Browsing by Author "Michael G. Kay, Committee Member"

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Analysis of Fuel Consumption for an Aircraft Deployment with Multiple Aerial Refuelings
(2006-06-08) Bush, Brett Alan; Yahya Fathi, Committee Member; Jeffrey R. Thompson, Committee Member; Michael G. Kay, Committee Member; Thom J. Hodgson, Committee Chair; Russell E. King, Committee Member
The purpose of the research has been to derive an algorithm that finds optimal aerial refueling segments (non-instantaneous) for a single aircraft deployment while also accounting for atmospheric winds. There are two decision variables: (1) Where to locate the refueling segments? (2) How much fuel to offload at each refueling segment? Later in the dissertation, a third decision variable is explored: How much fuel to load onto the aerial refueling aircraft? In previous research, the problem of having a single aircraft deployment with one instantaneous aerial refueling has been explored and solved. This paper piggybacks on that research and extends it. The first step (Problem P1) is deriving an algorithm that finds the optimal aerial refueling points for a single aircraft deployment with multiple instantaneous aerial refuelings. In the next step (Problem P2), one assumes aerial refueling is not instantaneous (in an effort to make the problem and solution more realistic), but requires some time frame depending on an offload rate. In problem (P2), optimal refueling segments are found (versus optimal refueling points). In the last problem (P3), one looks at a very similar algorithm that factors the winds aloft into the minimization algorithm. Finally, this paper looks at three distinct deployment scenarios with two aerial refuelings required. All of the scenarios were first planned by the U.S. Air Force and the results given to the author. Potential fuel and cost savings associated with using the aforementioned algorithms instead of current methods are then analyzed.
Assessing the Impact of Strategic Safety Stock Placement in a Multi-echelon Supply Chain
(2006-12-16) Bryksina, Elena Alexandrovna; Jennifer Blackhurst, Committee Member; Michael G. Kay, Committee Member; Donald P. Warsing, Committee Co-Chair; Robert B. Handfield, Committee Chair
The objective of this study is to develop prescriptions for strategically placing safety stocks in an arborescent supply chain in which there are moderate to severe risks of disruptions in supply. Our work builds off of recently published work by Graves and Willems (2003) that demonstrates that a simple-to-compute, congestion-based adjustment to supply lead times, first developed by Ettl et al. (2000), can be embedded in a non-linear optimization problem to minimize total investment in safety stock across the entire supply chain. We are interested in investigating how the Graves and Willems (GW) model performs under uncertainty in supply. We first propose an adjustment to the model (Mod-GW) by considering two types of fulfillment times, a normal fulfillment time and a worst possible fulfillment time , which allows us to account for supply uncertainty, or disruptions in supply. We evaluate the performance of GW and Mod-GW using Monte Carlo simulation and, using motivation from Timed-Petri Net analysis, develop an Informed Safety Stock Adjustment (ISSA) algorithm to compute the additional buffer stock levels necessary to improve downstream service performance to the target level. We find that the service performance of the Mod-GW solution is most sensitive to the probability of disruption at any node in the supply chain, requiring higher safety stock adjustments through ISSA as this probability increases. In particular, the relative value of the holding costs for components and finished goods—and the resulting impact on where safety stock is held in the network—is an important moderating factor in determining the level of service performance degradation of the Mod-GW solution as either , the probability of disruption at node j, or , the ratio of the disrupted and normal lead times, increases (i.e., as disruptions exert more impact on the network). The Informed Safety Stock Adjustment algorithm generally suggests a sufficient complementary amount to the safety stock.
The Effects of Automation on Team Performance and Team Coordination
(2002-07-15) Wright, Melanie Clay; Michael G. Kay, Committee Member; David B. Kaber, Committee Chair; Gary Mirka, Committee Member; Sharolyn Converse Lane, Committee Member
The advancement of technology has led to an increased use of automation in a number of work domains, including team environments. However, assessment of the effects of automation on teamwork has been primarily limited to the aviation domain (comparing early conventional aircraft models with more advanced aircraft cockpits) and studies have produced conflicting information regarding the impact of automation on team performance, communication, and coordination. To more fully understand the implications of automation on system performance, researchers have begun to develop taxonomies and models of automation so that specific forms of automation can be defined and evaluated. A model proposed by Parasuraman et al. (2000) considers automation as it is applied to stages of information processing, including information acquisition, information analysis, decision selection, and action implementation. The objective of this research was to evaluate the effects of automation as applied to these different stages of information processing on the performance and coordination of teams in a complex decision making task. A simulated Theatre Defense Task in which teams protect a home base from enemy attack was used as a test-bed for this evaluation. Two team members were required to work together to share information in order to successfully complete the task. One team member monitored incoming aircraft on a radarscope and used missiles to shoot down enemy aircraft. A second team member monitored information provided by reconnaissance aircraft to classify the incoming aircraft as enemy or friendly. Four automation conditions were designed that compared different degrees of information acquisition, information analysis, and decision selection automation. Two levels of difficulty, determined by the number of aircraft presented, were used in the experiment. Dependent measures for the experiment included team effectiveness, quantity of team communication, team coordination ratings by outside observers, and task and team workload ratings. The results of the experiment revealed that different forms of automation have different effects on teamwork. Automation of information acquisition caused a decrease in the total amount of communication and an increase in the ratio of information transferred compared to information requested between team members. Automation of information analysis resulted in higher team coordination ratings. Automation of decision selection led to better team effectiveness under low levels of task difficulty but at the cost of higher workload. The fact that differing forms of automation had different influences on team performance in this research aids in explaining conflicting historical findings regarding the effects of automation on teamwork. The results of this research may have utility for the design of complex systems used in team environments.
Global Sensor Management: Enumeration of All Possible Allocations of Military Surveillance Assets
(2009-01-21) Mears, Curtis Michael; Thom J. Hodgson, Committee Chair; Russell E. King, Committee Member; Michael G. Kay, Committee Member
The United States has been interested in tracking the activities of its enemies, natural events, and even allies since before the Cold War, to protect its people from threats. To this end, the Department of Defense has tasked the Strategic Command (STRATCOM) with the daunting task of managing a majority of the surveillance assets used in this mission. STRATCOM needs to be sure that the probability of successfully protecting the United States and its allies is held to a high standard. Success depends on assetsâ€™ abilities to accomplish each function successfully when an event occurs. These assets can have the ability to perform multiple functions. The question of concern here is how to assign the assets with multiple abilities to optimize the probability of success. This Thesis explains how to calculate the probabilities for a given allocation. Then it explains how a Visual Basic program enumerates each possible allocation, the inputs, options, and outputs of the program. This would allow a decision maker to pick the level of probability desired in each task.
Markov Model for Stock Market Buy and Sell Strategy
(2004-02-02) West, Donald Ray; Thom J. Hodgson, Committee Co-Chair; C. Thomas Culbreth, Committee Member; Michael G. Kay, Committee Member; Russell E. King, Committee Co-Chair
Approximately 50% of the households in America invest in the stock market. In many cases the investor's buy and hold strategy leads to negative returns. Given the stock market fluctuates up and down as time progresses, analysts examine stock prices, volumes traded and ratios to recommend buy and sell opportunities. The pattern of price and volume changes provides input for the analysts' recommendations. Using these patterns in a Markov model, this dissertation contains an intensive analysis of 41 securities over a 13-year period. The model establishes states of change in price and volume and calculates the best investor action for an individual security. The selection of the proper security enhances the investor's probability of achieving an exceptional return. The research examines the correlation of price and volume characteristics to overall return. With the proper correlation, higher yielding securities may be selected. Once selected, the dissertation's research recommends when to switch from one security to another security. Also, periods of staying in a cash position are recommended. Overall this model outperforms the average yearly buy and hold return of eleven percent by about four additional percentage points. Even subtracting the cost of the transactions, the model buys and sells the securities to obtain the additional return.
Modeling Inventory Systems with Imperfect Supply
(2010-07-20) Wangwatcharakul, Worawut; Russell E. King, Committee Chair; Kristin A. Thoney-Barletta, Committee Member; Donald P. Warsing, Committee Co-Chair; Michael G. Kay, Committee Member
We study inventory systems operating under an infinite-horizon, periodic-review base-stock control policy with stochastic demand and imperfect (i.e., less than 100% reliable) supply. We model demand using a general discrete distribution and replenishment lead time using a geometric distribution, resulting from a Bernoulli trial-based model of supply uncertainty. We develop a computational approach using a discrete time Markov process (DTMP) model to minimize the total system cost and obtain the optimal base-stock level when the backorder penalty is given. We develop a general, recursive solution for the steady state probability of each inventory level and use this to find the optimal base-stock level in this setting. Moreover, for specific demand distributions we are able to develop closed-form solutions for these outcomes. The lead-time demand (LTD) distribution can also be obtained from these recursive equations to determine the base-stock level when a target customer service level is specified in lieu of a backorder penalty cost. We conduct extensive computational experiments to observe the robustness of various approximate solutions under two scenarios for the lead-time distribution. The first scenario assumes a geometric lead time. The second scenario considers a general lead-time distribution. We conduct computational experiments to observe the conditions in which the DTMP model performs well, including situations where the demand and the lead-time distributions are specified separately, and where the LTD distribution is given and follows either a Beta distribution or a bimodal distribution. Finally, for a two-location inventory system consisting of a single retailer supplied by a single distributor, whose supply ultimately comes from an unreliable supplier upstream, we propose a computational approach to determine optimal or near-optimal base-stock levels at the retailer and distributor. We develop two decomposition-based approximation methods, solving the separate single-site inventory problems (distributor, retailer) sequentially, but with different methods to compute the implied backorder penalty at the distributor that induces near-optimal base-stock levels at both locations.