The petroleum industry has one of the most complex supply chains in the world. A unique characteristic of Petroleum Supply Chain (PSC) is the high degree of uncertainty which propagates through the network. Therefore, it is necessary to develop quantitative models aiming at optimizing the network and managing logistics operations. This work proposes a deterministic Mixed Integer Linear Program (MILP) model for downstream PSC to determine the optimal distribution center (DC) locations, capacities, transportation modes, and transfer volumes. Three products are considered in this study: gasoline, diesel, and jet fuel. The model minimizes multi-echelon multi-product cost along the refineries, distribution centers, transportation modes and demand nodes. The relationship between strategic planning and multimodal transportation is further elucidated. Furthermore, this work proposes a two stage Stochastic Mixed Integer Linear Program (SMILP) models with recourse for PSC under the risk of random disruptions, and a two stage Stochastic Linear Program (SLP) model with recourse under the risk of anticipated disruptions, namely hurricanes. Two separate types of mitigation strategies – proactive and reactive – are proposed in each model based on the type of disruption. The SMILP model determines optimal DC locations and capacities in the first stage and utilizes multimode transportation as the reactive mitigation strategy in the second stage to allocate transfer volumes. The SLP model uses proactive mitigation strategies in the first stage and employs multimode transportation as the reactive mitigation strategy. The goal of both stochastic models is to minimize the expected total supply chain costs under uncertainty. The proposed models are tested with real data from two sections of the U.S. petroleum industry, PADD 3 and PADD 1, and transportation networks within Geographic Information System (GIS). It involves supply at the existing refineries, proposed DCs and demand nodes. GIS is used to analyze spatial data and to map refineries, DCs and demand nodes to visualize the process. Sensitivity analysis is conducted to asses supply chain performance in response to changes in key parameters of proposed models to provide insights on PSC decisions, and to demonstrate the impact of key parameters on PSC decisions and total cost. / Upper Great Plains Transportation Institute (UGPTI) / Mountain Plains Consortium (MPC)

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The primary purpose of this study is to develop a realistic model of an oil company which can be used to evaluate alternative systems of petroleum accounting. The model is used to simulate several accounting variables produced by the full cost and successful efforts methods of accounting. The simulated accounting variables are net operating income, net oil properties, operating expenses and return on investment. This study also evaluates the oil company models used in prior petroleum accounting research. There are currently two systems of petroleum accounting (full cost accounting and successful efforts accounting) that are generally accepted. The full cost method basically requires the capitalization of all exploration and development costs while the successful efforts method does not permit the capitalization of either geological survey costs or the costs of dry exploratory wells. A petroleum accounting model should satisfy three requirements. The model should incorporate all significant revenues and expenditures. The values of these revenues and expenditures should be realistic and the timing of the business transactions must be realistic. Earlier accounting studies employed deterministic and stochastic models which failed to satisfy one or more of the three requirements of a petroleum accounting model. Because there are many significant differences between these models and the oil companies which they claim to represent, the findings of these studies cannot be considered to be realistic. The model developed for this study represents the exploration, development and production activities of an oil company. Multinomial probability distributions are used to model exploratory drilling success. A binomial distribution is used to model development drilling success. Exponential decline curves are used to represent oil production. The model displays the type of behavior that is predicted by the economic theory of exhaustible resources and it satisfies the three modeling requirements stated previously.

Philosophiae Doctor - PhD / Coordination, Collaboration, and Cooperation, (C04); Community (C01) Driven Development (D2) minus Historical Institutionalisation (HI) is equal to Policy Implementation Effectiveness (PIE), is recommended by the researcher to improve results. Trends in compiled data were analysed with regards to legislative oversight and accountability in the petroleum industry, using the OECD/ DAC evaluation criteria. In terms of relevance, effectiveness, and efficiency, the researcher discovered a partial link between the activities performed by parliamentarians representing the petroleum industry where HDSA transformation is concerned. It was concluded that electrical energy generation and distribution throughout the five-year period drew more focus in terms of oversight than on other sectors. It was further noted that oversight and accountability could have been applied more efficiently if input were linked to policy objectives and activities. This resulted in only partial or limited HDSA transformation in the petroleum industry. When it comes to sustainability the researcher argued that despite the apparent long-term stability of parliamentary activities, such stability may not translate to sufficient oversight and accountability to ensure sustainable transformation in the petroleum industry. It was concluded that while oversight and accountability was being applied to one specific arena, other areas in need of transformation did not receive sufficient, or indeed any, legislative oversight. The facilitation model: Communication, Coordination, Collaboration, and Cooperation, (C04); and Community (C01) Driven Oversight and Accountability (D1OA) is recommended by the researcher to improve effective oversight and accountability. Thus, Effective (A + O) = C04 + C01 /D1OA equals to (A + O)e = C205 / D1OA.

Thesis (M.B.A.)--Ohio State University, 1984. / Includes bibliographical references (leaves 54-58). Available online via OhioLINK's ETD Center.

Thesis--School of Advanced Airpower Studies, Maxwell Air Force Base, Ala., 1992-93. / Title from title screen (viewed Nov. 7, 2003). "June 1994." Includes bibliographical references.

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Thesis (M.A.)--Dalhousie University (Canada), 2007. / Includes bibliographical references.