Pursuit around a hole

The solution of isaacs¹ problem of optimal pursuit in a plane with a circular disk removed, given constant speeds, zero turning radius, and perfect visibility for both players is presented herein. The hole has three effects: the trivial effect that shortest paths are not straight, the trapping effect to turn the evader from running into the hole, and the screening effect causing an evader retreating behind the hole not to retreat across a line through its center.     

On the minimization of a certain convex function arising in applied decision theory

The author, in an expository paper [4], has presented an algorithm for choosing a non-negative vector

     

Optimal inspection under semi-markovian deterioration: Basic results

This article develops a model for determining the optimal inspection schedule for a system which deteriorates according to a semi-Markov process that progresses through three states: good, defective, and bad. A binary test is used, and false positives may occur. A true positive results in an action that reduces the likelihood of entering the bad state, but at most one such corrective action can occur during the lifetime of the system. Costs are associated with each inspection, each false positive, the corrective action, and the entrance into the bad state. Dynamic programming is used to compute the minimum expected cost, which is a function of the age of the system. The optimal inspection schedule is readily derived from this value function. Computational examples are provided. This model is appropriate for medical screening or for a mission where there is only one spare part.     

A note on the optimality conditions for the bilevel programming problem

A counterexample is given to demonstrate that previously proposed necessary conditions for the bilevel programming problem are not correct. An interpretation of the difficulty is given by appealing to a “theorem of alternative” result presented in the original work.     

Search and the introduction of improved technologies

Modeling R&D as standard sequential search, we consider a monopolist who can implement a sequence of technological discoveries during the technology search process: he earns revenue on his installed technology while he engages in R&D to find improved technology. What is not standard is that he has a finite number of opportunities to introduce improved technology. We show that his optimal policy is characterized by thresholds ξ_i(x): introduce the newly found technology if and only if it exceeds ξ_i(x) when x is the state of the currently installed technology and i is the number of remaining introductions allowed. We also analyze a nonstationary learning‐by‐doing model in which the monopolist's experience in implementing new technologies imparts increased capability in generating new technologies. Because this nonstationary model is not in the class of monotone stopping problems, a number of surprising results hold and several seemingly obvious properties of the stationary model no longer hold. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011