This dissertation examines the influence of executive ideological orientation on state foreign policy behavior. I advance an analytical model which asserts that foreign policy decisionmakers act in a manner consistent with the ideological principles presented in their political platforms, party manifestos, and their voters' expectations. Thus, I assert that within developed democracies, the further right a government is, the higher the propensity to behave more aggressively. Oppositely, the further left a government is, the more likely it is to behave more cooperatively. I empirically analyze this theoretical argument by developing three models where the foreign policy behavior is measured uniquely in each separate model. I estimate executive ideology by using two proxies: one which estimates the overall ideology of the executive while the other captures only the foreign policy dimension of executive ideology. To test the hypotheses derived from the theoretical model, I create a new dataset of responses to international crises. Foreign policy behavior is operationalized as an ordinal variable which takes into account a complex range of actions that governments take in the international arena, such as providing aid, mediation, non intervention, condemnation, sanctions, and the use of force. I employ Logit and Orderd Logit statistical analyses on a large-N cross national model. My dissertation focuses on all 22 OECD countries, during the period 1977 to 2001. The empirical findings partially support my theoretical argument, contingent upon the proxy used for executive ideology and the way state behavior is estimated. I find consistent support to my argument if executive ideology is estimated with the proxy which contains only the party manifestos' foreign policy variables.

The aim of this thesis is to develop a tractable method for approximating the steady-state behavior of continuous-review base-stock controlled assembly systems with Poisson demand arrivals and manufacturing and assembly facilities modeled as Jackson networks. One class of systems studied is to produce a single type of finished product assembling a number of components and another class is to produce two types of finished products allowing component commonality. The performance measures evaluated are the expected backorders, fill rate and the stockout probability for finished product(s). A partially aggregated but exact model is approximated assuming that the state-dependent transition rates arising as a result of the partial aggregation are constant. This approximation leads to the derivation of a closed-form steady-state probability distribution, which is of product-form. Adequacy of the proposed model in approximating the steady-state performance measures is tested against simulation experiments over a large range of parameters and the approximation turns out to be quite accurate with absolute errors of 10% at most for fill rate and stockout probability, and of less than 1.37 (&amp / #8776 / 2) requests for expected backorders. A greedy heuristic which is proposed to be employed using approximate steady-state probabilities is devised to optimize base-stock levels while aiming at an overall service level for finished product(s).

In this thesis, an approximation is proposed to evaluate the steady-state performance of kanban controlled assembly systems. The approximation is developed for the systems with two components making up an assembly. Then, it is extended to systems with more than two components. A continuous-time Markov model is aggregated keeping the model exact, and this aggregate model is approximated replacing some state-dependent transition rates with constant rates. Decomposition of the approximate aggregate model into submodels guarantees product-form steady-state distribution for each subsystem. Finally, submodels are combined in such a way that the size of the problem becomes independent of the number of kanbans. This brings about the computational advantage in solving the combined model using numerical matrix-geometric solution algorithms. Based on the numerical comparisons with simulation, the exact model, an approximate aggregate model and another approximation in a previous study in the literature, the approximation is observed to be good in terms of accuracy with respect to computational burden and has the potential to be a building block for the analysis of systems that are more complex but closer to real-life applications.