Operations Research Models for Scheduling Railway Infrastructure Maintenance

In the last decades, maintenance of technical systems has become increasingly impor- tant in many industries. Such systems are transport systems (rail, bus, airplane), civil engineering systems (roads, buildings, bridges), health care (hospitals), communication systems, manufacturing plants, etc. Failures of these systems may cause expensive pro- duction losses and can have a negative e®ect on the environment and safety. For example, a train accident, crash of an airplane, collapse of a building, computer breakdown in an hospital, may cause large societal costs. Substantial environmental damage may result from a failure i

Scheduling preventive railway maintenance activities

A railway system needs a substantial amount of maintenance. To prevent unexpected breakdowns as much as possible, preventive maintenance is required. In this paper we discuss the Preventive Maintenance Scheduling Problem (PMSP), where (short) routine activities and (long) unique projects have to be scheduled in a certain period. To reduce costs and inconvenience for the travellers and operators, these activities have to be scheduled as much as possible together. We present a mathematical formulation for this problem and some greedy heuristics to solve it fast. Moreover, we compare the performance of these heuristics with the optimal solution using some randomly generated instances

Genetic and memetic algorithms for scheduling railway maintenance activities

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), usin

Re-scheduling in railways: the rolling stock balancing problem

This paper addresses the Rolling Stock Balancing Problem (RSBP). This problem arises at a passenger railway operator when the rolling stock has to be re-scheduled due to changing circumstances. These problems arise both in the planning process and during operations. The RSBP has as input a timetable and a rolling stock schedule where the allocation of the rolling stock among the stations does not fit to the allocation before and after the planning period. The problem is then to correct these off-balances, leading to a modified schedule that can be implemented in practice. For practical usage of solution approaches for the RSBP, it is important to solve the problem quickly. Therefore, the focus is on heuristic approaches. In this paper, we describe two heuristics and compare them with each other on some (variants of) real-life instances of NS, the main Dutch passenger railway operator. Finally, to get some insight in the quality of the proposed heuristics, we also compare their outcomes with optimal solutions obtained by solving existing rolling stock circulation models

A review of planning models for maintenance and production.

In this article we give an overview of the relation between planning of maintenance and production. On the one hand we consider production planning and scheduling models in which failures and maintenance aspects are taken into account. Next we discuss the planning of maintenance activities, for which we consider both preventive as well as corrective maintenance. Thirdly, we consider the planning of maintenance activities at such moments in time where the items to be maintained are not or less needed for production, also called opportunity maintenance. Apart from describing the main ideas, approaches, trends, and results we also provide a number of applications