Details of Grant 

EPSRC Reference:	GR/N65615/01
Title:	A HYBRID METHODOLOGY FOR MULTIPLE CRITERIA DECISION ANALYSIS UNDER VARIOUS TYPES OF UNCERTAINTIES
Principal Investigator:	Yang, Professor J
Other Investigators:	Dale, Professor B Shackley, Dr S
Researcher Co-Investigators:
Project Partners:	Duckworth Liverpool John Moores University MARL International Mersey Care NHS Foundation Trust North West Water Norweb plc Silcoms Ltd
Department:	Manchester School of Management
Organisation:	UMIST
Scheme:	Standard Research (Pre-FEC)
Starts:	31 January 2001	Ends:	30 July 2003	Value (£):	88,911
EPSRC Research Topic Classifications:	Mathematical Aspects of OR
EPSRC Industrial Sector Classifications:	Aerospace, Defence and Marine Manufacturing Electronics Food and Drink Healthcare Energy Water
Related Grants:
Panel History:
Summary on Grant Application Form
In increasingly competitive world markets and with growing public concerns over issues like safety, quality, health and environment, organisations have to make complex decisions in new product development, quality management, environmental management, etc. Regular and objective assessment of companies' products, processes and strategies is essential to success. Real-world decision problems are complex in that one must simultaneously cater for many criteria of both a quantitative and qualitative nature under uncertainties. To conduct such multiple criteria decision analysis (MCDA) with acceptable confidence requires rigorous models, methodologies and domain specific knowledge. Traditional decision models and methodologies could only deal with problems having one type of data.This proposed project aims at conducting theoretical research to develop a new hybrid methodology for MCDA using both quantitative data and qualitative knowledge under probabilistic and fuzzy uncertainties. The methodology will be developed based on decision theory and a range of uncertainty modelling frameworks including Dempster-Shafer theory of evidence, the mass assignment theory and fuzzy set theory. It will comprise generic distributed frameworks for modelling complex decision problems and (fuzzy) evidential reasoning algorithms for criteria aggregation. Three decision models and a window based software package will be developed to provide an easy-to-use tool for decision analysis.
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