The purpose of this study was to develop and test an optimization model that will provide a deployment plan of emission reduction technologies to reduce emissions from non-road equipment. The focus of the study was on the counties of Texas that have nonattainment (NA) and near-nonattainment (NNA) status. The objective of this research was to develop methodologies that will help to deploy emission reduction technologies for non-road equipment of TxDOT to reduce emissions in a cost effective and optimal manner. Three technologies were considered for deployment in this research, (1) hydrogen enrichment (HE), (2) selective catalytic reduction (SCR) and (3) fuel additive (FA). Combinations of technologies were also considered in the study, i.e. HE with FA, and SCR with FA. Two approaches were investigated in this research. The first approach was "Method 1" in which all the technologies, i.e. FA, HE and SCR were deployed in the NA counties at the first stage. In the second stage the same technologies were deployed in the NNA counties with the remaining budget, if any. The second approach was called "Method 2" in which all the technologies, i.e. FA, HE and SCR were deployed in the NA counties along with deploying only FA in the NNA counties at the first stage. Then with the remaining budget, SCR and HE were deployed in the NNA counties in the second stage. In each of these methods, 2 options were considered, i.e. maximizing NOx reduction with and without fuel economy consideration in the objective function. Thus, the four options investigated each having different mixes of emission reduction technologies include Case 1A: Method 1 with fuel economy consideration; Case 1B: Method 1 without fuel economy consideration; Case 2A: Method 2 with fuel economy consideration; and Case 2B: Method 2 without fuel economy consideration and were programmed with Visual C++ and ILOG CPLEX. These four options were tested for budget amounts ranging from $500 to $1,183,000 and the results obtained show that for a given budget one option representing a mix of technologies often performed better than others. This is conceivable because for a given budget the optimization model selects an affordable option considering the cost of technologies involved while at the same time maximum emission reduction, with and without fuel economy consideration, is achieved. Thus the alternative options described in this study will assist the decision makers to decide about the deployment preference of technologies. For a given budget, the decision maker can obtain the results for total NOx reduction, combined diesel economy and total combined benefit using the four models mentioned above. Based on their requirements and priorities, they can select the desired model and subsequently obtain the required deployment plan for deploying the emission reduction technologies in the NA and NNA counties.

A direction-finding system with multiple direction finders (DFs) is a military intelligence system designed to detect the positions of transmitters of radio frequencies. This dissertation studies three decision problems associated with the direction-finding system. The first part of this dissertation is to prescribe DF deployment to maximize the effectiveness with which transmitter positions are estimated in an area of interest (AOI). Three methods are presented to prescribe DF deployment. The first method uses Stansfield’s probability density function to compute objective function coefficients numerically. The second and the third employ surrogate measures of effectiveness as objective functions. The second method, like the first, involves complete enumerations; the third formulates the problem as an integer program and solves it with an efficient network-based label-setting algorithm. Our results show that the third method, which involved use of a surrogate measure as an objective function and an exact label-setting algorithm, is most effective. The second part of this dissertation is to minimize the number of DFs to cover an AOI effectively, considering obstacles between DFs and transmitters. We formulate this problem as a partial set multicover problem in which at least -fraction of the likely transmitter positions must be covered, each by at least direction finders. We present greedy heuristics with random selection rules for the partial set multicover problem, estimating statistical bounds on unknown optimal values. Our results show that the greedy heuristic with column selection rule, which gives priority for selecting a column that advances more rows to k-coverage, performs best on the partial set multicover problems. Results also show that the heuristic with random row and column selection rules is the best of the heuristics with respect to statistical bounds. The third part of this dissertation deals with the problem of deploying direction finders with the goal of maximizing the effectiveness with which transmitter positions can be estimated in an AOI while hedging against enemy threats. We present four formulations, considering the probability that a direction finder deployed at a location will survive enemy threats over the planning horizon (i.e., not be rendered inoperative by an attack). We formulate the first two as network flow problems and present an efficient label-setting algorithm. The third and the fourth use the well-known Conditional Value at Risk (CVaR) risk measure to deal with the risk of being rendered inoperative by the enemy. Computational results show that risk-averse decision models tend to deploy some or all DFs in locations that are not close to the enemy to reduce risk. Results also show that a direction-finding system with 5 DFs provides improved survivability under enemy threats.