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Dynamic Pricing and Inventory Control: Robust vs. Stochastic Uncertainty Models
 

Summary: Dynamic Pricing and Inventory Control:
Robust vs. Stochastic Uncertainty Models
A Computational Study
Elodie Adida
and Georgia Perakis
August 2008, revised April 2009, December 2009
Abstract
In this paper, we consider a variety of models for dealing with demand uncertainty for a
joint dynamic pricing and inventory control problem in a make-to-stock manufacturing system.
We consider a multi-product capacitated, dynamic setting, where demand depends linearly on
the price. Our goal is to address demand uncertainty using various robust and stochastic opti-
mization approaches. For each of these approaches, we first introduce closed-loop formulations
(adjustable robust and dynamic programming), where decisions for a given time period are made
at the beginning of the time period, and uncertainty unfolds as time evolves. We then describe
models in an open-loop setting, where decisions for the entire time horizon must be made at time
zero. We conclude that the affine adjustable robust approach performs well (when compared to
the other approaches such as dynamic programming, stochastic programming and robust open
loop approaches) in terms of realized profits and protection against constraint violation while
at the same time it is computationally tractable. Furthermore, we compare the complexity of
these models and discuss some insights on a numerical example.

  

Source: Adida, Elodie - Department of Mechanical and Industrial Engineering, University of Illinois at Chicago

 

Collections: Engineering