Route optimization for multiple searchers

We consider a discrete time‐and‐space route‐optimization problem across a finite time horizon in which multiple searchers seek to detect one or more probabilistically moving targets. This article formulates a novel convex mixed‐integer nonlinear program for this problem that generalizes earlier models to situations with multiple targets, searcher deconfliction, and target‐ and location‐dependent search effectiveness. We present two solution approaches, one based on the cutting‐plane method and the other on linearization. These approaches result in the first practical exact algorithms for solving this important problem, which arises broadly in military, rescue, law enforcement, and border patrol operations. The cutting‐plane approach solves many realistically sized problem instances in a few minutes, while existing branch‐and‐bound algorithms fail. A specialized cut improves solution time by 50[percnt] in difficult problem instances. The approach based on linearization, which is applicable in important special cases, may further reduce solution time with one or two orders of magnitude. The solution time for the cutting‐plane approach tends to remain constant as the number of searchers grows. In part, then, we overcome the difficulty that earlier solution methods have with many searchers. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

A general strategic capacity planning model under demand uncertainty

Capacity planning decisions affect a significant portion of future revenue. In equipment intensive industries, these decisions usually need to be made in the presence of both highly volatile demand and long capacity installation lead times. For a multiple product case, we present a continuous‐time capacity planning model that addresses problems of realistic size and complexity found in current practice. Each product requires specific operations that can be performed by one or more tool groups. We consider a number of capacity allocation policies. We allow tool retirements in addition to purchases because the stochastic demand forecast for each product can be decreasing. We present a cluster‐based heuristic algorithm that can incorporate both variance reduction techniques from the simulation literature and the principles of a generalized maximum flow algorithm from the network optimization literature. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

The Management of Pakistan's Nuclear Arsenal

Pakistan, the fastest growing nuclear weapon state in the world, has established over the last decade a nuclear management system it holds to be “foolproof.” Despite the explosion of radical groups challenging the writ of the state, it dismisses concerns by critics that its nuclear weapons are not safe and secure as “preposterous” and an attempt to “malign” the state. This article examines Pakistan's nuclear management system in four functional areas: command-and-control, physical security, nuclear surety, and doctrine. It describes what is publicly known in each area, identifies areas of omission and inadequacy in each one, and examines several premises of the nuclear program the author considers to be unfounded. Comparing these deficiencies in Pakistan's nuclear management system to the current problems plaguing the US nuclear management system, the author concludes that complacency and unfounded confidence in the efficacy of such programs, if not addressed and corrected, could lead to a future nuclear catastrophe in South Asia.     

Bounded flexibility in days‐on and days‐off scheduling

This article presents a flexible days‐on and days‐off scheduling problem and develops an exact branch and price (B&P) algorithm to find solutions. The main objective is to minimize the size of the total workforce required to cover time‐varying demand over a planning horizon that may extend up to 12 weeks. A new aspect of the problem is the general restriction that the number of consecutive days on and the number of consecutive days off must each fall within a predefined range. Moreover, the total assignment of working days in the planning horizon cannot exceed some maximum value. In the B&P framework, the master problem is stated as a set covering‐type problem whose columns are generated iteratively by solving one of three different subproblems. The first is an implicit model, the second is a resource constrained shortest path problem, and the third is a dynamic program. Computational experiments using both real‐word and randomly generated data show that workforce reductions up to 66% are possible with highly flexible days‐on and days‐off patterns. When evaluating the performance of the three subproblems, it was found that each yielded equivalent solutions but the dynamic program proved to be significantly more efficient. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 678–701, 2013     

Penalty calculations and branching rules in a LAV best subset procedure

Least absolute value (LAV) regression has become a widely accepted alternative to least squares regression. This has come about as the result of advancements in statistical theory and computational procedures to obtain LAV estimates. Computer codes are currently available to solve a wide range of LAV problems including the best subset regression. The purpose of this article is to study the use of penalty calculations and other branching rules in developing the solution tree for the best subset LAV regression.     

A linear programming model for design of communications networks with time varying probabilistic demands

In this paper marginal investment costs are assumed known for two kinds of equipment stocks employed to supply telecommunications services: trunks and switching facilities. A network hierarchy is defined which includes important cases occurring in the field and also appearing in the literature. A different use of the classical concept of the marginal capacity of an additional trunk at prescribed blocking probability leads to a linear programming supply model which can be used to compute the sizes of all the high usage trunk groups. The sizes of the remaining trunk groups are approximated by the linear programming models, but can be determined more accurately by alternate methods once all high usage group sizes are computed. The approach applies to larger scale networks than previously reported in the literature and permits direct application of the duality theory of linear programming and its sensitivity analyses to the study and design of switched probabilistic communications networks with multiple busy hours during the day. Numerical results are presented for two examples based on field data, one of which having been designed by the multi-hour engineering method.     

Two machine flow shop scheduling problems with sequence dependent setup times: A dynamic programming approach

This paper considers the two different flow shop scheduling problems that arise when, in a two machine problem, one machine is characterized by sequence dependent setup times. The objective is to determine a schedule that minimizes makespan. After establishing the optimally of permutation schedules for both of these problems, an efficient dynamic programming formulation is developed for each of them. Each of these formulations is shown to be comparable, from a computational standpoint, to the corresponding formulation of the traveling salesman problem. Then, the relative merits of the dynamic programming and branch and bound approaches to these two scheduling problems are discussed.