Adaptive Light Control System

An adaptive traffic light system for crossroads is to be developed with the control being the data obtained through fixed cameras attached to the light system. The control itself is to be adaptive as there is no need for collecting data during the time when there is no traffic at all. Thus the problem is to collect data adaptively and control the light system accordingly. The idea, of course, is not to have people wait for unnecessary amount of time along the way, while there is no traffic across roads. Though looks rather reasonable, a very good adaptive strategy and an accompanying algorithm need to be developed. The study group is asked for such an algorithm

Up and beyond: Building a mountain in the Netherlands

We discuss the idea of building a 2 km high mountain in the Netherlands. In this paper, we give suggestions on three important areas for the completion of this project. Issues like location, structure and sustainability are investigated and discussed in detail

Up and Beyond - Building a Mountain in the Netherlands

We discuss the idea of building a 2 km high mountain in the Netherlands. In this paper, we give suggestions on three important areas for the completion of this project. Issues like location, structure and sustainability are investigated and discussed in detail

Optimal control of work-in-process inventory of a two-station production line

Most production lines keep a minimal level of inventory stock to save storage costs and buffer space. However, the random nature of processing, breakdown, and repair times can significantly affect the efficiency of a production line and force the stocking of work-in-process inventory. We are interested in the case when starvation and blockage are preferentially avoided. In this study, a mathematical model has been developed using asymptotic approximation and simulation that provides asymptotic results for the expected value and the variance of the stock level in a buffer as a function of time. In addition, the functional relationship between buffer capacity and the first stopping time caused by starvation or blockage has been determined. Copyright © 2009 John Wiley & Sons, Ltd

On the limiting behavior of the characteristic function of the ergodic distribution of the semi-Markov walk with two boundaries

The semi-Markov walk (X(t)) with two boundaries at the levels 0 and beta > 0 is considered. The characteristic function of the ergodic distribution of the processX(t) is expressed in terms of the characteristics of the boundary functionals N(z) and S (N)(z), where N(z) is the firstmoment of exit of the random walk {Sn}, n >= 1, from the interval (-z, beta - z), z a [0, beta]. The limiting behavior of the characteristic function of the ergodic distribution of the process W (beta) (t) = 2X(t)/beta - 1 as beta -> a is studied for the case in which the components of the walk (eta (i)) have a two-sided exponential distribution.[Aliyev, R. T.] Baku State Univ, Baku, Azerbaijan; [Aliyev, R. T.; Khaniyev, T. A.] Natl Acad Sci, Guseynov Inst Cybernet, Baku, Azerbaijan; [Khaniyev, T. A.] TOBB Econ & Technol Univ, Ankara, Turke

An asymptotic approach for a semi-Markovian inventory model of type (s, S)

In this study, we constructed a stochastic process (X(t)) that expresses a semi-Markovian inventory model of type (s, S) and it is shown that this process is ergodic under some weak conditions. Moreover, we obtained exact and asymptotic expressions for the nth order moments (n = 1,2,3,.) of ergodic distribution of the process X(t), as S - s › ?. Finally, we tested how close the obtained approximation formulas are to the exact expressions. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd

Asymptotic expansions for the moments of a semi-Markovian random walk with exponential distributed interference of chance

In this paper, a semi-Markovian random walk process (X (t)) with a discrete interference of chance is constructed and the ergodicity of this process is discussed. Some exact formulas for the first- and second-order moments of the ergodic distribution of the process X (t) are obtained, when the random variable ?1 has an exponential distribution with the parameter ? > 0. Here ?1 expresses the quantity of a discrete interference of chance. Based on these results, the third-order asymptotic expansions for mathematical expectation and variance of the ergodic distribution of the process X (t) are derived, when ? › 0. © 2007 Elsevier B.V. All rights reserved

On The Moments For Ergodic Distribution Of An Inventory Model Of Type (S, S) With Regularly Varying Demands Having Infinite Variance

In this study a stochastic process X(t) which represents a semi Markovian inventory model of type (s,S) has been considered in the presence of regularly varying tailed demand quantities. The main purpose of the current study is to investigate the asymptotic behavior of the moments of ergodic distribution of the process X(t) when the demands have any arbitrary distribution function from the regularly varying subclass of heavy tailed distributions with in finite variance. In order to obtain renewal function generated by the regularly varying random variables, we used a special asymptotic expansion provided by Geluk [14]. As a first step we investigate the current problem with the whole class of regularly varying distributions with tail parameter 1 < alpha < 2 rather than a single distribution. We obtained a general formula for the asymptotic expressions of nth order moments (n = 1, 2, 3, ...) of ergodic distribution of the process X(t). Subsequently we consider this system with Pareto distributed demand random variables and apply obtained results in this special case.[Kamislik, A. Bektas] Recep Tayyip Erdogan Univ, Fac Arts & Sci, Dept Math, Rize, Turkey; [Kesemen, T.] Karadeniz Tech Univ, Fac Sci, Dept Math, Trabzon, Turkey; [Khaniyev, T.] TOBB Univ Econ & Technol, Fac Engn, Dept Ind Engn, Ankara, Turke

A robust estimation model for surgery durations with temporal, operational, and surgery team effects

Due to copyright restrictions, the access to the full text of this article is only available via subscription.For effective operating room (OR) planning, surgery duration estimation is critical. Overestimation leads to underutilization of expensive hospital resources (e.g., OR time) whereas underestimation leads to overtime and high waiting times for the patients. In this paper, we consider a particular estimation method currently in use and using additional temporal, operational, and staff-related factors provide a statistical model to adjust these estimates for higher accuracy. The results show that our method increases the accuracy of the estimates, in particular by reducing large errors. For the 8093 cases we have in our data, our model decreases the mean absolute deviation of the currently used scheduled duration (42.65 ± 0.59 minutes) by 1.98 ± 0.28 minutes. For the cases with large negative errors, however, the decrease in the mean absolute deviation is 20.35 ± 0.74 minutes (with a respective increase of 0.89 ± 0.66 minutes in large positive errors). We find that not only operational and temporal factors, but also medical staff and team experience related factors (such as number of nurses and the frequency of the medical team working together) could be used to improve the currently used estimates. Finally, we conclude that one could further improve these predictions by combining our model with other good prediction models proposed in the literature. Specifically, one could decrease the mean absolute deviation of 39.98 ± 0.58 minutes obtained via the method of Dexter et al (Anesth Analg 117(1):204–209, 2013) by 1.02 ± 0.21 minutes by combining our method with theirs.Hewlett-Packard Compan

Asymptotic expansions for the moments of a semi-Markovian random walk with exponential distributed interference of chance

In this paper, a semi-Markovian random walk process (X(t)) with a discrete interference of chance is constructed and the ergodicity of this process is discussed. Some exact formulas for the first- and second-order moments of the ergodic distribution of the process X(t) are obtained, when the random variable [zeta]1 has an exponential distribution with the parameter [lambda]>0. Here [zeta]1 expresses the quantity of a discrete interference of chance. Based on these results, the third-order asymptotic expansions for mathematical expectation and variance of the ergodic distribution of the process X(t) are derived, when [lambda]-->0.Random walk First jump Ergodic distribution Asymptotic expansion Ladder variable Discrete interference of chance