Optimal ordering policies for a product that perishes in two periods subject to stochastic demand

This paper considers the problem of computing optimal ordering policies for a product that has a life of exactly two periods when demand is random. Initially costs are charged against runouts (stockouts) and outdating (perishing). By charging outdating costs according to the expected amount of outdating one period into the future, a feasible one period model is constructed. The central theorem deals with the n-stage dynamic problem and demonstrates the appropriate cost functions are convex in the decision variable and also provides bounds on certain derivatives. The model is then generalized to include ordering and holding costs. The paper is concluded with a discussion of the infinite horizon problem.     

Countering the effects of IED systems in Afghanistan: an integral approach

The current approach to countering the threat of improvised explosive devices (IEDs) in Afghanistan has met with limited success. The relative lack of success may be in part because the current approach is not holistic and discounts the social systems that foster the IEDs. Insurgents are using IEDs as a tool to further their strategic aims, but the coalition and to a lesser extent the Government of the Islamic Republic of Afghanistan (GIRoA) are attacking the IEDs as if they are an end in and of themselves. Combining network disruption with information change maximizes the opportunities for mitigating the IED threat. More specifically, to mitigate the IED threat requires disrupting the social and economic systems associated with IEDs, and at the same time providing alternative economic opportunities and improving rule of law and governance at the local level. In other words, counter-IED (C-IED) must be Counter-Insurgency (COIN) centric to be successful. This paper reviews the current state of C-IED efforts, identifies five main problems with the current approach, and suggests changes to reduce or mitigate the IED threat in Afghanistan.     

Demand estimation in lost sales inventory systems

This article considers the problem of estimating parameters of the demand distribution in lost sales inventory systems. In periods when lost sales occur demand is not observed; one knows only that demand is larger than sales. We assume that demands form a sequence of IID normal random variables, which could be a residual demand process after filtering out seasonality and promotional nonstationarities. We examine three estimators for the mean and standard deviation: maximum likelihood estimator, BLUE (best linear unbiased estimator), and a new estimator derived here. Extensive simulations are reported to compare the performance of the estimators for small and large samples and a variety of parameter settings. In addition, I show how all three estimators can be incorporated into sequential updating routines. © 1994 John Wiley & Sons, Inc.     

A lagrangean relaxation algorithm for the constrained matrix problem

We present variants of a convergent Lagrangean relaxation algorithm for minimizing a strictly convex separable quadratic function over a transportation polytope. The algorithm alternately solves two “subproblems,” each of which has an objective function that is defined by using Lagrange multipliers derived from the other. Motivated by the natural separation of the subproblems into independent and very easily solved “subsubproblems,” the algorithm can be interpreted as the cyclic coordinate ascent method applied to the dual problem. We exhibit our computational results for different implementations of the algorithm applied to a set of large constrained matrix problems.     

On ordering perishable inventory under erlang demand

Existing models for describing optimal ordering policies for perishable inventory cast the problem as a multidimensional dynamic program, the dimensionality being one less than the product lifetime in periods. An approach developed in previous work takes explicit account of outdating in the single period model. Formulas for the expected quantity of any new order which will outdate are developed for the case where the demand has a stationary Erlang distribution. A modified version of the one period model is shown to yield a reasonable approximation to the stationary optimal policy.     

Replacing nonidentical vital components to extend system life

We consider a system that depends on a single vital component. If this component fails, the system life will terminate. If the component is replaced before its failure then the system life may be extended; however, there are only a finite number of spare components. In addition, the lifetimes of these spare components are not necessarily identically distributed. We propose a model for scheduling component replacements so as to maximize the expected system survival. We find the counterintuitive result that when comparing components' general lifetime distributions based on stochastic orderings, not even the strongest ordering provides an a priori guarantee of the optimal sequencing of components. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008     

An efficient heuristic for the multi-item newsboy problem with a single constraint

This article treats the problem of determining optimal and approximately optimal order quantities for a multiple-item inventory system subject to a single constraint on space or budget. Although this problem can be solved by the usual method of Lagrange multipliers, we wish to consider a more efficient scheme that requires fewer computations. We provide calculations that compare and contrast four approximation techniques. In particular, we have discovered a method that yields a direct algebraic expression of the problem parameters for allocation and achieves an expected profit within 90% of the optimal in about 90% of the cases tested.