Naoto Fukumura

Real-time freight locomotive rescheduling and uncovered train detection during disruption

This paper discusses rescheduling of freight train locomotives when dealing with a disrupted situation in the daily operations in Japan. Within the current framework of dispatching processes, passenger railway operators modify the entire timetables and an adjusted freight train timetable is distributed to a freight train operator. For this timetable, we solve the locomotive rescheduling problem by changing the assignment of the locomotives to all the trains and considering their periodic inspections. We then solve the uncovered train detection problem that selects unassigned trains according to their value if the rescheduling phase fails.

An algorithm for station shunting scheduling problems combining probabilistic local search and PERT

Shunting scheduling problems at railway stations can be regarded as a sort of resource constrained project scheduling problem (RCPSP). Unlike the normal RCPSPs, however, shunting scheduling problems require that the number of work which consists of a project has to be dynamically changed in the process of solving the problem and some of the work has to be performed at a prescribed timing, for example. We propose an efficient algorithm for shunting scheduling problems combining probabilistic local search and PERT. Local search and PERT are combined so that the candidates for answers in the local search process are evaluated by PERT. This enables us to reduce the search space of the local search to a great extent and makes the algorithm work quite fast. We have confirmed the effectiveness of our algorithm through several experiments using practical train schedule data.