The U.S. Army distributes its 51,000 competitive category officers among manning targets specified by location, rank and skill that change over time in response to changing requirements. The officer inventory also changes over time and does not exactly match the manning target requirements. The Army responds to imbalances by redistributing officers in order to provide each location with the minimum required officers while minimizing the number of unfilled targets and excess officers at each location. This thesis focuses on branch officers, branch targets and generalist targets with ranks from Branch Qualified Captain to Colonel. Using data provided by the Army, we formulate an integer programming model called DISTRIBUTOR. When DISTRIBUTOR allows all officers in the inventory to move, it finds only 340 unfilled targets but this requires 4,688 or 28% of the inventory to move. We reduce the number of moves by using DISTRIBUTOR in two sequential steps. The first step optimally distributes officers at each location and identifies the excess officers and unfilled targets at each location. The second step takes the excess officers and distributes them to unfilled targets at other locations. The two-step leaves only 346 targets unfilled (6 more) but requires only 1,373 or 8% of the inventory to move. By allowing rank substitution DISTRIBUTOR can reduce the unfilled targets to 70.

Approved for public release, distribution unlimited / We introduce a tri-level defender-attacker-defender optimization model that prescribes how Iraq's oil infrastructure can, over time, be expanded, protected, and operated, even in the face of insurgent attacks. The outer-most defender model is a mixedinteger program that, given a set of anticipated insurgent attacks, specifies a quarterly capital expansion, defense, and operation plan to maximize oil exports over a decade-long planning horizon. The intermediate attacker model, observing the outer defender plans, is a mixed integer program that re-optimizes insurgent attacks to minimize export flow. The inner-most defender model is a linear program that re-directs flow in response to insurgent damage. We use open-source descriptions of current Iraqi oil infrastructure and reasonable estimates of the costs to expand capacity and/or defend operating assets, and reduce vulnerability to attacks. We solve this tri-level model by converting it into an equivalent bi-level one, and applying decomposition. For a range of scenarios, we determine the best allocation of effort between improving oil export infrastructure, and defending it.

This thesis addresses the problem of optimal evacuation of a naval ship. We propose the use of a dynamic escape-route system which employs a signaling system to adapt the emergency egress process to the instigating contingency. The evacuation process is represented by a nonlinear network optimization model with an objective function that integrates two conflicting goals: the average evacuation time and the ship's integrity. The nonlinearity in the model results from (a) speed being a nonlinear function of concurrent flow on passageways, and (b) delays caused by opening closures. We also account for counter-flows and passageways used by repair parties. The problem is heuristically solved through an iterative process that updates speeds and delays as it proceeds, and dynamically adds valid inequalities to avoid counter-flows. A bound on the solution quality is obtained by solving the problem under optimistic conditions. Compared to static routes in a modern frigate, model solutions show that dynamic routes can improve the average evacuation time by 20%, reduce the time of the last evacuee by 25%, and improve ship integrity. We also demonstrate that even greater improvements are achievable with minor design changes in the ship.

The objective of this research effort is to design and implement a laboratory and establish a research method focused on scientific evaluation of human factors considerations for military individual equipment under both laboratory and field conditions. This integrated approach for laboratory and field conditions is the first of its kind for military human factors research, enabling an unparalleled degree of scientific rigor in the collection of empirical human factors data. This effort includes: 1) a state-of-the-art usability laboratory designed specifically for quantitatively evaluating military individual equipment / 2) a rugged, embarkable, fully self-contained, portable usability laboratory for field research in military environments / 3) a codified manual for using the two main configurations (stationary and portable) of the usability laboratory, written for the beginning usability researcher / 4) a set of validated procedures for applying sound human factors principles, and traditional and non-parametric statistics to the specific problem of usability testing of military individual equipment / 5) a proof-of-concept practical application of the laboratory and procedures to a specific problem, namely the usability testing of ruggedized personal digital assistants (RPDAs) designed for United States Special Forces operations.

Approved for public release; distribution is unlimited / Recent events in the United States of America and Pakistan have exposed the shortcomings of existing planning in relief and humanitarian assistance in the face of large-scale natural disasters. This thesis develops a two-stage stochastic optimization model to provide guidance in the pre-positioning of relief units and assets, where budget, physical limitations and logistics are taken into account. Stochastic data include the numbers of survivors in each potential affected area (AA), the amount of commodities that needs to be delivered to each AA and the transportation time from each relief location (which reflects sceanrios where, for example, roads are blocked). As first-stage decisions, we consider the expansion of warehouses, medical facilities and their health care personnel, as well as ramp space to facilitate aircraft supply of commodities to the AAs. The second-stage is a logistic problem respresented as a network, where maximizing expected rescued survivors and delivery of required commodities are the driving goals. This is accomplished through land, air and sea transportation means (e.g., CH-53 helicopters configured for rescue missions), as well as relief workers. The model has been successfully assessed on notional scenarios and is expected to be tested on realistic cases by personnel who are involved in relief planning. / Outstanding Thesis

The 2006 Quadrennial Defense Review has emphasized the role of humanitarian assistance missions in winning the Global War on Terror. U.S. Pacific Fleet operates in an area prone to both terrorist recruitment and sudden-onset natural disasters that require humanitarian assistance and disaster relief operations. The U.S. Navy has unique capabilities to deliver first-response humanitarian assistance. This thesis develops and suggests prepositions for humanitarian assistance pack-up kits that contain emergency relief material commonly used in these missions in order to expedite delivery to those impacted by a disaster.

We describe major enhancements to the missile defense planning aid "JOINT DEFENDER" (JDEF). JDEF is the first system that shows how to evaluate and exploit new and anticipated improvements in interceptors, long-range surveillance and tracking capabilities, networked communications, and the ability of detecting platforms to cue intercepting ones downrange. We want to improve system-wide effectiveness, gauged here by the reduction of expected damage inflicted. We defend an asset list (DAL) of targets, characterized by their locations and values to us. Our defenders include pure "LOOKERs," radars and sensors of enemy missile launches, and "SHOOTERs," platforms with means to both detect and intercept enemy launches. JDEF optimally positions platforms that can be moved, and prescribes what each platform should do. JDEF can estimate the value to either opponent of secrecy, deception, or intelligence. JDEF is the only missile defense planning system using formal optimization. Among many advantages this conveys, JDEF is able to unambiguously quantify the difference among disparate plans. Although the JDEF planner can manually control any detail, the planner is well advised to let optimization suggest where to start.

The Armed Services Vocational Aptitude Battery (ASVAB) is a test that approximately 700,000 students in 12,000 high schools take each year to determine military occupation placement. Form Assembly for the ASVAB refers to the selection of 20-35 questions, known as items, from an item pool of approximately 300 items to create a paper and pencil test in one of its ten topics. Previous research formulates form assembly as an Integer Linear Program (ILP). The current ASVAB mostly uses a Computer Adaptive Test (CAT), which estimates an examinee's ability after the examinee answers each item and selects the next item based on prior performance. The current CAT-ASVAB implementation does not control the number of items selected from each subject (taxonomy group) for a test. This thesis introduces ILPs, previously used for form assembly, that impose taxonomy restrictions and applies them to the CAT-ASVAB. We create four ILP variations and test them against the current method of item selection, by simulating 3,500 examinees (500 examinees each for seven given ability levels). The results show that all of the ILPs have acceptable solution times for CAT use, and taxonomy restrictions can be imposed while also having more even exposure rates (the number of times an item is administered divided by the number of examinees) than the current implementation of the CAT-ASVAB. A variation that relaxes most of the binary variables and constrains the difficulty of each item to be within a predetermined magnitude of the current ability estimate, performs the best in terms of item exposure (for both under and over-utilized items) and error between an examinee's estimated ability level and actual ability level. / Defense Manpower Data Center author (civilian).

With the advent of easily accessible, deployable, and usable 802.11 technology, users can connect and network with practically any infrastructure that exists today. Due to that simplicity and ease of use, it only seems logical that the military and tactical users should also employ these technologies. The questions regarding 802.11 network performances in a hostile and signal-unfriendly environment (i.e., high temperature and high humidity) have yet to be answered. The goal of this thesis is to quantify 802.11 network capabilities, in terms of throughput, while it is employed in those areas. Ultimately, the objective is to produce statistical models able to represent any variations in the 802.11 signals and network due to those environmental factors.

<p> Bullwhip effect in Pricing (BP) refers to the amplified variability of prices in a supply chain. When the amplification takes place from the upstream (i.e. supplier&rsquo;s side) towards the downstream (i.e. retail side) of a supply chain, this is referred as the Reverse Bullwhip effect in Pricing (RBP). On the other hand, if an absorption in price variability takes place from the upstream towards the downstream of a supply chain, we refer this phenomenon as the Forward Bullwhip effect in Pricing (FBP). </p><p> In this research, we analyze the occurrence of BP in the case of different game structures and supply chain contracts. We consider three game scenarios (e.g. simultaneous, wholesale-leading, and retail-leading) and two supply chain contracts (e.g. buyback and revenue-sharing). We analyze the occurrence of BP for some common demand functions (e.g. log-concave, linear, isoelastic, negative exponential, logarithmic, logit etc.). We consider some common pricing practices such as a fixed-dollar and fixed-percentage markup pricing and the optimal pricing game. </p><p> We discuss the conditions for the occurrence of BP based on the concavity coefficient and the cost-pass-through. We analyze the price variation analytically and then illustrate the results through numerical simulations. We extend the cost-pass-through analysis for a N-stage supply chain and conjecture the BP ratios for a N-stage supply chain. We compute cost-pass-through under both a buyback and a revenue-sharing contract. We compared the BP ratios between a revenue-sharing contract and a no-contract cases. We include both the deterministic and stochastic demand functions with an additive and a multiplicative uncertainty. </p><p> The results indicate that the occurrence of BP depends on the concavity coefficient of the demand functions. For example: RBP occurs for an isoelastic demand, FBP occurs for a linear demand, No BP occurs for a negative exponential demand etc. This study also shows that, FBP and RBP occur in varying magnitude for different types of games and supply chain contracts. The comparison between the stochastic model and the risk-less model shows that the additive or multiplicative uncertainty changes the price fluctuation. The comparison between contract and no-contract cases shows that the contract minimizes FBP or RBP in some cases.</p>