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Probability of a feasible flow in a ...

Fanelli, Santino L.

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Probability of a feasible flow in a stochastic transportation network.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Probability of a feasible flow in a stochastic transportation network./
作者:	Fanelli, Santino L.
面頁冊數:	155 p.
附註:	Source: Masters Abstracts International, Volume: 50-05, page: 3401.
Contained By:	Masters Abstracts International50-05.
標題:	Operations Research. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1508039
ISBN:	9781267255020

Probability of a feasible flow in a stochastic transportation network.
Fanelli, Santino L.

Probability of a feasible flow in a stochastic transportation network. - 155 p.

Source: Masters Abstracts International, Volume: 50-05, page: 3401.

Thesis (M.S.)--Rutgers The State University of New Jersey - New Brunswick, 2012.

Electricity is one of the main sources of energy relied upon throughout the world today to power our homes, businesses, and other needs. The electric utility industry is the driving force behind the provision of electric service, responsible for generating and delivering electric power to end use customers on a reliable basis. In order to meet this responsibility, consideration needs to be given to both the supply of and demand for electricity, along with the available capacity through which to deliver it. A resulting fundamental objective of the electric utility industry, then, is to balance supply with customer demand by maintaining sufficient generating and delivery capacity. The probability by which this objective can be accomplished lends itself to representation as a stochastic transportation network. Determining this probability is the primary problem addressed in this paper.

ISBN: 9781267255020Subjects--Topical Terms:

626629
Operations Research.

Probability of a feasible flow in a stochastic transportation network.
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245 1 0 $a Probability of a feasible flow in a stochastic transportation network.
300 $a 155 p.
500 $a Source: Masters Abstracts International, Volume: 50-05, page: 3401.
500 $a Adviser: Andras Prekopa.
502 $a Thesis (M.S.)--Rutgers The State University of New Jersey - New Brunswick, 2012.
520 $a Electricity is one of the main sources of energy relied upon throughout the world today to power our homes, businesses, and other needs. The electric utility industry is the driving force behind the provision of electric service, responsible for generating and delivering electric power to end use customers on a reliable basis. In order to meet this responsibility, consideration needs to be given to both the supply of and demand for electricity, along with the available capacity through which to deliver it. A resulting fundamental objective of the electric utility industry, then, is to balance supply with customer demand by maintaining sufficient generating and delivery capacity. The probability by which this objective can be accomplished lends itself to representation as a stochastic transportation network. Determining this probability is the primary problem addressed in this paper.
520 $a The general construct of this paper is largely a continuation of On The Probability Of A Feasible Flow In A Stochastic Transportation Network (Prekopa and Boros, 1989). However, it has been updated to incorporate alternative methods to solve the problem and also include practical examples based on actual data from the industry.
520 $a This paper will seek to accomplish the following: (1) Section 1 provides a general introduction and overview of the electric utility industry. This section includes statistics and additional details on all aspects of the electric utility industry, a discussion of upcoming challenges currently facing the industry, and a brief overview of a large electric utility company in the United States. (2) Section 2 more formally addresses the problem of determining the likelihood that the electric utility has sufficient generating and delivery capacity available to satisfy customer demand by formulating it as a stochastic transportation network. The general formulation is based on the results of a well-established theorem and is improved by incorporating a procedure to increase the efficiency by which the problem can be solved. (3) Section 3 introduces several methods that can be used to solve the problem and focuses specifically on three of them that will be incorporated later on in the paper. A description of each applicable method is provided, along with some illustrative examples. (4) Sections 4, 5, and 6 are numerical examples of the problem. All three examples are based on the same general formulation and make use of actual data from the electric utility industry. The underlying assumptions, though, are different in each one, thus providing a range of sensitivity around the results. Each numerical example is solved using all three methods described in Section 3. (5) Section 7 provides a summary of the results from the numerical examples and some overall conclusions.
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650 4 $a Applied Mathematics. $3 1669109
650 4 $a Energy. $3 876794
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710 2 $a Rutgers The State University of New Jersey - New Brunswick. $b Graduate School - New Brunswick. $3 1019196
773 0 $t Masters Abstracts International $g 50-05.
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793 $a English
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