LOCOMOTIVE SCHEDULING UNDER UNCERTAIN DEMAND
Each day, railroads face the problem of allocating power to trains. Often, power requirements for each train are not known with certainty, and the fleet of locomotives may not be homogeneous. To deal with both of these complications, we formulate a multicommodity flow problem with convex objective function on a time-space network. The convex objective allows us to minimize expected cost under uncertainty by penalizing trip arcs likely to have too little power. Our solution heuristic sends locomotives down the shortest paths (based on marginal arc costs) in the time-space network and then attempts to improve interchanges of locomotives around cycles. Two lower bounds are also developed by relaxing the multicommodity aspect of the problem. In 19 test problems, ranging in size from 15 to 404 arcs, the heuristic performed well, with short running times and costs averaging within 3% of the best of the two lower bounds developed.
The Locomotive Routing Problem
Given a schedule of trains, the locomotive planning (or scheduling) problem (LPP) is to determine the minimum cost assignment of locomotive types to trains that satisfies a number of business and operational constraints. Once this is done, the railroad has to determine the sequence of trains to which each locomotive is assigned by unit number so that it can be fueled and serviced as necessary. We refer to this problem as the locomotive routing problem (LRP). The LRP is a very large scale combinatorial optimization problem, and the general version that we consider has previously been unstudied and unsolved in the research literature. In this paper, we develop robust optimization methods to solve the LRP. There are two major sets of constraints that need to be satisfied by each locomotive route: (1) locomotive fueling constraints, which require that every unit visit a fueling station at least once for every F miles of travel, and (2) locomotive servicing constraints, which require that every unit visit a service station at least once for every S miles of travel. The output of the LPP is not directly implementable because the LPP does not consider these fueling and servicing constraints. The LRP considers these constraints, and its output is therefore implementable. We model the LRP by considering alternative fueling and servicing friendly train paths (or strings) between servicing stations on the network. We formulate the LRP as an integer programming problem on a suitably constructed space-time network and show that this problem is NP-Complete. This integer programming problem has millions of decision variables. We develop a fast aggregation-disaggregation based algorithm to solve the problem within a few minutes of computational time to near optimality. Finally, we present computational results and extensive case studies of our algorithms on real data provided by a major Class I U.S. railroad.
OPTIMIZATION OF PROSPECTIVE PLANNING OF CLASSIFICATION YARD DEVELOPMENT
The paper deals with the solution of problems involved in determining the technical characteristics of classification yard operation relating the volume of car traffic to the car detention, and also to the allowable level of traffic to be handled by a yard, and in selecting measures for improving the technical equipment of a yard for the prospective conditions of its operation. The problems are solved with the use of the method of mathematical modeling of production processes on an electronic computer.