Abstract
Industry-scale projects often require large multi-functional teams to deliver complex inter-related tasks. However, to put together an organization structure to meet a project’s targets most effectively is challenging. In this paper, we introduce a novel agent-based simulation system that models team member behaviour by combining socio-technical attributes to estimate time and cost outputs. It defines team-level performance as a function of individual attributes including new skill learning by exchanging help requests. It provides a framework for simulating arbitrary team configurations against given project task workflow and hence, evaluating the performances of different organization structures. Different modeling granularity levels (tasks, individuals, and teams), together with the flexibility with which the system allows rapid modeling and evaluation of organizational scenarios represent a step change from existing organizational and project analysis tools. It provides the foundation for resource managers to design optimized project teams in future.
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References
Cherns, A.: Principles of Sociotechnical Design Revisited. Human Relations 40(3), 153–161 (1987)
Brocco, M., Groh, G.: Team Recommendation in Open Innovation Networks. In: 3rd ACM Conference on Recommender Systems, New York City, pp. 365–368 (2009)
Jennings, N.R.: An Agent-Based Approach for Building Complex Software Systems. Communications of the ACM 44(4), 35–41 (2001)
Bonabeau, E.: Agent-based Modeling: Methods and Techniques for Simulating Human Systems. National Academy of Sciences of the United States of America 99(3), 7280–7287 (2002)
Chen, X., Zhan, F.: Agent-based Modeling and Simulation of Urban Evacuation: Relative Effectiveness of Simultaneous and Staged Evacuation Strategies. Operational Research Society 59(1), 25–33 (2008)
Quillinan, T.B., Brazier, F., Aldewereld, H., Dignum, F., Dignum, V., Penserini, L., Wijngaards, N.: Developing Agent-based Organizational Models for Crisis Management. In: 8th International Conference on Autonomous Agents and Multiagent Systems, Budapest, pp. 45–52 (2009)
Crowder, R., Hughes, Sim, Y.W., Robinson, M.: An Agent Based Approach to Modelling Design Teams. In: 17th International Conference on Engineering Design, Stanford, pp. 91–102 (2009)
Jin, Y., Levitt, R.E.: The Virtual Design Team: A Computational Model of Project Organizations. Computational and Mathematical Organization Theory 2(3), 171–195 (1996)
Levitt, R.E., Thomsen, J., Christiansen, T.R., Kunz, J.C., Jin, Y., Nass, C.: Simulating Project Work Processes and Organizations: Toward a Micro-Contingency Theory of Organizational Design. Management Science 45(11), 1479–1495 (1999)
Louie, M.A., Carley, K.M., Haghshenass, L., Kunz, J.C., Levitt, R.E.: Model Comparisons: Docking OrgAhead and SimVision. In: North American Association for Computational Social and Organization Sciences, Pittsburgh (2003)
Emery, F.E., Trist, E.L.: Socio-technical Systems. Management Science, Models and Techniques 2, 83–97 (1960)
Balci, O.: A Methodology for Certification of Modeling and Simulation Applications. ACM Transactions on Modeling and Computer Simulation 11(4), 352–377 (2001)
Galan, J.M., Izquierdo, L.R., Izquierdo, S.S., Santos, J.I., Olmo, R.D., Paredes, A.L., Edmonds, B.: Errors and Artefacts in Agent-Based Modelling. Artificial Societies and Social Simulation 12(1), 1 (2009)
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Dutta, P., Pepe, C., Xi, H. (2015). Analyzing Organization Structures and Performance through Agent-Based Socio-technical Modeling. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_4
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DOI: https://doi.org/10.1007/978-3-319-13356-0_4
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