Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-18T00:26:34.416Z Has data issue: false hasContentIssue false
  • English
  • Français

Distributed coordination of project schedule changes using agent-based compensatory negotiation methodology

Published online by Cambridge University Press:  07 November 2003

KEESOO KIM ,
BOYD C. PAULSON ,
RAYMOND E. LEVITT ,
MARTIN A. FISCHER  and
CHARLES J. PETRIE
KEESOO KIM
Affiliation:
Daewoo Institute of Construction Technology, Daewoo E&C Co., Ltd., Kyonggi, Korea
BOYD C. PAULSON
Affiliation:
Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, USA
RAYMOND E. LEVITT
Affiliation:
Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, USA
MARTIN A. FISCHER
Affiliation:
Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, USA
CHARLES J. PETRIE
Affiliation:
Stanford Networking Research Center, Stanford University, Stanford, California 94305, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

In the construction industry, projects are becoming increasingly large and complex, necessitating multiple subcontractors. Traditional centralized coordination techniques used by general contractors become insufficient as subcontractors perform most work and provide their own resources. When subcontractors cannot provide enough resources, they hinder their own performance, that of other subcontractors, and ultimately the entire project. Thus, projects need a new distributed coordination approach wherein all of the concerned subcontractors can respond to changes and reschedule a project dynamically. This paper presents a new distributed coordination framework for project schedule changes (DCPSC) that is based on an agent-based negotiation approach wherein software agents evaluate the impact of changes, simulate decisions, and give advice on behalf of the human subcontractors. A case example demonstrates the significance of the DCPSC. It compares two centralized coordination methodologies used in current practice to the DCPSC framework. We demonstrate that our DCPSC framework always finds a solution that is better than or equal to any of two centralized coordination methodologies.

Keywords

Compensatory NegotiationDistributed CoordinationProject SchedulingSoftware Agent
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 2 , May 2003 , pp. 115 - 131
Copyright
2003 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Antill, J.M. & Woodhead, R.W. (1990). Critical Path Methods in Construction Practice. New York: Wiley.
Choo, H.J. & Tommelein, I.D. (2000). Interactive coordination of distributed work plan. Proc. Sixth Construction Congress, pp. 11–20. Orlando, FL: ASCE.
Choo, H.J., Tommelein, I.D., Ballard, G., & Zabelle, T.D. (1999). WorkPlan: Constraint-based database for work package scheduling. Journal of Construction Engineering and Management, ASCE, 125(3), 151–160.Google Scholar
Clough, R.H. & Sears, G.A. (1991). Construction Project Management. New York: Wiley.
Decker, K.S. & Lesser, V. (1992). Generalizing the partial global planning algorithm. International Journal on Intelligent Cooperative Information Systems, 1(2), 319–346.Google Scholar
Durfee, E.H. & Montgomery, T.A. (1991). Coordination as distributed search in a hierarchical behavior space. IEEE Transactions on Systems, Man, and Cybernetics, 21(6), 1363–1378.Google Scholar
El-Rayes, K. & Moselhi, O. (1996). Resource-driven scheduling of repetitive activities on construction projects. Construction Management and Economics, 16, 433–446.Google Scholar
Ephrati, E. & Rosenschein, J.S. (1996). Deriving consensus in multi-agent systems. Artificial Intelligence, 87(1–2), 21–74.Google Scholar
Feldman, A.M. (1980). Welfare Economics and Social Choice Theory, Boston: Martinus Nijhoff.
Finin, T., Fritzson, R., McKay, D., & McEntire, R. (1994). KQML as an agent communication language. Proc. Third International Conf. Information and Knowledge Management, pp. 456–463, New York: ACM.
Fondahl, J.M. (1961). A Non-computer Approach to the Critical Path Method for the Construction Industry. Technical Report No. 9. Stanford, CA: Stanford University.
Fondahl, J.W. (1991). The development of the construction engineer: Past progress and future problems. Journal of Construction Engineering and Management, 117(3), 380–392.Google Scholar
Gomes, C.P., Tate, A., & Thomas, L. (1994). Distributed scheduling framework. Proc. Int. Conf. Tools With Artificial Intelligence, pp. 49–55. Piscataway, NJ: IEEE.
Jeon, H. (2000). Dynamic constraint management in collaborative design. PhD Thesis. Stanford, CA: Stanford University.
Jin, Y. & Levitt, R.E. (1993). i-AGENTS: Modeling organizational problem solving in multi-agent teams. Intelligent Systems in Accounting, Finance and Management, 2, 247–270.Google Scholar
Khedro, T., Genesereth, M.R., & Teicholz, P.M. (1993). Agent-based framework for integrated facility engineering. Engineering with Computers, 9(2), 94–107.Google Scholar
Kim, K. & Paulson, B.C. (2003). An agent-based compensatory negotiation methodology to facilitate distributed coordination of project schedule changes. Journal of Computing in Civil Engineering, 17(1), 10–18.Google Scholar
Koo, C.C. (1987). A distributed model for performance systems. PhD Thesis. Stanford, CA: Stanford University.
Malone, T.W., Fikes, R.E., & Howard, M.T. (1988). Enterprise: A market like task scheduler for distributed computing environment. In The Ecology of Computation, (Huberman, B.A., Ed.), pp. 177–205. Amsterdam: Elsevier Science BV.
Oberlender, G.D. (1993). Project Management for Engineering and Construction. New York: McGraw–Hill.
O'Brien, W. & Fischer, M.A. (2000). Importance of capacity constraints to construction cost and schedule. Journal of Construction Engineering and Management, 126(5), 366–373.Google Scholar
O'Brien, W., Fischer, M.A., & Jucker, J.V. (1995). An economic view of project coordination. Construction Management and Economics, 13(5), 393–400.Google Scholar
Petrie, C. (1996). Agent-based engineering, the Web, and intelligence. IEEE Expert, 11(6), 24–29.CrossRefGoogle Scholar
Petrie, C., Goldmann, S., & Raquet, A. (1998). Agent-based Project Management. Technical Report No. 19981118, Stanford, CA: Stanford University.
Rosenschein, J.S. (1994). Rules of Encounter: Designing Conventions for Automated Negotiation Among Computers. Cambridge, MA: MIT Press.
Rosenschein, J.S. & Zlotkin, G. (1998). Designing conventions for automated negotiation. In Readings in Agents (Huhn, M.N. & Singh, M.P., Eds.). San Francisco, CA: Morgan Kaufmann.
Sandholm, T. (1993). An implementation of the contract net protocol based on marginal cost calculations. Proc. Eleventh National Conf. Artificial Intelligence, pp. 256–262. Menlo Park, CA: AAAI.
Sen, S. & Durfee, E.H. (1996). A contracting model for flexible distributed scheduling. Annals of Operations Research, 65, 195–222.Google Scholar
Shoham, Y. & Tanaka, K. (1997). A dynamic theory of incentives in multi-agent systems. Proc. Fifteenth Int. Joint Conf. Artificial Intelligence, pp. 626–631. Menlo Park, CA: AAAI.
Smith, R.G. (1980). The contract net protocol: High level communication and control in a distributed problem solver. IEEE Transactions on Computers, C-29, 1104–1113.Google Scholar
Sycara, K. (1989). Multi-agent compromise via negotiation. In Distributed Artificial Intelligence (Gasser, L. & Huhns, M., Eds.), Vol. 2, pp. 119–137. San Francisco, CA: Morgan Kaufmann.
Sycara, K., Roth, S., Sadeh, N., & Fox, M. (1991). Distributed constrained heuristic search. IEEE Transactions on Systems, Man, and Cybernetics, 21(6), 1446–1461.Google Scholar
Tommelein, I.D. & Ballard, G. (1997). Coordinating specialists. Technical Report, No. 97-8. Berkeley, CA: University of California.
Varian, H.R. (1978). Microeconomic Analysis. New York: W.W. Norton.
Wellman, M.P. (1993). A market-oriented programming environment and its application to distributed multicommodity flow problems. Journal of Artificial Intelligence Research, 1, 1–23.Google Scholar
Yokoo, M., Durfee, E.H., Ishida, T., & Kuwabara, K. (1992). Distributed constraint satisfaction for formalizing distributed problem solving. Proc. Twelfth IEEE Int. Conf. Distributed Computing Systems, pp. 614–621. Piscataway, NJ: IEEE.
Yokoo, M., Durfee, E.H., Ishida, T., & Kuwabara, K. (1998). Distributed constraint satisfaction problem: Formalization and algorithms. IEEE Transactions on Knowledge and Data Engineering, 10(5), 673–685.Google Scholar