A note on the costly surveillance of a stochastic system

We consider the costly surveillance of a stochastic system with a finite state space and a finite number of actions in each state. There is a positive cost of observing the system and the system earns at a rate depending on the state of the system and the action taken. A policy for controlling such a system specifies the action to be taken and the time to the next observation, both possibly random and depending on the past history of the system. A form of the long range average income is the criterion for comparing different policies. If R ^Δ denotes the class of policies for which the times between successive observations of the system are random variables with cumulative distribution functions on [0, Δ], Δ < ∞, we show that there exists a nonrandomized stationary policy that is optimal in R ^Δ. Furthermore, for sufficiently large Δ, this optimal policy is independent of Δ.     

An exact branch-and-bound procedure for the quadratic-assignment problem

The quadratic-assignment problem is a difficult combinatorial problem which still remains unsolved. In this study, an exact branch-and-bound procedure, which is able to produce optimal solutions for problems with twelve facilities or less, is developed. The method incorporates the concept of stepped fathoming to reduce the effort expended in searching the decision trees. Computational experience with the procedure is presented.     

Another inventory model with a mixture of backorders and lost sales

This article presents another inventory model for situations in which, during the stockout period, a fraction b of the demand is backordered and the remaining fraction 1 ? b is lost. By defining a time-proportional backorder cost and a fixed penalty cost per unit lost, a unimodal objective function representing the average annual cost of operating the inventory system is obtained. The optimal operating policy variables are calculated directly.     

A note on a paper by W. Szwarc

In this note the authors call for a change of the optimality criteria given by Theorem 3 in section 5 of the paper of W. Szwarc “On Some Sequencing Problems” in NRLQ Vol. 15, No. 2 [2]. Further, two cases of the three machine problem, namely, (i) ≦ and (ii) ≦ are considered, and procedures for obtaining optimal sequences in these cases are given. In these cases the three-machine problem is solved by solving n (the number of jobs) two-machine problems.     

Optimal assignment of high multiplicity flight plans to dispatchers

This paper addresses the problem of finding a feasible schedule of n jobs on m parallel machines, where each job has a deadline and some jobs are preassigned to some machine. This problem arises in the daily assignment of workload to a set of flight dispatchers, and it is strongly characterized by the fact that the job lengths may assume one out of k different values, for small k. We prove the problem to be NP‐complete for k = 2 and propose an effective implicit enumeration algorithm which allows efficiently solution a set of real‐life instances. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 359–376, 2000     

Applying Lanchester's linear law to model the Ardennes campaign

In modern warfare, many believe the decisive factor in winning a battle is seizing the right moment to shift from defense to attack, or vice versa. This paper attempts to bring that perspective to Lanchester's differential equations of warfare, and continues the application of Lanchester's linear law to the analysis of the World War II battle of Ardennes, as reported in earlier issues of Naval Research Logistics by Bracken and by Fricker. A new variable, shift time, accounting for the timing of the shift between defense and attack is explicitly included in our version of the model, and it helps obtain improved goodness of fit to historical data. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:653–661, 2001     

Bayesian testing strategies for software with an operational profile

We consider a software reliability model where the failure rate of each fault depends on the specific operation performed. The software is tested in a given sequence of test cases for fixed durations of time to collect data on failure times. We present a Bayesian analysis of software failure data by treating the initial number of faults as a random variable. Our analysis relies on the Markov Chain Monte Carlo methods and is used for developing optimal testing strategies in an adaptive manner. Two different models involving individual and common faults are analyzed. We illustrate an implementation of our approach by using some simulated failure data. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:747–763, 2001