Representation of Search and Target Acquisition Protocol in Models and Simulation

This research evaluates the representation of individual Soldier Search and Target Acquisition (STA) protocols in models and simulation and identifies gaps in the current methodology and implementation. The primary contributions of this research include a synthesis of related literature, an algorithmic exploration of the current STA algorithms implemented in military simulation models, a functional analysis of three systems with a significant relationship to STA, and a determination of gaps and proposed solutions to improve the representation of human STA in military simulation models. The analysis highlighted gaps in three important STA representations: (1) field of view search, (2) identification in a field of view, and (3) information acquisition and situational awareness. Implications and recommendations to resolve these gaps are discussed

Foreword by Guest Editor LTC James H. Schreiner, PhD, PMP, CPEM

This special issue of the Industrial and Systems Engineering Review highlights top papers from the 2019 annual General Donald R. Keith memorial capstone conference held at the United States Military Academy in West Point, NY. Following careful review of 48 academic paper submissions, eight were selected for publication in this journal. Each paper incorporated features of systems or industrial engineering and presented detailed and reflective analysis in the topic. Three general bodies of knowledge in the papers include: systems engineering and decision analysis, modeling and simulation, and artificial intelligence Systems Engineering and Decision Analysis topics included three unique contributions. The work of Flanick et al. examined adaptability in Hyper-Enabled Operator systems and recommended how each technology might address capability gaps for special operations forces. Wilby et al. employed a scalable predictive statistical model for decision support to significant work package prioritization for U.S. Army Corps of Engineers nationally significant inland waterway infrastructure. Contributions by Shi et al. employed value focused thinking and a robust cost model to enable decision quality for PM Cargo CH-47 technologies. Modeling and Simulation works also included three unique contributions. Recognized as ‘best paper’ at the 2019 conference, work by Cooley et al. developed a senior leader engagement model using sparse K-means clustering techniques to greatly improve the planning and execution for AFRICOM leadership. Lovell et al. employed robust military simulation models to evaluate and propose solutions Soldier Search and Target Acquisition protocols. Work by Drake et al. employed vehicle Routing Problem simulation software to enhance United Health Services material handling challenges across NY State thus enabling quality optimization choices. Finally, two unique contributions in artificial intelligence examined key text mining technologies. Shi et al. employed text mining and Latent Dirichlet Allocation modeling to derive insights through trends and clustering narratives on U.S. Army Officer Evaluation Reports and describe success. Similarly, text mining techniques by Senft et al. helped to examine and show similarities in success narratives across genders thus providing valuable insights for promotion boards. Congratulation to the 2019 undergraduate scholars and all authors who provided valuable contributions through thoughtful and steadfast intellectual efforts to their fields of study! LTC James H. Schreiner, PhD, PMP, CPEM Director, Operations Research Center Department of Systems Engineering United States Military Academy Mahan Hall, Bldg 752, Room 305 West Point, NY 10996, USA [email protected]

Simulating Army Rail Yard Operations at the Port of Bremerhaven

To maintain the United States military’s capability to deploy rapidly across the globe, logistical planning tools, simulations, and models enhance leaders’ decision making abilities. This research develops a discrete event model designed to simulate military operations within a railyard in order to support the Engineer Research and Development Center’s (ERDC) Planning Logistics Analysis Network System (PLANS). The research team chose the Port of Bremerhaven, Germany as a case study due to its relevance to current military operations, granting us access to timely data and stakeholders with recent operational experience. The discrete event simulation (DES) utilizes stochastic processes and multiple layouts in order to analyze the amount of time it takes to move varying amounts of cargo and vehicles and identify potential bottlenecks in the operation

Anomaly Detection and Accuracy Measurement for Categorical Data

The Department of Defense (DoD) recently initiated an effort to compile all inter-service maintenance data for equipment and infrastructure, requiring the consolidation of maintenance records from over 40 different data sources.  This research evaluates and improves the accuracy of this maintenance data warehouse by means of value modeling and statistical methods for anomaly detection. The first step in this work included the categorization of error-identifying metadata, which was then consolidated into a weighted scoring model. The most novel aspect of the work involved error identification processes using conditional probability combinations and likelihood measures. This analysis showed promising results, successfully identifying numerous invalid maintenance description labels through the use of conditional probability tests. This process has potential to both reduce the amount of manual labor necessary to clean the DoD maintenance data records and provide better fidelity on DoD maintenance activities

Foreword

This special issue of the Industrial and Systems Engineering Review once again showcases the top papers from the annual General Donald R. Keith memorial capstone conference at the United States Military Academy in West Point, NY.  After consideration of over 40 academic papers, the eight listed in this issue were selected for publication in this journal.  Topics addressed in the papers span a wide spectrum, however the distinguishing aspects of each paper included a common trend; each of these papers clearly implemented some aspect of systems or industrial engineering underpinned by thoughtful analysis.  The papers focus on three general bodies of knowledge:  systems engineering, modeling and simulation, and system dynamics modeling.Systems engineering topics included two unique contributions.  The work of Byers et. al examined the trades between weapon weight and weapon lethality.  Bares et. al. examined computing and storage needs of a simulation-intense analytical organization, considering the processing, storage, and growth that such an organization would need to consider as part of their IT solution. Three papers created unique contributions primarily through modeling and simulation studies.  Grubaugh et al. explored anomaly detection in categorical data, a notoriously difficult problem domain.  Bieger et al. used discrete event simulation to analyze rail yard operations in support of military deployments.  Kumar and Mittal analyzed the feasibility and benefits of alternative organizational structures to support cyber defense, primarily using a value modeling approach.       Lastly, applied system dynamics modeling and research produced several outstanding papers, primarily across social science problems.  Led by the extensive advising efforts of Jillian Wisniewski, three of her students contributed notably.  Ferrer and Wisniewski used system dynamics to understand the growth of Boko Haram over the course of the last decade.  Riedlinger and Wisniewski applied system dynamics to better understand the replication of mass killings across the United States.  Lastly, Provaznik and Wisniewski explored the diffusion of news and information using system dynamics, analyzing important social problems created by echo chambers for ideologies. Please join me in congratulating our authors, especially the young undergraduate scholars that provided the primary intellectual efforts that created the contents of this issue.COL Paul F. Evangelista, PhD, P

Modeling the Growth of Boko Haram Using System Dynamics

This study uses a systems dynamic approach to understand how the attacks conducted by Boko Haram influence the group’s growth. Boko Haram originated in the early 2000s under Muhammad Yusuf, but the group did not become known for its violence until 2009 (Oftedal, 2013). In 2013, the United States designated Boko Haram as a Foreign Terrorist Organization (U.S. Department of State, 2013). The Nigerian government’s efforts to eliminate the group’s influence in northern Nigeria and neighboring countries has not been successful. As Africa enters the world spotlight, the need for curbing the influence of Boko Haram strengthens. The system dynamics modeling process provides a method of understanding the relationships within the underlying structures that drive the scope of influence of Boko Haram, including organizational growth, media coverage, and attack efficacy. A formalized system dynamics model provides a basis for policy recommendations to counteract the group’s efforts

Quantifying the Effects of Weapon Weight on Lethality through Holistic Modeling

Though it is widely known that weapon weight affects shooter stability, the quantitative effects on lethality and survivability are not well known. This issue stems from weapon lethality primarily being captured by equipment properties. A more holistic analysis can be performed by treating the soldier as a system by incorporating human factors with equipment performance specifications. This analysis requires the building of human factor models to appropriately capture lethality. The model development effort started with the collecting of data from experiments where the shot group accuracy was measured for weighted rifles. The resulting data was used to generate a mathematical model. This model, along with other human factor models, was integrated into the Weapon Lethality Service (WLS), a cloud-based simulation. The WLS was then set up to represent possible combat situations; the results were used to quantify the change in soldier lethality and survivability from changing the weapon weight

A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment

Decision making for military railyard infrastructure is an inherently multi-objective problem, balancing cost versus capability. In this research, a Pareto-based Multi-Objective Evolutionary Algorithm is compared to a military rail inventory and decision support tool (RAILER). The problem is formulated as a multi-objective evolutionary algorithm in which the overall railyard condition is increased while decreasing cost to repair and maintain. A prioritization scheme for track maintenance is introduced that takes into account the volume of materials transported over the track and each rail segment’s primary purpose. Available repair options include repairing current 90 gauge rail, upgrade of rail segments to 115 gauge rail, and the swapping of rail removed during the upgrade. The proposed Multi-Objective Evolutionary Algorithm approach provides several advantages to the RAILER approach. The MOEA methodology allows decision makers to incorporate additional repair options beyond the current repair or do nothing options. It was found that many of the solutions identified by the evolutionary algorithm were both lower cost and provide a higher overall condition that those generated by DoD’s rail inventory and decision support system, RAILER. Additionally, the MOEA methodology generates lower cost, higher capability solutions when reduced sets of repair options are considered. The collection of non-dominated solutions provided by this technique gives decision makers increased flexibility and the ability to evaluate whether an additional cost repair solution is worth the increase in facility rail condition

Warehouse Waste Reduction Level and Its Impact on Warehouse and Business Performance

Warehouses considered as having a strategic role in supporting the overall competitiveness of the supply chain through achieving improved efficiency. Accordingly, lean thinking have recently found its way in the warehouse operations. The aim of this paper is to propose and test a model to assess the level of waste reduction practices and its impact on warehouse and business performance, and to encourage scholars to develop models to assess lean thinking and waste reduction in warehouse operations. A Delphi technique used in order to develop related questionnaires to measure the degree of waste reduction in the different warehouse activities. The results suggested the existence of a positive relationship between waste reduction activities and business performance and warehouse operational performance. Finally, this paper provides practical implications for warehouse and supply chains managers and directions for future researchers in this field

Simulation Study for Multi-Echelon Multi-Depot Supply Chain System Using Live Data

The manufacturing industry is eager to implement the advancements of the fourth industrial revolution (Industry 4.0) due to the magnitude of the benefits it can provide. Hence, Industry 4.0 opens a wide avenue for researchers to explore possibilities in the field of the supply chain. This project focuses on building a decision framework for a supply chain system with disruptions. The impact of strategic decisions under the condition of unprecedented events for a vehicle routing problem (VRP) using simulation models is studied here. Those results help the supply chain managers in making sound decisions regarding different scenarios of disruption in VRP. To achieve this, multiple cases under different scenarios of facility disruption are considered. For all cases, the dependent parameter, namely, retailer service level and lost revenue, form the basis of the decision framework. The concept of live data is implemented by making retailer demand, current inventory at the depot, the position of the vehicle in the network and the current number of units in transit as the input data