing several progressive formulations that incorporate many practical considerations in the empty railroad car distribution planning system. We investigate the performance of two principal models in this progression to gain more insights into the implementation aspects of our approach. The first model (TDSS1: Tactical Decision Support System-1) considers all the identified features of the problem except …