, Volume 21, Issue 3, pp 327–343 | Cite as

Towards building an intelligent decision support system for project management

  • C Deepak Kumar
  • V V S Sarma
Intelligent systems


Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).


Intelligent decision support system project management semistructured problems 


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  1. Cooper K G 1994 The rework cycle: Vital insights into managing projects.IEEE Eng. Manage. Rev. 21(3): 4–12Google Scholar
  2. Fox M S 1985 Knowledge representation for decision support.Knowledge representation for decision support (ed.) R H Sprague (Amsterdam: North-Holland)Google Scholar
  3. Howard R A, Matheson J E (eds) 1981 Influence diagrams. InThe principles and applications of decision analysis (Menlo Park, CA: Strategic Decisions Group)Google Scholar
  4. Miller A C, Merkhofer M M, Howard R A, Matheson J E, Rice T R 1976 Development of automated aids for decision analysis (Menlo Park, CA: Stanford Res. Inst.)Google Scholar
  5. Nilsson N J 1981 Problem solving methods. InArtificial Intelligence (New York: McGraw-Hill)Google Scholar
  6. Noronha S J 1993Intelligent decision support systems for project planning and scheduling. Ph D thesis, Dept. of Computer Science and Automation, Indian Institute of Science, BangaloreGoogle Scholar
  7. Noronha S J, Sarma V V S 1989 Artificial intelligence and knowledge-based approaches for scheduling problems. InProject Management — Proc. Int. Conf. Expert Systems for Development, Kathmandu, pp 105–114Google Scholar
  8. Object oriented programming: Special issue, Aug. 1989.Comput. J. 32: 4Google Scholar
  9. Shachter R D 1986 Evaluating influence diagrams.Oper. Res. 34: 871–882MathSciNetCrossRefGoogle Scholar
  10. Zadeh L A 1965 Fuzzy sets.Inf. Contr. 8: 338–353MATHCrossRefMathSciNetGoogle Scholar
  11. Zhang W R, Chen S, Bedzek J C 1989 Pool2: A generic system for cognitive map development and decision analysis.IEEE Trans. Syst. Man Cybern. 19: 31–39CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 1996

Authors and Affiliations

  • C Deepak Kumar
    • 1
  • V V S Sarma
    • 1
  1. 1.Department of Computer Science & AutomationIndian Institute of ScienceBangaloreIndia

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