A hybrid Delphi-SWOT paradigm for oil and gas pipeline strategic planning in Caspian Sea basin

The Caspian Sea basin holds large quantities of both oil and natural gas that could help meet the increasing global demand for energy resources. Consequently, the oil and gas potential of the region has attracted the attention of the international oil and gas industry. The key to realizing the energy producing potential of the region is the development of transnational export routes to take oil and gas from the landlocked Caspian Sea basin to world markets. The evaluation and selection of alternative transnational export routes is a complex multi-criteria problem with conflicting objectives. The decision makers (DMs) are required to consider a vast amount of information concerning internal strengths and weaknesses of the alternative routes as well as external opportunities and threats to them. This paper presents a hybrid model that combines strength, weakness, opportunity and threat (SWOT) analysis with the Delphi metho

A case study in multiple criteria decision support systems

Evaluation of strategic alternatives is an important task for strategic managers. This is a difficult task due to inherent complexities of the evaluation process and lack of structured information. The evaluation process must consider external opportunities and threats, and internal strengths and weaknesses. This paper presents a case study in multiple criteria decision support systems. The decision support system presented in this paper utilizes the model presented in the appendix along with several computer systems including EXPERT CHOICE and Spreadsheets to enhance and aid the decision maker\u27s- intuition in evaluating potential alternatives

A clustering algorithm to identify information subsystems

This paper presents an algorithm to cluster the entities and relationships identified by database designers into a set of internally cohesive subsystems. Our algorithm is based on the calculation of a distance score that is inversely related to the similarity of interactions of a pair of entities with the relationships in a binary entity-relationship matrix. Our algorithm avoids manual manipulation of rows and columns required by some of the available approaches (Feldman et al, 1986; Teorey, et al, 1989). It has been implemented on a PC, and does not require a super computer as the Wei and Gaither (1990) method does. Using a part-machine clustering problem presented by King (1980), we also show that our algorithm is superior to King\u27s rank order cluster algorithm which requires manual intervention to suppress exceptional entries before one can arrive at the final solution. Directions for further research are identified

Interview of John J. Seydow, Ph.D.

John J. Seydow was born and raised in Olney section of Philadelphia. He was educated in Philadelphia’s Parochial School System from kindergarten through high school. He graduated from Cardinal Dougherty High School in June of 1959. He attended La Salle College on a full time basis from September 1961 through May 1965. He majored in English at La Salle and received his Bachelors degree in May of 1965. The following September he began a graduate fellowship at Ohio University where he earned his Masters and Doctorial degrees in English by May of 1968. In August 1968, he returned to La Salle College as a professor in the English Department. He has taught at La Salle for the last forty-one years and is currently a Professor of English

Campus News September 14, 1990

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Campus News May 23, 2008

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Campus News March 17, 1995

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Campus News June 26, 1998

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Campus News February 12, 1999

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