A Scenario-based Parametric Analysis of the Army Personnel-to-assignment Matching Problem

This study aims to compare linear programming and stable marriage approaches to the personnel assignment problem under conditions of uncertainty. Robust solutions should exhibit reduced variability of solutions in the presence of one or more additional constraints or problem perturbations added to some baseline problems.Several variations of each approach are compared with respect to solution speed, solution quality as measured by officer-to-assignment preferences and solution robustness as measured by the number of assignment changes required after inducing a set of representative perturbations or constraints to an assignment instance. These side constraints represent the realistic assignment categorical priorities and limitations encountered by army assignment managers who solve this problem semiannually, and thus the synthetic instances considered herein emulate typical problem instances

Examining Military Medical Evacuation Dispatching Policies Utilizing a Markov Decision Process Model of a Controlled Queueing System

Military medical planners must develop dispatching policies that dictate how aerial medical evacuation (MEDEVAC) units are utilized during major combat operations. The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on a request’s classification (i.e., zone and precedence level) and the state of the MEDEVAC system. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic policies. Two computational experiments are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system performance measure. Several excursions are examined to identify how the 9-line MEDEVAC request arrival rate and the MEDEVAC flight speeds impact the optimal dispatching policy. Results indicate that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Moreover, an analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the linear programming algorithms executed by CPLEX 12.6 with regard to computational effort. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems

Equitable apportionment of railcars within a pooling agreement for shipping automobiles

This paper examines the problem of apportioning a railcar fleet to car manufacturers and railroads within a pooling agreement for shipping automobiles. We demonstrate the potential inequities in the presently implemented allocation procedure in the industry, and we propose four alternative schemes to apportion railcars to manufacturers and an alternative railroad allocation scheme. We test the combinations of current and proposed techniques on realistic instances derived from representative data of the current business environment, and illustrate their impact relative to the existing methodology.Fleet allocation Railcar management Marginal cost analysis Shapley values

Leveraging Behavioral Game Theory to Inform Military Operations Planning

Since Thomas Schelling published The Strategy of Conflict (1960), the study of game theory and international relations have been closely linked. Developments in the former often trigger analytical changes in the latter, as evidenced by the recent behavioral and psychological focus among some international relations and defense economics scholars. Despite this connection, decisions regarding military operations have rarely been influenced by game theoretic analysis, a fact often attributed to standard game theory’s normative nature. Therefore, this research applies selected behavioral game theoretic solution techniques to classical interstate conflict games, demonstrating their utility to inform the planning of military operations

Examining military medical evacuation dispatching policies utilizing a Markov decision process model of a controlled queueing system

Military medical planners must develop dispatching policies that dictate how aerial medical evacuation (MEDEVAC) units are utilized during major combat operations. The objective of this research is to determine how to optimally dispatch MEDEVAC units in response to 9-line MEDEVAC requests to maximize MEDEVAC system performance. A discounted, infinite horizon Markov decision process (MDP) model is developed to examine the MEDEVAC dispatching problem. The MDP model allows the dispatching authority to accept, reject, or queue incoming requests based on a request’s classification (i.e., zone and precedence level) and the state of the MEDEVAC system. A representative planning scenario based on contingency operations in southern Afghanistan is utilized to investigate the differences between the optimal dispatching policy and three practitioner-friendly myopic policies. Two computational experiments are conducted to examine the impact of selected MEDEVAC problem features on the optimal policy and the system performance measure. Several excursions are examined to identify how the 9-line MEDEVAC request arrival rate and the MEDEVAC flight speeds impact the optimal dispatching policy. Results indicate that dispatching MEDEVAC units considering the precedence level of requests and the locations of busy MEDEVAC units increases the performance of the MEDEVAC system. These results inform the development and implementation of MEDEVAC tactics, techniques, and procedures by military medical planners. Moreover, an analysis of solution approaches for the MEDEVAC dispatching problem reveals that the policy iteration algorithm substantially outperforms the linear programming algorithms executed by CPLEX 12.6 with regard to computational effort. This result supports the claim that policy iteration remains the superlative solution algorithm for exactly solving computationally tractable Markov decision problems

Social Network Analysis of Twitter Interactions: A Directed Multilayer Network Approach

Effective employment of social media for any social influence outcome requires a detailed understanding of the target audience. Social media provides a rich repository of self-reported information that provides insight regarding the sentiments and implied priorities of an online population. Using Social Network Analysis, this research models user interactions on Twitter as a weighted, directed network. Topic modeling through Latent Dirichlet Allocation identifies the topics of discussion in Tweets, which this study uses to induce a directed multilayer network wherein users (in one layer) are connected to the conversations and topics (in a second layer) in which they have participated, with inter-layer connections representing user participation in conversations. Analysis of the resulting network identifies both influential users and highly connected groups of individuals, informing an understanding of group dynamics and individual connectivity. The results demonstrate that the generation of a topically-focused social network to represent conversations yields more robust findings regarding influential users, particularly when analysts collect Tweets from a variety of discussions through more general search queries. Within the analysis, PageRank performed best among four measures used to rank individual influence within this problem context. In contrast, the results of applying both the Greedy Modular Algorithm and the Leiden Algorithm to identify communities were mixed; each method yielded valuable insights, but neither technique was uniformly superior. The demonstrated four-step process is readily replicable, and an interested user can automate the process with relatively low effort or expense

Informing National Security Policy by Modeling Adversarial Inducement and Its Governance

The distinction between peace and conflict in contemporary international relations is no longer well-defined. Leveraging modern technology, hostile action below the threshold of war has become increasingly effective. The objective of such aggression is often the influence of opinions, emotions, and, ultimately, the decisions of a nation\u27s citizenry. This work presents two new game theoretic frameworks, denoted as prospect games and regulated prospect games, to inform defensive policy against these threats. These frameworks respectively model (a) the interactions of competing entities influencing a populace and (b) the preemptive actions of a regulating agent to alter such a framework. Prospect games and regulated prospect games are designed to be adaptable, depending on the assumed nature of persuaders\u27 interactions and their rationality. The contributions herein are a modeling framework for competitive influence operations under a common set of assumptions, model variants that respectively correspond to scenario-specific modifications of selected assumptions, the illustration of practical solution methods for the suite of models, and a demonstration on a representative scenario with the ultimate goal of providing a quantifiable, tractable, and rigorous framework upon which national policies defending against competitive influence can be identified

Approximate Dynamic Programming for the Military Inventory Routing Problem

The United States Army can benefit from effectively utilizing cargo unmanned aerial vehicles (CUAVs) to perform resupply operations in combat environments to reduce the use of manned (ground and aerial) resupply that incurs risk to personnel. We formulate a Markov decision process (MDP) model of an inventory routing problem (IRP) with vehicle loss and direct delivery, which we label the military IRP (MILIRP). The objective of the MILIRP is to determine CUAV dispatching and routing policies for the resupply of geographically dispersed units operating in an austere, combat environment. The large size of the problem instance motivating this research renders dynamic programming algorithms inappropriate, so we utilize approximate dynamic programming (ADP) methods to attain improved policies (relative to a benchmark policy) via an approximate policy iteration algorithmic strategy utilizing least squares temporal differencing for policy evaluation. We examine a representative problem instance motivated by resupply operations experienced by the United States Army in Afghanistan both to demonstrate the applicability of our MDP model and to examine the efficacy of our proposed ADP solution methodology. A designed computational experiment enables the examination of selected problem features and algorithmic features vis-à-vis the quality of solutions attained by our ADP policies. Results indicate that a 4-crew, 8-CUAV unit is able to resupply 57% of the demand from an 800-person organization over a 3-month time horizon when using the ADP policy, a notable improvement over the 18% attained using a benchmark policy. Such results inform the development of procedures governing the design, development, and utilization of CUAV assets for the resupply of dispersed ground combat forces