2019 MOR Journal

2019 Vol. 24, #4 - Full Issue





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2019 Vol. 24, #4 - Adaptive Unmanned Aerial Vehicle Surveillance Using a Prize- Collecting Vertex Routing Model

Michael D. Moskal, II and Rajan Batta

This work considers methods to generate a series of tractable routes to maximize information collection in the context of unmanned aerial vehicle routing for intelligence, surveillance, and reconnaissance missions. Considering the applicability of this work to field applications, a new interpretation of the prize-collecting and orienteering problems is presented by incorporating partial and recurring collections along the route. An area of operation is discretized into a series of uniformly shaped microgrids and decomposed into an undirected graph, representing regions of potential interest to intelligence operations. A mixed integer program and simulated annealing-based heuristic are evaluated for performance, objective quality, and applicability to real-world exercises.

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2019 Vol. 24, #4 - An Adaptive Mix Correction for Fire Support in Combat Operations

Israel David

A classic and prominent problem in artillery operation is how to immediately shift fire from one target to another. This paper proposes an innovative method for artillery firing transfer. This method greatly outperforms the current firing techniques. A constrained quadratic problem is formulated and solved explicitly to determine the optimal parameters of the new method. The new method can immediately be applied to real operations.

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2019 Vol. 24, #4 - Optimizing the Purchases of Military Air-to- Ground Weapons

Matthew S. Goldberg and David M. Goldberg

The United States military services apply modeling and simulation when determining their purchases of air-to-ground weapons for use in strike warfare. The authors pose the problem of aircraft weapon budgeting as a nonlinear program, but that problem has difficult nonlinearities and is high-dimensional. They offer two heuristics that reduce the dimensionality of the problem. Except in polar cases, neither heuristic is guaranteed to duplicate the optimal solution that uses all feasible combinations of aircraft, weapons, and targets. However, the two heuristics—particularly when applied in combination—achieve a near-optimal solution in greatly reduced runtime.

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2019 Vol. 24, #4 - A Heuristic Algorithm for Weapon Target Assignment and Scheduling

Hyun Seop Uhm and Young Hoon Lee

In a defense situation, there exist several attacking missiles (targets) that must be defended simultaneously and continuously by defending weapons. The weapon target assignment and scheduling problem assigns a defending weapon to an attacking missile and schedules each target’s collision time with limited guiding radar channels. The problem is formulated as an integer nonlinear program to maximize the total expected kill probability, and heuristics are developed by decomposition of the nonlinear program, linearization techniques, and simulated annealing, which was evaluated on the several test scenarios.

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2019 Vol. 24, #4 - Military Operations Research Society (MORS) Oral History Project Interview of Jeffrey E. Kline (CAPT, USN-ret)

Dr. Gerald G. “Jerry” Brown, Captain Wayne Hughes, and Dr. Bob Sheldon, FS



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2019 Vol. 24, #3 - Full Issue





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2019 Vol. 24, #3 - Testing Policies and Key Influences on Long-Term Aircraft Fleet Management Using Designed Simulation Experiments

David Marlow, Susan M. Sanchez, and Paul J. Sanchez

The authors apply a designed simulation experiment approach to a model of a fleet of military aircraft over its life. The model includes daily flying, scheduled and unscheduled maintenance, and aircraft (helicopters) moving between embarked and ashore states. We test various fleet management policy types, covering flying and maintenance allocation, resource and personnel sharing, and achieving balanced usage rates across the fleet and each aircraft. The experiment incorporates 21 variables, including continuous, discrete, and categorical variables representing the policy types. We demonstrate the power of the approach in revealing to fleet managers the key factors and policies that influence and impact fleet performance.

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2019 Vol. 24, #3 - Petri Net Models of Adversarial Scenarios in Safety and Security

David H. Collins and Aparna V. Huzurbazar

Adversarial scenarios of interest to the defense and intelligence communities, such as attacks on guarded facilities, may involve multiple actors operating concurrently and interactively. These scenarios cannot be modeled realistically with many of the currently used risk and vulnerability assessment methods. Collins and Huzurbazar propose Petri nets (PNs), originally developed to model concurrency in computer architectures, as a powerful tool for modeling complex adversarial scenarios. PNs provide a graphical representation for eliciting scenarios from subject matter experts, as well as a basis for computer simulation of the scenarios. An application to site security is used to illustrate how PNs with stochastic extensions can be used to derive statistical properties of dynamic scenarios involving any number of concurrent actors. The example scenario and others have been implemented using a graphical tool written in the statistical computing language R.

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2019 Vol. 24, #3 - Managing the Risk of Satellite Collisions: A Probabilistic Risk Analysis of Improving Space Surveillance Systems

Richard H. Kim and Elisabeth Pate-Cornell

Space systems face the risk of unintended collisions with other satellites and debris. Various space surveillance networks observe the population of resident space objects and make predictions on the probability of future collisions. An open question is whether these surveillance systems should be comprised of a few large, exquisite, but costly sensors, or many small, individually less capable, but very cheap sensors. We propose a mathematical model based on Bayesian updating of sensor signals to assess the collision risk reduction benefit from these opposing sensor architectures.

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2019 Vol. 24, #3 - Military Operations Research Society (MORS) Oral History Project Interview of Dr. William G. Lese, Jr, FS

Mr. Bill Dunn, FS, Mr. Michael W. Garrambone, FS, Dr. Bob Sheldon, FS, and Mr. Bill Woodson



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2019 Vol. 24, #2 - Full Issue





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2019 Vol. 24, #2 - Incorporating Resilience in an Integrated Analysis of Alternatives

Zephan Wade, Simon Goerger, Gregory S. Parnell, Ed Pohl, and Eric Specking

The analysis of alternatives (AoA) is the Department of Defense process to identify the best affordable system for development.The authors propose improvements to expand and explore the design space beyond current point-based design methods. They propose an iterative set-based designmethod to leverage set-based design and statistical analysis to provide greater insight into efficient solutions for decision makers. They illustrate this method with an integrated AoA for a squad enhancement portfolio of systems study performed for the Engineered Resilient Systems research program. The integrated AoA leverages modeling and simulation to improve insights for decision makers and contribute to better system designs.

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2019 Vol. 24, #2 - Patrolling Games on General Graphs with Time- Dependent Node Values

Abdolmajid Yolmeh and Melike Baykal-Gursoy

Scheduling and deployment of patrols are important operational decisions in safeguarding an area against adversarial invasion or illicit activity. Most patrolling game models assume that all nodes have the same value or that their values are fixed throughout time. The authors introduce a more realistic patrolling game model on a general graph with time-dependent node values, multiple attackers and patrollers, and node-specific attack times. They propose a column and row generation algorithm, demonstrate its efficiency, and apply it to a real case of an urban rail network in a major US city.

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2019 Vol. 24, #2 - The Use of Markov Decision Processes for Australian Naval Aviation Training Schedules

Sofi Suvorova, Ana Novak, Bill Moran, and Terry Caelli

This paper investigates an algorithm for recruitment through the Royal Australian Navy training continuum for helicopter crews. This problem is complicated by a range of hard constraints and requirements; in particular, preference is given to instructors taken from the pool of existing trained pilots, and operational capability needs to be maintained. Further complications are the high and highly variable failure rates in some courses. The authors compare a Markov decision process (MDP) stationary solution with a one-step and two-step ahead integer linear programming (ILP) approach. Currently a totally manual heuristic approach is used. The MDP gives the best performance.

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2019 Vol. 24, #2 - Military Operations Research Society (MORS) Oral History Project: Dr. Dorothy E. Denning

Captain Wayne Hughes, FS, Dr. Bob Sheldon, FS, and Mr. Robert Hayden



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2019 Vol. 24, #2 - Military Operations Research Society (MORS) Oral History Project: Dr. Peter J. Denning

Captain Wayne Hughes, FS and Dr. Bob Sheldon, FS



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2019 Vol. 24, #1 - Full Issue



 


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2019 Vol. 24, #1 - Finding the Best System Nested Defense Under Budget Uncertainty

Gary L. Lazzaro and W. Matthew Carlyle

A set of defenses is nested if the defense for a particular budget scenario includes the defenses in all smaller budget scenarios. The use of nested defenses simplifies the choice of a defense under budget uncertainty into a single, prioritized list. However, a nested set of defenses over many budget scenarios is almost always suboptimal for some of those scenarios. We extend a defender-attacker-defender tri-level optimization model to identify a nested set of defenses to a system that is as close as possible to the optimal defenses over all possible budget scenarios, for several measures of “closeness.”

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2019 Vol. 24, #1 - A Combinatorial Benders Decomposition Algorithm for the Directed Multiflow Network Diversion Problem

Chungmok Lee, M.S. Donghyun Cho, and Sungsoo Park

In warfare, the defender often faces a hard decision of destroying his or her own facilities to force the attacker to pass a particular location. The network interdiction problem involves determining which arcs to destroy to make the network flow have no option but to pass the given arc. The authors extend this problem to the case where many network flows exist. They propose a variant of the Benders decomposition algorithm to solve the problem, which gives superior performance.

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2019 Vol. 24, #1 - On the Use of Simulation and Optimization for Mission Modules Selection in a Maritime Context

Jean-Denis Caron, Van Fong, and Vladislav Brion

For many navies, mission modularity (i.e., the process of delivering capability in a vessel through the use of standardized modules) is still a relatively new concept that needs further research. In this paper, the authors present an approach developed to inform decision makers within the Royal Canadian Navy (RCN) on the number and types of mission modules required to meet ambitions and mandate. The two-fold approach makes use of a Monte Carlo simulation to generate the operational demand and a mixed integer linear programming model to determine the optimal mix of mission modules. The proposed methodology can be applied by other navies around the world.

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2019 Vol. 24, #1 - Military Operations Research Society (MORS) Oral History Project Interview of Dr. Stuart H. Starr, FS

Bob Sheldon and Bill Dunn



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