Optimal ordering policies when anticipating parameter changes in EOQ systems

The classical Economic Order Quantity Model requires the parameters of the model to be constant. Some EOQ models allow a single parameter to change with time. We consider EOQ systems in which one or more of the cost or demand parameters will change at some time in the future. The system we examine has two distinct advantages over previous models. One obvious advantage is that a change in any of the costs is likely to affect the demand rate and we allow for this. The second advantage is that often, the times that prices will rise are fairly well known by announcement or previous experience. We present the optimal ordering policy for these inventory systems with anticipated changes and a simple method for computing the optimal policy. For cases where the changes are in the distant future we present a myopic policy that yields costs which are near-optimal. In cases where the changes will occur in the relatively near future the optimal policy is significantly better than the myopic policy.     

Survey of approaches to readiness

About thirty references that feature naval logistics environments are considered. All are unclassified and all appear in the open literature or are available from the Defense Logistics Studies Information Exchange. Three approaches are identified–data analysis, theoretical models, and readiness indexes–and conclusions are presented as to possibilities for answering two questions: (a) Can the unit do the job? (b) How does readiness depend on resources? Four cases are treated in detail to illustrate methodology.     

A cutting plane algorithm for the bilinear programming problem

In this paper we discuss the properties of a Bilinear Programming problem, and develop a convergent cutting plane algorithm. The cuts involve only a subset of the variables and preserve the special structure of the constraints involving the remaining variables. The cuts are deeper than other similar cuts.     

Optimal investment,pricing and replacement of computer resources

The problem of multiple-resource capacity planning under an infinite time horizon is analyzed using a nonlinear programming model. The analysis generalizes to the long term the short-run pricing model for computer networks developed in Kriebel and Mikhail [5]. The environment assumes heterogeneous resource capacities by age (vingate), which service a heterogeneous and relatively captive market of users with known demand functions in each time period. Total variable operating costs are given by a continuous psuedoconcave function of system load, capacity, and resource age. Optimal investment, pricing, and replacement decision rules are derived in the presence of economies of scale and exogenous technological progress. Myopic properties of the decision rules which define natural (finite) planning subhorizons are discussed.     

Optimal sample size allocation for tests with multiple levels of stress with extreme value regression

In this article, we discuss the optimal allocation problem in a multiple stress levels life‐testing experiment when an extreme value regression model is used for statistical analysis. We derive the maximum likelihood estimators, the Fisher information, and the asymptotic variance–covariance matrix of the maximum likelihood estimators. Three optimality criteria are defined and the optimal allocation of units for two‐ and k‐stress level situations are determined. We demonstrate the efficiency of the optimal allocation of units in a multiple stress levels life‐testing experiment by using real experimental situations discussed earlier by McCool and Nelson and Meeker. Monte Carlo simulations are used to show that the optimality results hold for small sample sizes as well. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Supply licensing when the manufacturer strategically commits to invest in R&D

This article proposes a strategic reason for a proprietary component supplier to license her technology to a competitor or a manufacturer: her anticipation of the manufacturer's strategic commitment to invest in research and development (R&D). We address this phenomenon with a game theoretic model. Our results show that the manufacturer's full commitment to invest in R&D enables the supplier to license, sell a larger quantity through the supply chain, and charge lower prices. These results are robust to the type of demand uncertainty faced by the manufacturer within the class of increasing generalized failure rate distributions. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 341–350, 2014     

The “motivated bias” dilemma in warfare and intelligence

The goal of this article is to challenge the assumption of rationality in the behavior of decision-making units involved in security, defense, intelligence and warfare and to consider the influence of “motivated bias” in such instances. A review of motivational literature within international politics and a discussion of literature applying “motivated biases” to warfare and strategic surprise will offer an alternative view of the primacy of rationality in such decisions.