Validation of a strategy for harbor defense based on the use of a min‐max algorithm receding horizon control law

We present a validation of a centralized feedback control law for robotic or partially robotic water craft whose task is to defend a harbor from an intruding fleet of water craft. Our work was motivated by the need to provide harbor defenses against hostile, possibly suicidal intruders, preferably using unmanned craft to limit potential casualties. Our feedback control law is a sample‐data receding horizon control law, which requires the solution of a complex max‐min problem at the start of each sample time. In developing this control law, we had to deal with three challenges. The first was to develop a max‐min problem that captures realistically the nature of the defense‐intrusion game. The second was to ensure the solution of this max‐min problem can be accomplished in a small fraction of the sample time that would be needed to control a possibly fast moving craft. The third, to which this article is dedicated, was to validate the effectiveness of our control law first through computer simulations pitting a computer against a computer or a computer against a human, then through the use of model hovercraft in a laboratory, and finally on the Chesapeake Bay, using Yard Patrol boats. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 247–259, 2016     

Level workforce planning for multistage transfer lines

In this article, we define two different workforce leveling objectives for serial transfer lines. Each job is to be processed on each transfer station for c time periods (e.g., hours). We assume that the number of workers needed to complete each operation of a job in precisely c periods is given. Jobs transfer forward synchronously after every production cycle (i.e., c periods). We study two leveling objectives: maximin workforce size () and min range (R). Leveling objectives produce schedules where the cumulative number of workers needed in all stations of a transfer line does not experience dramatic changes from one production cycle to the next. For and a two‐station system, we develop a fast polynomial algorithm. The range problem is known to be NP‐complete. For the two‐station system, we develop a very fast optimal algorithm that uses a tight lower bound and an efficient procedure for finding complementary Hamiltonian cycles in bipartite graphs. Via a computational experiment, we demonstrate that range schedules are superior because not only do they limit the workforce fluctuations from one production cycle to the next, but they also do so with a minor increase in the total workforce size. We extend our results to the m‐station system and develop heuristic algorithms. We find that these heuristics work poorly for min range (R), which indicates that special structural properties of the m‐station problem need to be identified before we can develop efficient algorithms. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 577–590, 2016     

Linking a throughput simulation to a systems dynamics simulation to assess the utility of a US Navy foreign humanitarian aid mission

The success of any humanitarian aid mission is generally measured by the timeliness of critical supplies that are delivered to the affected area. However, a more interesting analysis may be to determine the effect of the aid on the overall satisfaction of the local population. The authors' research focused on the delivery of humanitarian aid to a notional region that was decimated by flooding with ships, landing craft and security personnel provided by the US Navy and Marines. While the research effort addressed naval force structure, the focus of the research was: (1) to assess different delivery methods for the aid; and (2) to determine how the aid delivery impacted the overall satisfaction of the local population. To examine both concerns, two simulation models were developed, with one examining the throughput of aid delivered during the operation, and the other the satisfaction of the population based on the humanitarian aid effort.     

Flexible supply contracts for short life‐cycle goods: The buyer's perspective

In this paper we analyze a two‐period supply contract which allows for order adjustment by the buyer. The buyer is required to place orders for two periods. After observing initial demand, the buyer is then allowed to adjust the second order, paying a per unit order adjustment penalty. We describe the optimal behavior of the buyer under such a contract, both in determining the initial order quantities and in subsequently adjusting the order. We compare the solution to a contract where no adjustment is allowed and to the case where adjustment is allowed without penalty. We demonstrate that flexible contracts can reduce the potentially negative effect of correlation of demand between two periods. Further, we investigate how the duration of the first period vis‐à‐vis the second period affects the profitability of the buyer as a function of the degree of correlation. © 2002 John Wiley & Sons, Inc. Naval Research Logistics, 49: 25–45, 2002; DOI 10.1002/nav.10002     

Exact algorithms for integrated facility location and production planning problems

We consider a class of facility location problems with a time dimension, which requires assigning every customer to a supply facility in each of a finite number of periods. Each facility must meet all assigned customer demand in every period at a minimum cost via its production and inventory decisions. We provide exact branch‐and‐price algorithms for this class of problems and several important variants. The corresponding pricing problem takes the form of an interesting class of production planning and order selection problems. This problem class requires selecting a set of orders that maximizes profit, defined as the revenue from selected orders minus production‐planning‐related costs incurred in fulfilling the selected orders. We provide polynomial‐time dynamic programming algorithms for this class of pricing problems, as well as for generalizations thereof. Computational testing indicates the advantage of our branch‐and‐price algorithm over various approaches that use commercial software packages. These tests also highlight the significant cost savings possible from integrating location with production and inventory decisions and demonstrate that the problem is rather insensitive to forecast errors associated with the demand streams. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

The United States Coast Guard Computer-Assisted Search Planning system (CASP)

This paper provides an overview of the Computer-Assisted Search Planning (CASP) system developed for the United States Coast Guard. The CASP information processing methodology is based upon Monte Carlo simulation to obtain an initial probability distribution for target location and to update this distribution to account for drift due to currents and winds. A multiple scenario approach is employed to generate the initial probability distribution. Bayesian updating is used to reflect negative information obtained from unsuccessful search. The principal output of the CASP system is a sequence of probability “maps” which display the current target location probability distributions throughout the time period of interest. CASP also provides guidance for allocating search effort based upon optimal search theory.