Computation of approximate optimal policies in a partially observed inventory model with rain checks

This paper proposes a new methodology to solve partially observed inventory problems. Generally, these problems have infinite-dimensional states that are conditional distributions of the inventory level. Our methodology involves linearizing the state transitions via unnormalized probabilities. It then uses an appropriate functional basis to represent the state. Considering the speed and stability of computations, we choose truncated Chebyshev polynomials as the basis. We use Fast Fourier Transforms along with an appropriate discretization of inventory levels to speed up the computations. These main ideas are blended to obtain an iterative algorithm to solve a partially observed inventory model with rain checks. In this model, the inventory manager (IM) does not know the inventory level when it is positive. Otherwise, the IM fully observes it. This model provides a context to illustrate our methodology, which applies to other such models. Although this model has been studied mathematically in the literature, the use of our algorithm provides a numerical approximation of the optimal order quantities. These are compared to the orders released under a base mean-stock policy, where the IM replaces the unobserved inventory level with its mean and applies the well-known base stock policy. We show numerically that the optimal order quantity is very close to the base mean-stock order quantity, when the variance of the inventory distribution is small. When the mean of the inventory distribution is large, the optimal order quantity is more than the base mean-stock quantity, and it is the other way around when the mean is small or negative. These insights are explained via uncertainty and information effects and their interplay. We expect this interplay to show up in other partially observed inventory models.     

Molecular dynamics simulation of compressible hot/cold moving lid-driven microcavity flow

Hard-sphere molecular dynamics simulations of lid-driven microcavity gas flow with various subsonic speeds and lid temperatures are conducted. Simulations with faster and colder lids show streamlines of stronger primary vortices. Variations of mass and energy centers with respect to lid speed and temperature are examined. Center of energy is less sensitive to employed lid conditions than center of gravity is. Although moving lid imparts energy into fluid, due to change of impingement rates on the walls of fixed temperature, average energy within the cavity seems quite insensitive to the subsonic lid speed. Behavior of compressibility at both top corners is observed even at low Mach numbers widely considered within incompressible flow region. While high Knudsen number causes considerable property slips near the lid, two-dimensional pressure, density, and temperature plots of excellent quality are generated. Results are promising in use of molecular dynamics simulations for compressible vortex flow analyses while providing insights for understanding microfluidics and nanofluidics in context of molecular mass, momentum and heat transfer in microscale and nanoscale systems.     

Fuzzy axiomatic design extension for managing model selection paradigm in decision science

The model selection paradigm is one of the focused themes within decision science. This paper addresses the consistent solution of model selection issue on the basis of the fuzzy axiomatic design (FAD) methodology. Moreover, the developed FAD–based model selection interface (FAD–MSI) is performed over the critical ship management processes in order to assign suitable (multiple criteria decision-making) MCDM techniques even if commonly utilized hybrid approaches. The outcomes of this study encourage the maritime practitioners for the further researches towards analytical modelling of ship management processes.     

EKF Based Attitude Estimation and Stabilization of a Quadrotor UAV Using Vanishing Points in Catadioptric Images

In recent years, Unmanned Air Vehicles (UAVs) have become more and more important. These vehicles are employed in many applications from military operations to civilian tasks. Under situations where global positioning system (GPS) and inertial navigation system (INS) do not function, or as an additional sensor, computer vision can be used. Having 360° view, catadioptric cameras might be very useful as they can be used as measurement units, obstacle avoidance sensors or navigation planners. Although many innovative research has been done about this camera, employment of such cameras in UAVs is very new. In this paper, we present the use of catadioptric systems in UAVs to estimate vehicle attitude using parallel lines that exist on many structures in an urban environment. After explanation of the algorithm, the UAV modeling and control will be presented. In order to increase the estimation and control speed an Extended Kalman Filter (EKF) and multi-threading are used and speeds up to 40 fps are obtained. Various simulations have been done to present the effectiveness of the estimation algorithms as well as the UAV controllers. A custom test stand has been designed to perform successful experiments on the UAV. Finally, we will present the experiments and the results of the estimation and control algorithms on a real model helicopter. EKF based attitude estimation and stabilization using catadioptric images has found to be a reliable alternative to other sensor usage.     

Parasitic compensation in CCI-based circuits for reduced power consumption

In this paper, the compensation advantage of the first-generation current conveyor (CCI) over the second-generation current conveyor (CCII) in tunable circuits is shown. For this purpose, a new floating frequency dependent negative resistor (FDNR) simulator using three CCIs is presented and employed in a third-order high-pass filter. The compensation feature of the CCI is shown for the proposed high-pass filter. As a second example, the presented compensation method is tested in a second-order band-pass filter constructed with two CCIs. Applying the proposed compensation technique, the CCI-based circuits can operate in lower biasing currents, which result in lower power consumption.     

Cholesteric Liquid Crystal Polymeric Coatings for Colorful Artificial Muscles and Motile Humidity Sensor Skin Integrated with Magnetic Composites

Structural colorful cholesterics show impressive susceptibility to external stimulation, leading to applications in electro/mechano-chromic devices. However, out-of-plane actuation of structural colorful actuators based on cholesterics and the integration with other stimulation remains underdeveloped. Herein, colorful actuators and motile humidity sensors are developed using humidity-responsive cholesteric liquid crystal networks (CLCNs) and magnetic composites. The developed colorful actuator can exhibit synergistic out-of-plane shape morphing and color change in response to humidity, with CLCNs as colorful artificial muscles. Through the integration with magnetic control, the motile sensor can be navigated to open and confined spaces with the aid of friction to detect local relative humidity. The integration of multi-stimulation actuation of cholesteric magnetic actuators will expand the research frontier of structural colorful actuators and motile sensors for confined spaces.     

Planning of capacity,production and inventory decisions in a generic reverse supply chain under uncertain demand and returns

There is a growing interest for the design and operation of reverse supply chain systems due to the cost and the legislation issues. In this paper, we address the disassembly, refurbishing and production operations in a reverse supply chain setting for modular products such as computers and mobile phones considering the uncertainties in this system, which are the return amounts of the used products and demand for final products. We develop a large-scale mixed integer programming model in order to capture all characteristics of this system, and use two-stage stochastic optimisation and robust optimisation approaches to analyse the system behaviour. In the first stage, we focus on the strategic decisions about the capacities at disassembly and refurbishing sites considering different scenarios regarding the uncertainties in the system. In the second stage, we analyse the operational decisions such as production, inventory and disposal rates. We observe through our extensive numerical analysis that the randomness of demand and return values effect the performance of the system substantially and the uncertainty of the return amounts of used products is much more important than the uncertainty of demand in this system.