A mathematical programming approach to railway network asset management

A main challenge in railway asset management is selecting the maintenance strategies to apply to each asset on the network in order to effectively manage the railway infrastructure given that some performance and safety targets have to be met under budget constraints. Due to economic, functional and operational dependencies between different assets and different sections of the network,# optimal solutions at network level not always include the best strategies available for each asset group. This paper presents a modelling approach to support decisions on how to effectively maintain a railway infrastructure system. For each railway asset, asset state models combining degradation and maintenance are used to assess the impact of any maintenance strategy on the future asset performance. The asset state models inform a network-level optimisation model aimed at selecting the best combination of maintenance strategies to manage each section of a given railway network in order to minimise the impact of the assets conditions on service, given budget constraints and performance targets. The optimisation problem is formulated as an integer-programming model. By varying the model parameters, scenario analysis can be performed so that the infrastructure manager is provided with a range of solutions for different combination of budget available and performance targets

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 292)

This bibliography lists 192 reports, articles and other documents introduced into the NASA scientific and technical information system in December, 1986

Chaos and energy spreading for time-Dependent Hamiltonians, and the various Regimes in the theory of Quantum Dissipation

We make the first steps towards a generic theory for energy spreading and quantum dissipation. The Wall formula for the calculation of friction in nuclear physics and the Drude formula for the calculation of conductivity in mesoscopic physics can be regarded as two special results of the general formulation. We assume a time-dependent Hamiltonian $H(Q,P;x(t))$ with $x(t)=Vt$, where $V$ is slow in a classical sense. The rate-of-change $V$ is not necessarily slow in the quantum-mechanical sense. Dissipation means an irreversible systematic growth of the (average) energy. It is associated with the stochastic spreading of energy across levels. The latter can be characterized by a transition probability kernel $P_t(n|m)$ where $n$ and $m$ are level indices. This kernel is the main object of the present study. In the classical limit, due to the (assumed) chaotic nature of the dynamics, the second moment of $P_t(n|m)$ exhibits a crossover from ballistic to diffusive behavior. We define the $V$ regimes where either perturbation theory or semiclassical considerations are applicable in order to establish this crossover in the quantal case. In the limit $\hbar\to 0$ perturbation theory does not apply but semiclassical considerations can be used in order to argue that there is detailed correspondence, during the crossover time. In the perturbative regime there is a lack of such correspondence. Namely, $P_t(n|m)$ is characterized by a perturbative core-tail structure that persists during the crossover time. In spite of this lack of (detailed) correspondence there may be still a restricted correspondence as far as the second-moment is concerned. Such restricted correspondence is essential in order to establish the universal fluctuation-dissipation relation.Comment: 46 pages, 6 figures, 4 Tables. To be published in Annals of Physics. Appendix F improve

Stochastic Optimization Models for Perishable Products

For many years, researchers have focused on developing optimization models to design and manage supply chains. These models have helped companies in different industries to minimize costs, maximize performance while balancing their social and environmental impacts. There is an increasing interest in developing models which optimize supply chain decisions of perishable products. This is mainly because many of the products we use today are perishable, managing their inventory is challenging due to their short shelf life, and out-dated products become waste. Therefore, these supply chain decisions impact profitability and sustainability of companies and the quality of the environment. Perishable products wastage is inevitable when demand is not known beforehand. A number of models in the literature use simulation and probabilistic models to capture supply chain uncertainties. However, when demand distribution cannot be described using standard distributions, probabilistic models are not effective. In this case, using stochastic optimization methods is preferred over obtaining approximate inventory management policies through simulation. This dissertation proposes models to help businesses and non-prot organizations make inventory replenishment, pricing and transportation decisions that improve the performance of their system. These models focus on perishable products which either deteriorate over time or have a fixed shelf life. The demand and/or supply for these products and/or, the remaining shelf life are stochastic. Stochastic optimization models, including a two-stage stochastic mixed integer linear program, a two-stage stochastic mixed integer non linear program, and a chance constraint program are proposed to capture uncertainties. The objective is to minimize the total replenishment costs which impact prots and service rate. These models are motivated by applications in the vaccine distribution supply chain, and other supply chains used to distribute perishable products. This dissertation also focuses on developing solution algorithms to solve the proposed optimization models. The computational complexity of these models motivated the development of extensions to standard models used to solve stochastic optimization problems. These algorithms use sample average approximation (SAA) to represent uncertainty. The algorithms proposed are extensions of the stochastic Benders decomposition algorithm, the L-shaped method (LS). These extensions use Gomory mixed integer cuts, mixed-integer rounding cuts, and piecewise linear relaxation of bilinear terms. These extensions lead to the development of linear approximations of the models developed. Computational results reveal that the solution approach presented here outperforms the standard LS method. Finally, this dissertation develops case studies using real-life data from the Demographic Health Surveys in Niger and Bangladesh to build predictive models to meet requirements for various childhood immunization vaccines. The results of this study provide support tools for policymakers to design vaccine distribution networks

Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning

Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer. Meta learning techniques are leveraged to pre-train the NN weights and priors of the ABLR model using data collected from historical similar tasks. For a new control task, we refine the meta-learned models using a few samples, and introduce pessimistic confidence bounds into CBF constraints to ensure safe control. Moreover, we provide theoretical criteria to guarantee probabilistic safety during the control processes. To validate our approach, we conduct comparative experiments in various obstacle avoidance scenarios. The results demonstrate that our algorithm significantly improves the Bayesian model-based CBF method, and is capable for efficient safe exploration even with multiple uncertain constraints