Adaptive control of a production-inventory system

1981 ◽  
Vol 18 (1) ◽  
pp. 204-215 ◽  
Author(s):  
Bharat T. Doshi
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Properties ◽  
Inventory System ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Unknown Parameters ◽  
True Value ◽  
Adaptive Policy ◽  
Production Inventory ◽  
Production Inventory System

We study a production-inventory system in which input is deterministic, and its rate is the controlled parameter. The output is a compound Poisson process with exponential jump-size distribution. The parameters of the output process are unknown. The following adaptive policy is used: At each time t the unknown parameters are estimated by maximum likelihood method and the input rate which would be optimal if these were the true values of the parameter is used. The issues investigated are (1) the convergence of maximum likelihood estimates to the true value, and (2) the asymptotic properties of the cost under the adaptive policy.

Adaptive control of a production-inventory system

1981 ◽  
Vol 18 (01) ◽  
pp. 204-215
Author(s):  
Bharat T. Doshi
Keyword(s):  
Maximum Likelihood ◽  
Asymptotic Properties ◽  
Inventory System ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Unknown Parameters ◽  
True Value ◽  
Adaptive Policy ◽  
Production Inventory ◽  
Production Inventory System

We study a production-inventory system in which input is deterministic, and its rate is the controlled parameter. The output is a compound Poisson process with exponential jump-size distribution. The parameters of the output process are unknown. The following adaptive policy is used: At each time t the unknown parameters are estimated by maximum likelihood method and the input rate which would be optimal if these were the true values of the parameter is used. The issues investigated are (1) the convergence of maximum likelihood estimates to the true value, and (2) the asymptotic properties of the cost under the adaptive policy.

scholarly journals An algorithm for the assessment of several small rates

2012 ◽  
Vol 53 ◽  
Author(s):  
Leonidas Sakalauskas ◽  
Ingrida Vaičiulytė
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Approach ◽  
Maximum Likelihood Method ◽  
Rare Events ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Empirical Bayesian ◽  
Empirical Bayesian Approach

The present paper describes the empirical Bayesian approach applied in the estimation of several small rates. Modeling by empirical Bayesian approach the probabilities of several rare events, it is assumed that the frequencies of events follow to Poisson’s law with different parameters, which are correlated Gaussian random values. The unknown parameters are estimated by the maximum likelihood method computing the integrals appeared here by Hermite–Gauss quadratures. The equations derived that are satisfied by maximum likelihood estimates of model parameters.

Metric for Disassembly and Reuse Decisions: Formulation and Validation

2008 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Deborah Thurston
Keyword(s):  
Maximum Likelihood ◽  
Value Function ◽  
Choice Theory ◽  
Decision Makers ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Maximum Value ◽  
Long Range Planning ◽  
The Relationship ◽  
The Value Function

Design for disassembly and reuse focuses on developing methods to minimize difficulty in disassembly for maintenance or reuse. These methods can gain substantially if the relationship between component attributes (material mix, ease of disassembly etc.) and their likelihood of reuse or disposal is understood. For products already in the marketplace, a feedback approach that evaluates willingness of manufacturers or customers (decision makers) to reuse a component can reveal how attributes of a component affect reuse decisions. This paper introduces some metrics and combines them with ones proposed in literature into a measure that captures the overall value of a decision made by the decision makers. The premise is that the decision makers would choose a decision that has the maximum value. Four decisions are considered regarding a component’s fate after recovery ranging from direct reuse to disposal. A method on the lines of discrete choice theory is utilized that uses maximum likelihood estimates to determine the parameters that define the value function. The maximum likelihood method can take inputs from actual decisions made by the decision makers to assess the value function. This function can be used to determine the likelihood that the component takes a certain path (one of the four decisions), taking as input its attributes, which can facilitate long range planning and also help determine ways reuse decisions can be influenced.

scholarly journals Robust parametric estimates of inhomogeneous experimental data

2020 ◽  
pp. 55-62
Author(s):  
V.A. Simakhin ◽  
◽  
L.G. Shamanaeva ◽  
A.E. Avdyushina ◽  
◽  
...  
Keyword(s):  
Experimental Data ◽  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Robust Estimates

In the present work, a weighed maximum likelihood method (WMLM) is proposed to obtain robust estimates for processing experimental data containing outliers. The method allows robust asymptotic unbiased and effective estimates to be obtained in the presence of not only external, but also internal asymmetric and symmetric outliers. Algorithms for obtaining robust WMLM estimates are considered at the parametric level of aprioristic uncertainty. It is demonstrated that these estimates converge to maximum likelihood estimates of an inhomogeneous sample for each distribution from the Tukey supermodel.

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