An application of the MTP2 property on bounds on system reliability

This paper is concerned with the joint prior distribution of the dependent reliabilities of the components of a binary system. When this distribution is MTP₂ (Multivariate Totally Positive of Order 2), it is shown in general that this actually makes the machinery of Natvig and Eide [7] available to arrive at the posterior distribution of the system's reliability, based on data both at the component and system level. As an illustration in a common environmental stress case, the joint prior distribution of the reliabilities is shown to have the MTP₂ property. We also show, similarly to Gåsemyr and Natvig [3], for the case of independent components given component reliabilities how this joint prior distribution may be based on the combination of expert opinions. A specific system is finally treated numerically. © 1997 John Wiley & Sons, Inc. Naval Research Logistics 44: 741–755, 1997     

How Is a Man to Decide? Unjust Combatants,Duress and McMahan’s Killing in War

Jeff McMahan’s much-discussed work Killing in War is an important part of the revisionist school of just war studies. This paper avoids discussion of McMahan’s use of human rights and examines the practical consequences of his argument about duress on soldiers to fight an unjust war. These arguments are found to be wanting and to be impractical ones that do not fit battlefield realities. The importance of the Law of Armed Conflict and the legal equality of combatants that is part of it is emphasised and accepted as the most practical way of regulating battlefield behaviour and saving lives. It is concluded that attempts to tell soldiers what to do when they may be fighting an unjust war add to their burdens and are misplaced.     

Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

Ordering and controlling the dimensions of strategy

Colin Gray’s dimensions of strategy, built upon the earlier work of Clausewitz and Howard, has theoretical, practical, and pedagogic value for the Strategic Studies community. This paper further develops the theory, arguing that the dimensions can be controlled or managed to improve strategic performance. The dimensions are divided into two categories: “internal” and “external” to the process of strategy. The paper describes the dynamic process by which the internal dimensions, which can be controlled, are used to exert influence over the external dimensions that are beyond the control of the strategist. It is argued that six dimensions hold the key to strategic success: politics and the five dimensions concerned with warfighting. This conclusion validates the Clausewitzian paradigm with its emphasis on policy and battle.     

Transit vessel scheduling

A transit vessel traffic scheduling algorithm has been developed to limit the negative effects on cargo volume throughput in two‐way waterways where separation distances between transiting vessels must be maintained and passage restrictions may hold. It runs in time that is polynomial in the number of ships involved in the computation and finds schedules which increase the utilization of waterways. Three examples illustrate its use. The first example is situated in the Sunda Strait where the algorithm is used to enhance the safety of merchant shipping against a terrorist threat. It illustrates important features of the algorithm and demonstrates how it can be used with cross traffic. The second example is situated in the Strait of Istanbul and offers a comparison between the developed algorithm and the transit vessel scheduling algorithm of Ulusçu et al., J Navig 62 (2009), 59–77. This was done using a plausible model of the Strait of Istanbul. The third and last example shows how the algorithm can be used to schedule transit vessel traffic in two‐way waterways with junctions. This feature is especially useful in congested waters with a high risk of collisions like the Inland Sea of Japan. An extreme test case proves that the developed algorithm is a practical algorithm ready for such use. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 225–248, 2017     

Interval scheduling: A survey

In interval scheduling, not only the processing times of the jobs but also their starting times are given. This article surveys the area of interval scheduling and presents proofs of results that have been known within the community for some time. We first review the complexity and approximability of different variants of interval scheduling problems. Next, we motivate the relevance of interval scheduling problems by providing an overview of applications that have appeared in literature. Finally, we focus on algorithmic results for two important variants of interval scheduling problems. In one variant we deal with nonidentical machines: instead of each machine being continuously available, there is a given interval for each machine in which it is available. In another variant, the machines are continuously available but they are ordered, and each job has a given “maximal” machine on which it can be processed. We investigate the complexity of these problems and describe algorithms for their solution. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Note: Facility location with closest rectangular distances

This paper considers the problem of locating one or more new facilities on a continuous plane, where the destinations or customers, and even the facilities, may be represented by areas and not points. The objective is to locate the facilities in order to minimize a sum of transportation costs. What is new in this study is that the relevant distances are the distances from the closest point in the facility to the closest point in the demand areas. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 77–84, 2000