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Reconnaissance missions are not only one of the vital modes of intelligence gathering methods, they are one of the most important contributors of military intelligence as well. They show the battlefield as it is to the commander. A simplified reconnaissance cycle includes the arrival of reconnaissance requests, planning of reconnaissance flights, flying the mission and exploitation of the films or images, and then dissemination of the intelligence reports. The reconnaissance cycle is modeled for four different scenarios (peace and war as situations, RF-4 and F-16 as configurations). There are two points of view regarding this cycle. The first is the reconnaissance requesters' view: they want to know the estimated time it would take for a request to be answered, based on the resources and other factors, before an actual request was made. The second is the reconnaissance squadron commanders' perspective: they want to respond to as many reconnaissance requests as possible. For that reason, they want to know and revise the ideal numbers of personnel and equipment. Analysis includes regression models and partition trees. When results are considered, we see that there is no common rule to determine which factors (either decision or noise) are the key determinants for each scenario. But we noticed that noise factors have much more impact on several measures of effectiveness than decision factors in each model.

Reconnaissance missions are not only one of the vital modes of intelligence-gathering methods; they are one of the most important contributors of military intelligence as well. They show the battlefield as it is to the commander. A simplified reconnaissance cycle includes the arrival of reconnaissance requests, planning of reconnaissance flights, flying the mission and exploitation of the films or images, and then dissemination of the intelligence reports. The reconnaissance cycle is modeled for four different scenarios (peace and war as situations, RF-4 and F-16 as configurations). There are two points of view regarding this cycle. The first is the reconnaissance requesters' view: they want to know the estimated time it would take for a request to be answered, based on the resources and other factors, before an actual request was made. The second is the reconnaissance squadron commanders' perspective: they want to respond to as many reconnaissance requests as possible. For that reason, they want to know and revise the ideal numbers of personnel and equipment. For the purpose of answering these questions, satisfying these requests, and having a better understanding about the reconnaissance cycle, Reconnaissance Squadron Workflow is modeled, experimented and analyzed in this thesis. Analysis includes regression models and partition trees. When results are considered, we see that there is no common rule to determine which factors (either decision or noise) are the key determinants for each scenario. But we noticed that noise factors have much more impact on several measures of effectiveness than decision factors in each model. Some of these noise factors could be controllable, including aircraft, camera and pod defect probabilities and their repair times. Therefore, some precautionary measures should be taken to reduce these defect probabilities and repair times. Specifically, in the RF-4 configuration models, pilot filming error is a significant factor, which shows that training of the pilots cannot be ignored. When the F-16 models are considered, we see that data link defect probability is a significant factor too. This suggests that special precautions should be taken to keep this capability working.

Anti-air warfare (AAW) is a primary naval warfare area. Using AAW tactics and concepts of operations, this research explores the most critical success factors of convoy operations. In this study, a discrete event simulation (DES) was built by modeling ships, and their sensors and weapons, to simulate convoy operations under air threat. Where classified data was unavailable, assumptions were made and approximations were used in constructing the ships, weapons, and sensors. The model was used to simulate over 1.5 million naval battles varying 99 input variables using sophisticated and systematically created data combinations. To select the input settings over a specific range of input variables, a nearly orthogonal nearly balanced (NOB) Latin hypercube design was used. The effects of these input changes on the outputs were analyzed using partition trees and nominal logistic regression. The primary response variable was the survival of the High Value Unit (HVU) as a binary outcome. According to the analysis, in a convoy operation under air threat, the surface-to-air missile (SAM) specifications of the screen ships, the staying power of the HVU, and the anti-ship missile (ASM) specifications of the enemy ships had the most significant effect on the survival of the HVU.

Marine Corps Installation and Logistics Command is seeking assistance to improve operations within Marine Corps Maintenance Production plants. The problem addressed in this thesis deals with production lines: there must be a proper balance of parts on hand and inventory costs to ensure optimal production output. This problem becomes increasingly difficult to solve as production-line complexity increases and overall budget flexibility decreases. As the Marine Corps enters a time of fiscal austerity and reduced overseas combat operations, it is critical to optimize its processes so major end items are refurbished in the quickest and most cost-effective manner, thereby ensuring maximum combat effectiveness. This research focuses on developing a proof of concept analytical tool to better facilitate order management of repair parts. This tool integrates optimization and discrete-event simulation. This construct can potentially provide an effective means in improving order management decisions. However, the effectiveness of the tool is contingent on accurate vehicle condition history, parts order history, and/or future estimated parts shipping dates. Information derived from the analysis can be used to make recommendations for reorder policy, enable future model development, and improve the overall maintenance production process.

Many simulation scenarios attempt to seek a balance between model fidelity and computational efficiency. Unfortunately, scenario realism and model level of detail are often reduced or eliminated due to the complexity of experimental design and corresponding limitations of computational power. Such simplifications can produce misleading results. For example, Radar Cross Section (RCS)effects in response to time-varying target aspect angle are dominant yet often ignored. This work investigates a new approach. A hybrid, high-fidelity sensor model can be achieved by using a Time-Step (TS) approach with precomputed a temporal response factors(such as RCS), each situated on active simulation entities that interact within an overall Discrete Event Simulation (DES) framework. This work further applies regression analysis to the cumulative results of 1,281 design points multiplied by 100 replications each to provide additional insight from massive results. Through a rigorous cross-validation (CV) for ensuring proper model selection, this new methodology adapts the best aspects of temporal control by each simulation approach, integrating multiple high-fidelity physically based models in a variety of tactical scenarios with tractable computational complexity. The meta-model generates statistically identical results as the baseline hybrid sensor model in mean with higher variation. This outcome sensor model performs in an efficient DES architecture by predicting only probability of detection (Pd) and time-to-detect (TTD) of the targets with the consideration of the range and the aspect relationship between the sensor and the target. The range and the aspect factors were ignored in current existing DES sensor models due to the overly simplified detection algorithm and the constraint of modeling complexity.

Many simulation scenarios attempt to seek a balance between model fidelity and computational efficiency. Unfortunately, scenario realism and model level of detail are often reduced or eliminated due to the complexity of experimental design and corresponding limitations of computational power. Such simplifications can produce misleading results. For example, Radar Cross Section (RCS) effects in response to time-varying target aspect angle are dominant yet often ignored. This work investigates a new approach. A hybrid, high-fidelity sensor model can be achieved by using a Time-Step (TS) approach with precomputed atemporal response factors (such as RCS), each situated on active simulation entities that interact within an overall Discrete Event Simulation (DES) framework. This work further applies regression analysis to the cumulative results of 1,281 design points multiplied by 100 replications each to provide additional insight from massive results. Through a rigorous cross-validation (CV) for ensuring proper model selection, this new methodology adapts the best aspects of temporal control by each simulation approach, integrating multiple high-fidelity physically based models in a variety of tactical scenarios with tractable computational complexity. The meta-model generates statistically identical results as the baseline hybrid sensor model in mean with higher variation. This outcome sensor model performs in an efficient DES architecture by predicting only probability of detection (Pd) and time-to-detect (TTD) of the targets with the consideration of the range and the aspect relationship between the sensor and the target. The range and the aspect factors were ignored in current existing DES sensor models due to the overly simplified detection algorithm and the constraint of modeling complexity.

Almost all flight simulators are centered on the problems that can occur during flight, whereas airport ground traffic problems are seldom addressed and are growing considerably. A number of precautions have been directed by the U.S. Federal Aviation Administration (FAA) to overcome these challenges, such as pilot training and adding taxiway indicator signals to help pilots follow specific paths when taxiing. Further work is needed. This thesis simulates the problem of Ground Traffic incursions. Discrete Event Simulation (DES) and the Viskit tool are used to build two scenarios describing the takeoff and the landing maneuvers including potential ground incidents. It also presents the different techniques used to build 3D graphics models for the airplanes and the airport environment using Extensible 3D (X3D) graphics. After running the simulation a number of times with different parameters, collected data support basic analysis and potential conclusions. This approach demonstrates a proof-of-concept capability worthy of future work.

This paper evaluates how quickly students can be trained to construct useful discrete-event simulation models using Excel The typical supply chain used by many large national retailers is described, and an Excel-based simulation model is constructed of it The set of programming and simulation skills required for development of that model are then determined we conclude that six hours of training are required to teach the skills to MBA students . The simulation presented here contains all fundamental functionallty of a simulation model, and so our result holds for any discrete-event simulation model. We argue therefore that Industry workers with the same technical skill set as students having completed one year in an MBA program can be quickly trained to construct simulation models. This result gives credence to the efficacy of Desktop Modeling and Simulation whereby simulation analyses can be quickly developed, run, and analyzed with widely available software, namely Excel.

This paper evaluates how quickly students can be trained to construct useful discrete-event simulation models using Excel The typical supply chain used by many large national retailers is described, and an Excel-based simulation model is constructed of it The set of programming and simulation skills required for development of that model are then determined we conclude that six hours of training are required to teach the skills to MBA students . The simulation presented here contains all fundamental functionallty of a simulation model, and so our result holds for any discrete-event simulation model. We argue therefore that Industry workers with the same technical skill set as students having completed one year in an MBA program can be quickly trained to construct simulation models. This result gives credence to the efficacy of Desktop Modeling and Simulation whereby simulation analyses can be quickly developed, run, and analyzed with widely available software, namely Excel.

Almost all flight simulators are centered on the problems that can occur during flight, whereas airport ground traffic problems are seldom addressed and are growing considerably. A number of precautions have been directed by the U.S. Federal Aviation Administration (FAA) to overcome these challenges, such as pilot training and adding taxiway indicator signals to help pilots follow specific paths when taxiing. Further work is needed. This thesis simulates the problem of Ground Traffic incursions. Discrete Event Simulation (DES) and the Viskit tool are used to build two scenarios describing the takeoff and the landing maneuvers including potential ground incidents. It also presents the different techniques used to build 3D graphics models for the airplanes and the airport environment using Extensible 3D (X3D) graphics. After running the simulation a number of times with different parameters, collected data support basic analysis and potential conclusions. This approach demonstrates a proof-of-concept capability worthy of future work.

The Cultural Geography (CG) Model is a low-resolution, agent-based discrete event social simulation tailored to specific operational environments. It is based on doctrine and social theory that are designed to represent the behavioral response of civilian populations in conflict environments. The current version of the CG Model does not represent key leader engagements (KLEs), which are activities between coalition military forces and host nation civilian personnel as means of obtaining information, influencing behavior, and building an indigenous base of support for coalition and government objectives. These capabilities are needed for additional tactical-level representation of the operational environment. This research develops a simulation model using Simkit to explore the feasibility of modeling KLEs using discrete event simulation. A total of 32 dynamic input factors are varied using a 512-design point design. Second-order regression metamodels and partition tree models are developed for simulation model output responses that track numbers of engagements, numbers of times knowledge is provided, numbers of campaigns, and numbers of captures and kills. These analytical models are used to verify the proper execution of the simulation model. Summary statistics are analyzed to gain further insights about the simulation model's behavior.

The Cultural Geography (CG) Model is a low-resolution, agent-based discrete event social simulation tailored to specific operational environments. It is based on doctrine and social theory that are designed to represent the behavioral response of civilian populations in conflict environments. The current version of the CG Model does not represent key leader engagements (KLEs), which are activities between coalition military forces and host nation civilian personnel as means of obtaining information, influencing behavior, and building an indigenous base of support for coalition and government objectives. These capabilities are needed for additional tactical-level representation of the operational environment. This research develops a simulation model using Simkit to explore the feasibility of modeling KLEs using discrete event simulation. A total of 32 dynamic input factors are varied using a 512-design point design. Second-order regression metamodels and partition tree models are developed for simulation model output responses that track numbers of engagements, numbers of times knowledge is provided, numbers of campaigns, and numbers of captures and kills. These analytical models are used to verify the proper execution of the simulation model. Summary statistics are analyzed to gain further insights about the simulation model's behavior.

NASA's human exploration initiatives will invest in technologies, public/private partnerships, and infrastructure, paving the way for the expansion of human civilization into the solar system and beyond. As it is has been for the past half century, the Kennedy Space Center will be the embarkation point for humankind's journey into the cosmos. Functioning as a next generation space launch complex, Kennedy's launch pads, integration facilities, processing areas, launch and recovery ranges will bustle with the activities of the world's space transportation providers. In developing this complex, KSC teams work through the potential operational scenarios: conducting trade studies, planning and budgeting for expensive and limited resources, and simulating alternative operational schemes. Numerous tools, among them discrete event simulation (DES), were matured during the Constellation Program to conduct such analyses with the purpose of optimizing the launch complex for maximum efficiency, safety, and flexibility while minimizing life cycle costs. Discrete event simulation is a computer-based modeling technique for complex and dynamic systems where the state of the system changes at discrete points in time and whose inputs may include random variables. DES is used to assess timelines and throughput, and to support operability studies and contingency analyses. It is applicable to any space launch campaign and informs decision-makers of the effects of varying numbers of expensive resources and the impact of off nominal scenarios on measures of performance. In order to develop representative DES models, methods were adopted, exploited, or created to extend traditional uses of DES. The Delphi method was adopted and utilized for task duration estimation. DES software was exploited for probabilistic event variation. A roll-up process was used, which was developed to reuse models and model elements in other less - detailed models. The DES team continues to innovate and expand DES capabilities to address KSC's planning needs.

This thesis uses conceptual modeling and discrete event simulation (DES) to assess warehousing efficiency for Marine Forces Storage Center (MFSC), Marine Corps Logistics Command. In April 2018, MFSC took possession of a new warehouse aboard Marine Corps Logistics Base-Albany to consolidate multiple warehousing operations into one warehouse facility to store, issue, receive, and account for military equipment. In August 2018, MFSC began the consolidation process and started moving all military equipment into one single facility. Today, MFSC conducts warehousing in a 57,000-square-foot storage facility, significantly smaller than the 120,000-square-foot facility it operated in previously. First, simulation event graphs illustrate the specific warehouse workflow behaviors and procedures. Simkit, a Java-based simulation package, is used to build a DES to represent the warehouse workflow processes, compute statistical outputs, and assess the warehousing performances based on different input values. This study analyzes the warehouse throughput using different statistical analysis methods, such as simulation experiments and predictive modeling, to reveal the input-output relationships that promote optimal warehouse performance. The warehouse workflow simulation model serves as a proof of concept and verification tool to promote workflow optimization, identify and mitigate bottlenecks in the workflow processes, and assist data driven decision-making to improve operating procedures.

Marine Corps Installation and Logistics Command is seeking assistance to improve operations within Marine Corps Maintenance Production plants. The problem addressed in this thesis deals with production lines: there must be a proper balance of parts on hand and inventory costs to ensure optimal production output. This problem becomes increasingly difficult to solve as production-line complexity increases and overall budget flexibility decreases. As the Marine Corps enters a time of fiscal austerity and reduced overseas combat operations, it is critical to optimize its processes so major end items are refurbished in the quickest and most cost-effective manner, thereby ensuring maximum combat effectiveness. This research focuses on developing a proof of concept analytical tool to better facilitate order management of repair parts. This tool integrates optimization and discrete-event simulation. This construct can potentially provide an effective means in improving order management decisions. However, the effectiveness of the tool is contingent on accurate vehicle condition history, parts order history, and/or future estimated parts shipping dates. Information derived from the analysis can be used to make recommendations for reorder policy, enable future model development, and improve the overall maintenance production process.

The Cultural Geography (CG) Model is a low-resolution, agent-based discrete event social simulation tailored to specific operational environments. It is based on doctrine and social theory designed to represent the behavioral response of civilian populations in conflict environments. The current version of the CG Model does not represent key leader engagements (KLE), which are activities between coalition military forces and host nation civilian personnel, as means of obtaining information, influencing behavior, and building an indigenous base of support for coalition and government objectives. These capabilities are needed for additional tactical level representation of the operational environment. This research develops a simulation model using Simkit to explore the feasibility of modeling KLEs using discrete event simulation. A total of 32 dynamic input factors are varied using a 512-design point design. Second-order regression metamodels and partition tree models are developed for simulation model output responses that track numbers of engagements, numbers of times knowledge is provided, numbers of campaigns, and numbers of captures and kills, these analytical models are used to verify the proper execution of the simulation model. Summary statistics are analyzed to gain further insights about the simulation models behavior.

This research improved a simulation that models a single operator responsible for multiple UAV rovers. The improvement calibrated the model by increasing the realism of its expected time that the target will be within the field of view of a UAV's camera and how much of that will be observed by an operator that has multiple tasks to perform throughout the mission.The calibration was derived from multiple flight tests, by using a Field of View Algorithm in MATLAB and by visually recording times for loiter loops by hand. It was determined that the target will be within the field of view of a UAV loitering in a circular pattern between 62% and 66% of the overall loiter time. For an 8 hour beyond line of sight mission, the model's optimal results were 145 min of Value Added Time in low wind conditions and 137 min in high wind. For an 8 hour within line of sight mission, the optimal result was 287 min in low wind conditions and 268 min in high wind.

Anti-air warfare (AAW) is a primary naval warfare area. Using AAW tactics and concepts of operations, this research explores the most critical success factors of convoy operations. In this study, a discrete event simulation (DES) was built by modeling ships, and their sensors and weapons, to simulate convoy operations under air threat. Where classified data was unavailable, assumptions were made and approximations were used in constructing the ships, weapons, and sensors. The model was used to simulate over 1.5 million naval battles varying 99 input variables using sophisticated and systematically created data combinations. To select the input settings over a specific range of input variables, a nearly orthogonal nearly balanced (NOB) Latin hypercube design was used. The effects of these input changes on the outputs were analyzed using partition trees and nominal logistic regression. The primary response variable was the survival of the High Value Unit (HVU) as a binary outcome. According to the analysis, in a convoy operation under air threat, the surface-to-air missile (SAM) specifications of the screen ships, the staying power of the HVU, and the anti-ship missile (ASM) specifications of the enemy ships had the most significant effect on the survival of the HVU.