This paper studies a problem of determining the level of certain decisions, taken prior to certain events taking place, and the subsequent additional resource procurement decisions needed to implement the initial program once these events have materialised. The problem is formulated first of all...

Drawing on recent developments in discrete time fixed income options theory, we propose a stochastic programming procedure, which we call stochastic dedication, for managing asset/liability portfolios with interest rate contingent claims. The model uses scenario generation to combine...

We present a variant of Karmarkar's algorithm for block-angular structured linear programs, such as stochastic linear programs. By computing the projection efficiently, we give a worst-case bound on the order of the running time that can be an order of magnitude better than that of Karmarkar's...

We consider a stochastic version of the Stackelberg-Nash-Cournot model proposed by Murphy et al. (Murphy, F. H., H. D. Sherali, A. L. Soyester. 1983. Stackelberg-Nash-Cournot equilibria characterizations and computations. Oper. Res. 31 253--276.). In the first stage, the leader chooses and...

Some important conceptual problems concerning the application of chance constrained programming (CCP) to risky practical decision problems are discussed by comparing CCP to stochastic programming with recourse (SPR). We expand on Garstka's distinction between mathematical equivalence and...

We consider the problem of optimizing inventories for problems where the demand distribution is unknown, and where it does not necessarily follow a standard form such as the normal. We address problems where the process of deciding the inventory, and then realizing the demand, occurs repeatedly....

This paper considers a multicomponent, multiproduct periodic-review assemble-to-order (ATO) system that uses an independent base-stock policy for inventory replenishment. Product demands in each period are integer-valued correlated random variables, with each product being assembled from...

We consider nonlinear programming problem (P) with stochastic constraints. The Lagrangean corresponding to such problems has a stochastic part, which in this work is replaced by its certainty equivalent (in the sense of expected utility theory). It is shown that the deterministic surrogate...

Practical portfolio investment problems under uncertainty can be modeled well as multiperiod stochastic programs. However, the numerical optimization methods that need to be used to solve such models seriously limit the level of detail in the uncertainty about future asset prices and returns...