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), using some randomly generated instances.genetic algorithm;heuristics;opportunities;maintenance optimization;memetic algorithm

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.heuristics;railway planning;integer linear programming;rolling stock re-scheduling

Assessing a Knowledge Management (KM) Project Selection Framework

Knowledge management (KM) is becoming recognized as a valuable tool for establishing and maintaining competitive advantage. Decision superiority, the ultimate goal of KM, is only possible through the effective and efficient use of knowledge. But, to effectively and efficiently create and use KM, it is important to carefully select KM projects. This research assesses the usefulness of a KM project selection framework that was developed by U.S. Air Force Captain William Bower (2001) and refined by 1st Lt Jeffrey Phillips (2003) referred to here as the Bower-Phillips decision framework. To assess the usability of the framework, a case study was undertaken at an Air Force organization to identify and address potential knowledge management opportunities. The framework was found to be useful in identifying possible KM projects

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

Similarities and differences in the dolomitization history of two coeval Middle Triassic carbonate platforms, Balaton Highland, Hungary

Dolomitization of platform carbonates is commonly the result of multiphase processes. Documentation of the complex dolomitization history is difficult if completely dolomitized sections are studied. Two Middle Anisian sections representing two coeval carbonate platforms were investigated and compared in the present study. Both sections are made up of meter-scale peritidal–lagoonal cycles with significant pedogenic overprint. One of the sections contains non-dolomitized, partially dolomitized, and completely dolomitized intervals, whereas the other is completely dolomitized. Based on investigations of the partially dolomitized section, penecontemporaneous dolomite formation and/or very early post-depositional dolomitization were identified in various lithofacies types. In shallow subtidal facies, porphyrotopic dolomite was found preferentially in microbial micritic fabrics. Microbially induced dolomite precipitation and/or progressive replacement of carbonate sediments could be interpreted for stromatolites. Cryptocrystalline to very finely crystalline dolomite, probably of pedogenic origin, was encountered in paleosoil horizons. Fabric-destructive dolomite commonly found below these horizons was likely formed via reflux of evaporated seawater. As a result of the different paleogeographic settings of the two platforms, their shallow-burial conditions were significantly different. One of the studied sections was located at the basinward platform margin where pervasive fabric-retentive dolomitization took place in a shallow-burial setting, probably via thermal convection. In contrast, in the area of the other, smaller platform shallow-water carbonates were covered by basinal deposits, preventing fluid circulation and accordingly pervasive shallow-burial dolomitization. In the intermediate to deep burial zone, recrystallization of partially dolomitized limestone and occlusion of newly opened fractures and pores by coarsely crystalline dolomite took place

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

Optical and structural properties of ZnO films deposited on GaAs by pulsed laser deposition

doi:10.1063/1.373643ZnO films were synthesized on GaAs substrates at different growth conditions by pulse laser deposition. High-purity (99.999%) oxygen was used as the ambient gas. The pressure of the ambient oxygen gas for ZnO film growth was varied from 20 to 50 mTorr, and the growth temperature from 300 to 450 °C. ZnO films showed very strong bound exciton peaks located between 3.37 and 3.35 eV. The full width at half maximum of the bound exciton peak is less than 5 meV. These results indicate ZnO films on GaAs substrates can be used for optical devices such as light-emitting diodes. The other significant properties of textured ZnO films on GaAs substrates are described.This work was supported in part by University of Missouri Research Board under Grant No. RB95-061, the U.S. Army Research Office ~Research Triangle Park, N.C.! under Grant No. DAAH04-94-G-0305, and the Office of Naval Research-Electronics Division, Contract No. N00014-99-1-0288