Stationary and Time-Dependent Optimization of the Casino Floor Slot Machine Mix

Modeling and optimizing the performance of a mix of slot machines on a gaming floor can be addressed at various levels of coarseness, and may or may not consider time-dependent trends. For example, a model might consider only time-averaged, aggregate data for all machines of a given type; time-dependent aggregate data; time-averaged data for individual machines; or fully time dependent data for individual machines. Fine-grained, time-dependent data for individual machines offers the most potential for detailed analysis and improvements to the casino floor performance, but also suffers the greatest amount of statistical noise. We present a theoretical analysis of single and multi-objective optimization methods that address the casino floor optimization problem at all levels of coarseness, considering both linear and non-linear formulations of the problem. We also address the impact of statistical noise and time-dependent trends on solutions, using both Gaussian and non-Gaussian distributions to model the performance of individual machines. We show that advanced methods from evolutionary computing can track trends in performance and continually adjust the optimal mix of machines, potentially allowing an operator to respond rapidly to customer preferences, and allowing a property to operate continuously near the optimal mix

Probing microscopic models for system-bath interactions via parametric driving

We show that strong parametric driving of a quantum harmonic oscillator coupled to a thermal bath allows one to distinguish between different microscopic models for the oscillator-bath coupling. We consider a bath with an Ohmic spectral density and a model where the system-bath interaction can be tuned continuously between position and momentum coupling via the coupling angle $\alpha$. We derive a master equation for the reduced density operator of the oscillator in Born-Markov approximation and investigate its quasi-steady state as a function of the driving parameters, the temperature of the bath and the coupling angle $\alpha$. We find that the time-averaged variance of position and momentum exhibits a strong dependence on these parameters. In particular, we identify parameter regimes that maximise the $\alpha$-dependence and provide an intuitive explanation of our results.Comment: 13 pages, 8 figure

Optical control of the current-voltage relation in stacked superconductors

We simulate the current-voltage relation of short layered superconductors, which we model as stacks of capacitively coupled Josephson junctions. The system is driven by external laser fields, in order to optically control the voltage drop across the junction. We identify parameter regimes in which supercurrents can be stabilised against thermally induced phase slips, thus reducing the effective voltage across the superconductor. Furthermore, single driven Josephson junctions are known to exhibit phase-locked states, where the superconducting phase is locked to the driving field. We numerically observe their persistence in the presence of thermal fluctuations and capacitive coupling between adjacent Josephson junctions. Our results indicate how macroscopic material properties can be manipulated by exploiting the large optical nonlinearities of Josephson plasmons.Comment: 7 pages, 7 figure

Passenger Flows in Underground Railway Stations and Platforms, MTI Report 12-43

Urban rail systems are designed to carry large volumes of people into and out of major activity centers. As a result, the stations at these major activity centers are often crowded with boarding and alighting passengers, resulting in passenger inconvenience, delays, and at times danger. This study examines the planning and analysis of station passenger queuing and flows to offer rail transit station designers and transit system operators guidance on how to best accommodate and manage their rail passengers. The objectives of the study are to: 1) Understand the particular infrastructural, operational, behavioral, and spatial factors that affect and may constrain passenger queuing and flows in different types of rail transit stations; 2) Identify, compare, and evaluate practices for efficient, expedient, and safe passenger flows in different types of station environments and during typical (rush hour) and atypical (evacuations, station maintenance/ refurbishment) situations; and 3) Compile short-, medium-, and long-term recommendations for optimizing passenger flows in different station environments