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Decision Making

By: Contributor(s): Material type: TextTextPublication details: IntechOpen 2018Description: 1 electronic resource (64 p.)ISBN:
  • 9781789237962
Subject(s): Online resources: Summary: Decision Making is a book where each chapter has been contributed to by a different author(s). The book synthesizes the analytical principles with business practice of Decision Making. Specifically, the book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of decision making. It is complementary to other sub-disciplines such as economics, finance, marketing, decision and risk analysis, etc. The chapters introduce and demonstrate decision making theory in practical case studies. It demonstrates key results for each sector with diverse real-world case studies. The theory is accompanied by relevant analysis techniques, with a progressional approach building from simple theory to complex and dynamic decisions with multiple data points, including big data, etc. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements.
List(s) this item appears in: E-Books from Directory of Open Access Books
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Decision Making is a book where each chapter has been contributed to by a different author(s). The book synthesizes the analytical principles with business practice of Decision Making. Specifically, the book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of decision making. It is complementary to other sub-disciplines such as economics, finance, marketing, decision and risk analysis, etc. The chapters introduce and demonstrate decision making theory in practical case studies. It demonstrates key results for each sector with diverse real-world case studies. The theory is accompanied by relevant analysis techniques, with a progressional approach building from simple theory to complex and dynamic decisions with multiple data points, including big data, etc. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements.

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