Scheduling trains as a blocking parallel-machine job shop scheduling problem

In this paper, the train scheduling problem is modelled as a blocking parallel-machine job shop scheduling (BPMJSS) problem. In the model, trains, single-track sections and multiple-track sections, respectively, are synonymous with jobs, single machines and parallel machines, and an operation is regarded as the movement/traversal of a train across a section. Due to the lack of buffer space, the real-life case should consider blocking or hold-while-wait constraints, which means that a track section cannot release and must hold the train until next section on the routing becomes available. Based on literature review and our analysis, it is very hard to find a feasible complete schedule directly for BPMJSS problems. Firstly, a parallel-machine job-shop-scheduling (PMJSS) problem is solved by an improved shifting bottleneck procedure (SBP) algorithm without considering blocking conditions. Inspired by the proposed SBP algorithm, feasibility satisfaction procedure (FSP) algorithm is developed to solve and analyse the BPMJSS problem, by an alternative graph model that is an extension of the classical disjunctive graph models. The proposed algorithms have been implemented and validated using real-world data from Queensland Rail. Sensitivity analysis has been applied by considering train length, upgrading track sections, increasing train speed and changing bottleneck sections. The outcomes show that the proposed methodology would be a very useful tool for the real-life train scheduling problems

Examination of pencil beam predictions of small-field dose using dosimetry film and Monte Carlo simulations

The iPlan treatment planning sys-tem uses a pencil beam algorithm, with density cor-rections, to predict the doses delivered by very small (stereotactic) radiotherapy fields. This study tests the accuracy of dose predictions made by iPlan, for small-field treatments delivered to a planar solid wa-ter phantom and to heterogeneous human tissue using the BrainLAB m3 micro-multileaf collimator.

Determining operations affected by delay in predictive train timetables

Constructing train schedules is vital in railways. This complex and time consuming task is however made more difficult by additional requirements to make train schedules robust to delays and other disruptions. For a timetable to be regarded as robust, it should be insensitive to delays of a specified level and its performance with respect to a given metric, should be within given tolerances. In other words the effect of delays should be identifiable and should be shown to be minimal. To this end, a sensitivity analysis is proposed that identifies affected operations. More specifically a sensitivity analysis for determining what operation delays cause each operation to be affected is proposed. The information provided by this analysis gives another measure of timetable robustness and also provides control information that can be used when delays occur in practice. Several algorithms are proposed to identify this information and they utilise a disjunctive graph model of train operations. Upon completion the sets of affected operations can also be used to define the impact of all delays without further disjunctive graph evaluations.

Time and cost performance of design–build projects

The design-build (DB) delivery method has been widely used in the United States due to its reputed superior cost and time performance. However, rigorous studies have produced inconclusive support and only in terms of overall results, with few attempts being made to relate project characteristics with performance levels. This paper provides a larger and more finely grained analysis of a set of 418 DB projects from the online project database of the Design-Build Institute of America (DBIA), in terms of the time-overrun rate (TOR), early start rate (ESR), early completion rate (ECR) and cost overrun rate (COR) associated with project type (e.g., commercial/institutional buildings and civil infrastructure projects), owners (e.g., Department of Defense and private corporations), procurement methods (e.g., ‘best value with discussion’ and qualifications-based selection), contract methods (e.g., lump sum and GMP) and LEED levels (e.g., gold and silver). The results show ‘best value with discussion’ to be the dominant procurement method and lump sum the most frequently used contract method. The DB method provides relatively good time performance, with more than 75% of DB projects completed on time or before schedule. However, with more than 50% of DB projects cost overrunning, the DB advantage of cost saving remains uncertain. ANOVA tests indicate that DB projects within different procurement methods have significantly different time performance and that different owner types and contract methods significantly affect cost performance. In addition to contributing to empirical knowledge concerning the cost and time performance of DB projects with new solid evidence from a large sample size, the findings and practical implications of this study are beneficial to owners in understanding the likely schedule and budget implications involved for their particular project characteristics.

Domain Adaptation on the Statistical Manifold

In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions be- come similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that prob- ability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this man- ifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the cor- responding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of- the-art results on a standard object recognition benchmark.