Decision Making in the Manufacturing Environment: Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods
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Tag: Science/Engineering
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R. Venkata Rao "Decision Making in the Manufacturing Environment:
Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods"
Springer | 2007-06-15 | ISBN:1846288185 | PDF | 373 pages | 2 Mb
Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods"
Springer | 2007-06-15 | ISBN:1846288185 | PDF | 373 pages | 2 Mb
Manufacturing is the backbone of any industrialized nation. Recent worldwide advances in manufacturing technologies have brought about a metamorphism in the industry. Fast-changing technologies on the product front have created a need for an equally fast response from manufacturing industries. To meet these challenges, manufacturing industries have to select appropriate manufacturing strategies, product designs, manufacturing processes, work piece and tool materials, and machinery and equipment. The selection decisions are complex as decision making is more challenging today. Decision makers in the manufacturing sector frequently face the problem of assessing a wide range of options and selecting one based on a set of conflicting criteria.
Decision Making in the Manufacturing Environment demonstrates how graph theory and matrix approach, and fuzzy multiple attribute decision making methods can be effectively used for decision making in various situations of the manufacturing environment. Divided into two parts; Part I introduces the decision making situations in the manufacturing environment and presents decision making methods; Part II uses case studies to present the applications of these methods in real manufacturing situations.
Decision Making in the Manufacturing Environment will be very useful to decision makers in the manufacturing sector as it makes decision making easier, more logical, systematic, efficient and effective. It is intended for designers, manufacturing engineers, practitioners, managers, institutes involved in design and manufacturing related projects, applied research workers, academics, and graduate students in mechanical, industrial, and manufacturing engineering.
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