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- SourceAvailable from: Sydney Dixon Howell[Show abstract] [Hide abstract]
ABSTRACT: In this paper, we study optimal revenue management applied to carparks, with primary objective to maximize revenues under a continuous-time framework. We develop a stochastic discrete-time model and propose a rejection algorithm that makes optimal decisions (accept/reject) according to the future expected revenues generated and on the opportunity cost that arises before each sale. For this aspect of the problem, a Monte Carlo approach is used to derive optimal rejection policies. We then extend this approach to show that there exists an equivalent continuous-time methodology that yields to a partial differential equation (PDE). The nature of the PDE, as opposed to the Monte Carlo approach, generates the rejection policies quicker and causes the optimal surfaces to be significantly smoother. However, because the solution to the PDE is considered not to solve the 'full' problem, we propose an approach to generate optimal revenues using the discrete-time model by exploiting the information coming from the PDE. We give a worked example of how to generate near-optimal revenues with an order of magnitude decrease in computation speed.
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ABSTRACT: Reynolds' paper sought to explain the change in character of flow through a pipe from laminar to turbulent that his earlier experiments had shown to occur when the dimensionless group that today bears his name exceeded approximately 2000. This he did by decomposing the velocity into mean and fluctuating components and noting how the average kinetic energy generation and dissipation rates changed with Reynolds number. The paper was only grudgingly accepted by two very distinguished referees and initially raised little external interest. As years went by, however, the averaged form of the equations of motion, known as the Reynolds equations (which were an intermediate stage in Reynolds' analysis) became the acknowledged starting point for computing turbulent flows. Moreover, some 50 years after his paper, a refinement of his strategy for predicting transition was also successfully taken up. For some engineering problems, the continual rapid growth of computing resources has meant that more detailed approaches for computing turbulent flow phenomena can nowadays be employed. However, this growth of computing power likewise makes possible a Reynolds-averaging strategy for complex flow systems in industry or the environment which formerly had to adopt less comprehensive analyses. Thus, Reynolds' approach may well remain in use throughout the present century. This commentary was written to celebrate the 350th anniversary of the journal Philosophical Transactions of the Royal Society.Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 04/2015; 373(2039). DOI:10.1098/rsta.2014.0231
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ABSTRACT: In a fixed-field alternating-gradient (FFAG) accelerator, eliminating pulsed magnet operation permits rapid acceleration to synchrotron energies, but with a much higher beam-pulse repetition rate. Conceived in the 1950s, FFAGs are enjoying renewed interest, fuelled by the need to rapidly accelerate unstable muons for future high-energy physics colliders. Until now a ‘scaling’ principle has been applied to avoid beam blow-up and loss. Removing this restriction produces a new breed of FFAG, a non-scaling variant, allowing powerful advances in machine characteristics. We report on the first non-scaling FFAG, in which orbits are compacted to within 10 mm in radius over an electron momentum range of 12–18 MeV/c. In this strictly linear-gradient FFAG, unstable beam regions are crossed, but acceleration via a novel serpentine channel is so rapid that no significant beam disruption is observed. This result has significant implications for future particle accelerators, particularly muon and high-intensity proton accelerators.
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