Many industries use dynamic pricing on an operational level to maximize revenue from selling a fixed capacity over a finite horizon. Classical risk-neutral approaches do not accommodate the risk aversion often encountered in practice. We add to the scarce literature on risk aversion by...

Focuses on a study which developed a framework for forecast and decision horizons. Definition of finite and infinite horizon stochastic optimization problems for a given forecast; Description of the general framework; Conditions for the existence of a solution horizon; Development of sufficient...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...

A version of the classical secretary problem is studied, in which one is interested in selecting one of the b best out of a group of n differently ranked persons who are presented one by one in a random order. It is assumed that b is bigger than or equal to 1 is a preassigned number. It is...

In this paper an algorithm is proposed to find an integral solution of (nonlinear) complementarity problems. The algorithm starts with a nonnegative integral point and generates a unique sequence of adjacent integral simplices of varying dimension. Conditions are stated under which the algorithm...

This paper, using the different alternative methods of dynamic optimization (the Lagrange/Kuhn-Tucker (LKT) method, the substitution method, the Hamiltonian method, and the dynamic programming approach) derives the conditions that must be satisfied by the solution to the so-called Ramsey...

I introduce and evaluate a new stochastic simulation method for dynamic economic models. It is based on recent work in the operations research and engineering literatures (Van Roy et. al, 1997; Powell, 2007; Bertsekas, 2011). The baseline method involves rewriting the household's dynamic program in...

Policy makers constantly face optimal control problems: what controls allow to achieve certain targets in, e.g., GDP growth or inflation? Conventionally this is done by applying certain linear-quadratic optimization algorithms to dynamic econometric models. Several algorithms extend this...

We introduce the market resources method (MRM) for solving dynamic optimization problems. MRM extends Carroll’s (2006) endogenous grid point method (EGM) for problems with more than one control variable using policy function iteration. The MRM algorithm is simple to implement and provides...

Increasing renewable generation results in growing supply uncertainty. By now hydrostorages are the most efficient way of smoothing uncertain power supply. In liberalized and competitive markets the valuation of hydro storages investment projects needs to take the market information and...