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Ayrıntılı Bilimsel Program ve Bildiri Özetleri - YAEM2010

Ayrıntılı Bilimsel Program ve Bildiri Özetleri - YAEM2010

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YAEM 2010<br />

YÖNEYLEM ARAÞTIRMASI VE ENDÜSTRÝ MÜHENDÝSLÝGI 30. ULUSAL KONGRESÝ<br />

takes a leading role in describing the risk attitude of in<strong>ve</strong>stors in<br />

financial models. We discuss optimal in<strong>ve</strong>ntory and risk management<br />

policies in a single period model. We use financial instruments like<br />

futures and derivati<strong>ve</strong>s to hedge the risks associated with the cash<br />

flow. It is known that the demand or supply for the product is often<br />

correlated with some financial or economic indices or financial assets.<br />

Therefore, one can manage the risks invol<strong>ve</strong>d in an in<strong>ve</strong>ntory model<br />

by taking positions in the futures or derivati<strong>ve</strong>s markets for these<br />

instruments. The manager now has to determine the optimal portfolio<br />

of these hedging instruments as well as the optimal order quantity.<br />

Nonparametric Bayesian Replacement Strategies<br />

Refik Soyer<br />

Department of Decision Sciences, George Washington Uni<strong>ve</strong>rsity,<br />

Washington, DC, USA<br />

We present a Bayesian decision theoretic approach for replacement<br />

strategies for systems that are subject to wear. In so doing, we<br />

consider a semi-parametric model to describe the failure<br />

characteristics of the system by specifying a nonparametric form for<br />

cumulati<strong>ve</strong> intensity function and by taking into account effect of<br />

covariates by a parametric form. Use of a gamma process prior for<br />

the cumulati<strong>ve</strong> intensity function complicates the Bayesian analysis<br />

when the updating is based on failure count data. We de<strong>ve</strong>lop a<br />

Bayesian analysis of the model using Markov chain Monte Carlo<br />

(MCMC) methods and determine replacement strategies. Adoption of<br />

MCMC methods invol<strong>ve</strong>s a data augmentation algorithm. We show the<br />

implementation of our approach using actual data.<br />

Reliability, MTTF, and Availability of Systems with Markovian<br />

Missions and Aging<br />

1 2<br />

Bora Çekyay , Süleyman Özekici<br />

1 Faculty of Management, McGill Uni<strong>ve</strong>rsity, Montreal, Quebec, Canada<br />

2 Department of Industrial Engineering, KOC Uni<strong>ve</strong>rsity, Istanbul<br />

We consider a mission-based reliability system that is designed to<br />

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