PASTA: A State-Space Representation for In-Line Estimation of Time-Varying Parameters of Market Response Models

2014 ◽  
Author(s):  
Noah Tilzer ◽  
Nazrul I. Shaikh
Keyword(s):  
State Space ◽  
Space Representation ◽  
Market Response ◽  
Time Varying ◽  
Response Models ◽  
Varying Parameters ◽  
State Space Representation ◽  
Time Varying Parameters ◽  
Estimation Of Time

APPROXIMATE DYNAMIC PROGRAMMING TECHNIQUES FOR THE CONTROL OF TIME-VARYING QUEUING SYSTEMS APPLIED TO CALL CENTERS WITH ABANDONMENTS AND RETRIALS

2009 ◽  
Vol 24 (1) ◽  
pp. 27-45 ◽  
Author(s):  
Dennis Roubos ◽  
Sandjai Bhulai
Keyword(s):  
Dynamic Programming ◽  
State Space ◽  
Approximate Dynamic Programming ◽  
Retrial Queue ◽  
The State ◽  
Space Representation ◽  
Time Varying ◽  
Queuing Systems ◽  
Varying Parameters ◽  
Time Varying Parameters

In this article we develop techniques for applying Approximate Dynamic Programming (ADP) to the control of time-varying queuing systems. First, we show that the classical state space representation in queuing systems leads to approximations that can be significantly improved by increasing the dimensionality of the state space by state disaggregation. Second, we deal with time-varying parameters by adding them to the state space with an ADP parameterization. We demonstrate these techniques for the optimal admission control in a retrial queue with abandonments and time-varying parameters. The numerical experiments show that our techniques have near to optimal performance.

Adaptive Estimation of Time-Varying Parameters in Linearly Parametrized Systems

2005 ◽  
Vol 128 (3) ◽  
pp. 691-695 ◽  
Author(s):  
Yongliang Zhu ◽  
Prabhakar R. Pagilla
Keyword(s):  
Adaptive Estimation ◽  
Parameter Estimate ◽  
Time Varying ◽  
Time Intervals ◽  
Varying Parameters ◽  
Time Varying Parameter ◽  
Time Varying Parameters ◽  
Parametrized Systems ◽  
Simulation Results ◽  
Estimation Of Time

Adaptive estimation of time-varying parameters in linearly parametrized systems is considered. The estimation time is divided into small intervals; in each interval the time-varying parameter is approximated by a time polynomial with unknown coefficients. A condition for resetting of the parameter estimate at the beginning of each interval is derived; the condition guarantees that the estimate of the time-varying parameter is continuous and also allows for the coefficients of the polynomial to be different in various time intervals. A modified version of the least-squares algorithm is provided to estimate the time-varying parameters. Stability of the proposed algorithm is shown and discussed. Simulation results on an example are given to validate the proposed method.

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