The Analytic Hierarchy Process: Advances in Research and Applications (Mathematics Research Developments) - Hardcover

Daniel, Rolando

Hardcover

ISBN 10: 1536133337 ISBN 13: 9781536133332

Publisher: Nova Science Pub Inc, 2018

Synopsis

In this collection, the authors investigate a variety of hazards that drilling operations may be exposed to. Some may be location dependent and some may be activity-dependent and each can pose a different level of risk. The key elements of the framework to assess risk are: a hierarchy of major contributors to risk in two or more levels; a quantification of primary hazard categories with measurable attributes; a graphical representation of risk to aide distinguishing critical hazards; a layered, filtering approach to remove the less important hazards; and a weighted-score method, which is based on the analytic hierarchy process (AHP) approach. Next, the authors seek to develop a multi-criteria decision-making method for the evaluation of railway restructuring models through reviews of practices, alternatives, and criteria of railway restructuring. The development of the tool was based on the combination of the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. A paper is included which aims to present a new way of stakeholder assessment going beyond standard approaches, such as the Gardner Model and the Salience Model of Mitchell, Agle and Wood. Later, an ICT oriented procurement model resulting from a partial re-engineering of the basic procurement process is proposed. This model was obtained by merging the traditional frameworks buygrid and buying centre to provide a consistent choice of procurement alternatives in a multiplicity of Web solutions. More specifically, the book presents evaluation experiments that use AHP, fuzzy AHP or their combination with other methods for e-government evaluation. The analytic hierarchy process method is employed in the closing chapter to reduce single parameter error and improve the accuracy of the prediction model, combined with the sensitivity and correlation of the characteristic parameters. (Nova)

Edition details

• Publisher: Nova Science Pub Inc
• Publication date: 2018
• Language: English
• ISBN 10: 1536133337
• ISBN 13: 9781536133332
• Binding: Hardcover
• Number of pages: 240