Decision models and knowledge rules are widely used to assist in decision-making. They are common decision support devices that should be effectively managed in decision support systems. Research and development (R&D) project selection is a complicated and knowledge intensive decision-making process where decision models and knowledge rules play an important role. This paper presents a hybrid knowledge and model system, which integrates mathematical models with knowledge rules, for R&D project selection. The system is designed to support the whole decision process of R&D project selection and has been used in the selection of R&D projects in the National Natural Science Foundation of China (NSFC).
Selection of research and development (R&D) project is an important and recurring activity in many organizations. It is also a challenge task that takes place in a complicated and knowledge intensive decision-making process (Ghasemzadeh & Archer, 2000). In the past four decades, a variety of decision models have been developed to support the R&D project selection (Martino, 1995). According to a recent literature review (Henriksen & Traynor, 1999), the current decision models and methods fall into the following categories: (1) Mathematical Programming and Optimization, including integer programming (IP), linear programming (LP), non-linear programming (NLP), goal programming (GP), and dynamic programming (DLP), portfolio optimization. (2) Decision Analysis, including multi-attribute utility theory (MAUT), decision trees, risk analysis, the analytic hierarchy process (AHP), and scoring; (3) Economic Models, such as internal rate return (IRR), net present value (NPV), return on investment (ROI), cost-benefit analysis, and option pricing theory; (4) Interactive Method, such as Delphi, Q-Sort, behavioral decision aids (BDA), and decentralized hierarchical modeling (DHM). But due to the complexity of these models, most of them can hardly be used by managers in real situation ( Liberatore and Stylianou, 1995 and Schmidt and Freeland, 1992).
This paper reports the design and implementation of a hybrid knowledge and model system for R&D project selection. Decision models and knowledge rules are integrated and used to support the whole decision support process. The system has been incorporated into the implementation and application of ISIS in NSFC. Application results show that the system greatly simplifies the management processes and improves decision efficiency for R&D project selection. Our current work can be extended to include more decision models and techniques like data mining.
