Waypoint analysis for command and control

Command and Control (C2) in a military setting can be epitomized in battles‐of‐old when commanders would seek high ground to gain superior spatial‐temporal information; from this vantage point, decisions were made and relayed to units in the field. Although the fundamentals remain, technology has changed the practice of C2; for example, enemy units may be observed remotely, with instruments of varying positional accuracy. A basic problem in C2 is the ability to track an enemy object in the battlespace and to forecast its future position; the (extended) Kalman filter provides a straightforward solution. The problem changes fundamentally if one assumes that the moving object is headed for an (unknown) location, or waypoint. This article is concerned with the new problem of estimation of such a waypoint, for which we use Bayesian statistical prediction. The computational burden is greater than an ad hoc regression‐based estimate, which we also develop, but the Bayesian approach has a big advantage in that it yields both a predictor and a measure of its variability. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

Algorithms for solving the conditional covering problem on paths

Consider the conditional covering problem on an undirected graph, where each node represents a site that must be covered by a facility, and facilities may only be established at these nodes. Each facility can cover all sites that lie within some common covering radius, except the site at which it is located. Although this problem is difficult to solve on general graphs, there exist special structures on which the problem is easily solvable. In this paper, we consider the special case in which the graph is a simple path. For the case in which facility location costs do not vary based on the site, we derive characteristics of the problem that lead to a linear‐time shortest path algorithm for solving the problem. When the facility location costs vary according to the site, we provide a more complex, but still polynomial‐time, dynamic programming algorithm to find the optimal solution. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Approximating nondominated sets in continuous multiobjective optimization problems

Many important problems in Operations Research and Statistics require the computation of nondominated (or Pareto or efficient) sets. This task may be currently undertaken efficiently for discrete sets of alternatives or for continuous sets under special and fairly tight structural conditions. Under more general continuous settings, parametric characterisations of the nondominated set, for example through convex combinations of the objective functions or ε‐constrained problems, or discretizations‐based approaches, pose several problems. In this paper, the lack of a general approach to approximate the nondominated set in continuous multiobjective problems is addressed. Our simulation‐based procedure only requires to sample from the set of alternatives and check whether an alternative dominates another. Stopping rules, efficient sampling schemes, and procedures to check for dominance are proposed. A continuous approximation to the nondominated set is obtained by fitting a surface through the points of a discrete approximation, using a local (robust) regression method. Other actions like clustering and projecting points onto the frontier are required in nonconvex feasible regions and nonconnected Pareto sets. In a sense, our method may be seen as an evolutionary algorithm with a variable population size. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Analysis of a new vehicle scheduling and location problem

We consider a container terminal discharging containers from a ship and locating them in the terminal yard. Each container has a number of potential locations in the yard where it can be stored. Containers are moved from the ship to the yard using a fleet of vehicles, each of which can carry one container at a time. The problem is to assign each container to a yard location and dispatch vehicles to the containers so as to minimize the time it takes to download all the containers from the ship. We show that the problem is NP‐hard and develop a heuristic algorithm based on formulating the problem as an assignment problem. The effectiveness of the heuristic is analyzed from both worst‐case and computational points of view. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 363–385, 2001     

Case Study Commentary and Analysis: The Moral Sword of Damocles

Case summary, by James Cook (Case Study Editor):

In the final issue of the 2015 volume of the Journal of Military Ethics, we published a case study entitled “Coining an Ethical Dilemma: The Impunity of Afghanistan’s Indigenous Security Forces”, written by Paul Lushenko. The study detailed two extra-judicial killings (EJKs) by Afghan National Police (ANP) personnel in an area stabilized and overseen by a US-led Combined Task Force (CTF). To deter further EJKs following the first incident, the CTF’s commander reported the incidents up his chain of command and used the limited tools at his disposal to influence local indigenous officials directly. Apparently, the ANP unit took no notice. In his commentary on the case study, Paul Robinson considered moral compromise in war more generally. Coalition troops in Afghanistan, for instance, have encountered not just EJKs but also sexual abuse of minors, killing of non-combatants, kidnapping, torture, and widespread corruption. What should the soldier on the ground do if indigenous personnel violate Laws of Armed Conflict (LOAC) with impunity? Refusing to serve will not right or prevent moral wrongs, while staying on to fight the good but futile fight will mire the soldier in moral compromise. “?… [S]oldiers faced with this dilemma have no good options. The systemic failings surrounding them mean that it is probable that nothing they do will help”. In a concluding note, I suggested that while an individual soldier may indeed have no good options, as Paul Robinson suggests, that soldier’s military and nation at large are obliged to do what they can. At least, they must keep to the moral high ground so as not to give indigenous security forces an excuse to misbehave, and determine the nature of crimes such as EJKs: are they outlaw acts or in fact endorsed by the indigenous culture and perhaps even government? Below Colonel Dave Barnes, himself a veteran of Operation Enduring Freedom, analyzes Paul Lushenko’s case study at “?…?the local, tactical level: If a commander is in this situation – where her unit witnesses an EJK or other war crime – what should she do?”     

‘The People are Revolting’: An Anatomy of Authoritarian Counterinsurgency

Rather than win hearts and minds, authoritarian counterinsurgency is said to rely heavily on coercion. It has a reputation for effectiveness, if also for its amorality. Still, the research into authoritarian counterinsurgency is surprisingly lacking. By distilling common features from key cases, this article concludes that this approach goes beyond the indiscriminate violence that typically captures the imagination. Like their democratic counterparts but differently, authoritarian regimes also engage in mobilisation, create narratives, and turn military advantage into political gain. The analysis explains how these tasks are undertaken and, by contradistinction, sheds light on more liberal approaches as well.     

Multiperiod allocation of limited resources: A minimax approach

We consider a multiperiod model in which limited resources are allocated among competing activities in each period. The objective is to minimize the maximum weighted deviation of the cumulative activity levels from the cumulative demands among all activities at all periods. All resources are assumed to be storable; that is, surpluses at one period can be used later on. This model is useful, for example, in multiperiod production planning for high-technology industries that assemble a large variety of circuit boards using numerous electronic components. The model is formulated with a minimax objective. We develop an efficient algorithm that can solve large-scale problems very quickly. At each iteration, the algorithm makes use of the solution to a relaxed problem to identify activities that should be permanently set to zero, as well as groups of activities that should have the same value.