JDR Vol.9 No.3 pp. 381-399
doi: 10.20965/jdr.2014.p0381


Data Model of the Strategic Action Planning and Scheduling Problem in a Disaster Response Team

Reza Nourjou*1, Pedro Szekely*2, Michinori Hatayama*1,
Mohsen Ghafory-Ashtiany*3, and Stephen F. Smith*4

*1Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan

*2Information Sciences Institute, University of Southern California, USA

*3International Institute of Earthquake Engineering and Seismology, Iran

*4The Robotics Institute, Carnegie Mellon University, USA

June 27, 2013
February 14, 2014
June 1, 2014
data model, problem formulation, strategic and macro, action planning and scheduling, coordination, incident commander, disaster emergency response
Problem: Strategic action planning and scheduling (SAP) in the coordination of a disaster response team involves selecting and decomposing an objective into sub-goals, grouping available units into coalitions and assigning them to the sub-goals, allocating units to tasks, and adjusting the decisions that have been made. The primary responsibility of a team’s incident commander (IC) in SAP is to coordinate the actions of operational units in disaster crisis/emergency response management by making macro/strategic decisions. Objective: In this paper, we completely model a real-world problem and present data related to the SAP problem. This data model is used to support the design and development of an appropriate approach to SAP. Method: The employed methodology is to analyze and study the SAP problem, which is composed of six essential dimensions: the problem domain, geographic information, geospatial-temporal macro tasks, strategic action planning, strategic action scheduling, and team structure. Result: The contribution of this paper is the SAP problem data model. It is designed as a unified modeling language (UML) class diagram consisting of entity types, attributes, and relationships associated with SAP problem data modeling. Conclusion: To evaluate the quality of SAP data modeling, the SAP problem data model is used to propose and develop an intelligent assistant software system to assist and collaborate with incident commanders in SAP. The study makes five novel contributions: 1) a complete data model for SAP problem modeling, 2) a presentation and aggregation of task information in geographic objects, 3) the expression and encoding of human intuition as human high-level strategy guidance for SAP, 4) the formulation of a strategic action plan, and 5) the integration of strategic action schedule information with other entities.
Cite this article as:
R. Nourjou, P. Szekely, M. Hatayama, M. Ghafory-Ashtiany, and S. Smith, “Data Model of the Strategic Action Planning and Scheduling Problem in a Disaster Response Team,” J. Disaster Res., Vol.9 No.3, pp. 381-399, 2014.
Data files:
  1. [1] F. Fiedrich, F. Gehbauer, and U. Rickers, “Optimized resource allocation for emergency response after earthquake disasters,” Safety Science, Vol.35, No.1, pp. 41-57, 2000.
  2. [2] R. Chen, R. Sharman, H. R. Rao, and S. J. Upadhyaya, “Coordination in emergency response management,” Communications of the ACM, Vol.51, No.5, pp. 66-73, 2008.
  3. [3] R. Chen, R. Sharman, H. Raghav Rao, and Shambhu Upadhyaya, “Design principles of coordinated multi-incident emergency response systems,” In Intelligence and Security Informatics, pp. 81-98, Springer Berlin Heidelberg, 2005.
  4. [4] T. W. Malone and K. Crowston, “The interdisciplinary study of coordination,” ACM Computing Surveys (CSUR), Vol.26, No.1, pp. 87-119, 1994.
  5. [5] S. Jain and C. McLean, “Simulation for emergency response: a framework for modeling and simulation for emergency response,” In Proceedings of the 35th conference onWinter simulation: driving innovation, pp. 1068-1076. Winter Simulation Conference, 2003.
  6. [6] K. M. Khalil, M. Abdel-Aziz, T. T. Nazmy, and A.-B. M. Salem, “Multi-agent crisis response systems-design requirements and analysis of current systems,” arXiv preprint arXiv:0903.2543, 2009.
  7. [7] H. S. Nwana, L. C. Lee, and N. R. Jennings, “Coordination in software agent systems,” British Telecom Technical Journal, Vol.14, No.4, pp. 79-88, 1996.
  8. [8] J. D. Hunger and T. L. Wheelen, “Essentials of strategic management,” New Jersey: Prentice Hall, 2003.
  9. [9] D. A. Buck, J. E. Trainor, and B. E. Aguirre, “A critical evaluation of the incident command system and NIMS,” Journal of Homeland Security and Emergency Management, Vol.3, No.3, 2006.
  10. [10] G. A. Bigley and K. H. Roberts, “The incident command system: High-reliability organizing for complex and volatile task environments,” Academy of Management Journal, Vol.44, No.6, pp. 1281-1299, 2001.
  11. [11] FEMA, “Fema Incident Action Planning Guide,” [accessed April, 2013]
  12. [12] A. R. Vafaeinezhad, A. A. Alesheikh, M. Hamrah, R. Nourjou, and R. Shad, “Using GIS to Develop an Efficient Spatio-temporal Task Allocation Algorithm to Human Groups in an Entirely Dynamic Environment Case Study: Earthquake Rescue Teams,” In Computational Science and Its Applications ICCSA 2009, pp. 66-78. Springer Berlin Heidelberg, 2009.
  13. [13] R. Nourjou, M. Hatayama, and H. Tatano, “Introduction to spatially distributed intelligent assistant agents for coordination of humanagent teams’ actions,” In Safety, Security, and Rescue Robotics (SSRR), 2011 IEEE International Symposium on, pp. 251-258, IEEE, 2011.
  14. [14] S. Fuhrmann, A. MacEachren, and G. Cai, “Geoinformation technologies to support collaborative emergency management,” In Digital Government, pp. 395-420. Springer US, 2008.
  15. [15] H. Kitano and S. Tadokoro, “Robocup rescue: A grand challenge for multiagent and intelligent systems,” AI Magazine, Vol.22, No.1, p. 39, 2001.
  16. [16] Decker, Keith S., and Victor R. Lesser, “Quantitative modeling of complex environments,” International Journal of Intelligent Systems in Accounting, Finance, and Management, Vol.2, No.4, pp. 215-234, 1993.
  17. [17] M. Boddy, B. Horling, J. Phelps, R. P. Goldman, R. Vincent, A. C. Long, B. Kohout, and R. Maheswaran, “C TAEMS Language Specification, Version 2.02,” DARPA, Arlington, VA, 2006.
  18. [18] N. Schurr, J. Marecki, J. P. Lewis, M. Tambe, and P. Scerri, “The defacto system: Training tool for incident commanders,” In AAAI, pp. 1555-1562. 2005.
  19. [19] R. T. Maheswaran, P. Szekely, and R. Sanchez, “Automated adaptation of strategic guidance in multiagent coordination,” In Agents in Principle, Agents in Practice, pp. 247-262. Springer Berlin Heidelberg, 2011.
  20. [20] N. Jennings, S. D. Ramchurn, M. Allen-Williams, R. Dash, P. Dutta, A. Rogers, and I. Vetsikas, “The ALADDIN project: Agent technology to the rescue,” In Proceedings of the First Intl. Workshop on Agent Technology for Disaster Management. 2006.
  21. [21] R. T. Maheswaran, C. M. Rogers, R. Sanchez, and P. Szekely, “Human-agent collaborative optimization of real-time distributed dynamic multi-agent coordination,” In Workshop 25: Optimisation in Multi-agent Systems, p. 49. 2010.
  22. [22] M. H. Burstein and D, V.McDermott, “Issues in the development of human-computer mixed-initiative planning,” Advances in Psychology, Vol.113, pp. 285-303, 1996.
  23. [23] R. Johnson, “GIS technology for disasters and emergency management,” An ESRI White Paper, 2000.
  24. [24] D. L. Moody, “Measuring the quality of data models: an empirical evaluation of the use of quality metrics in practice,” In ECIS, pp. 1337-1352. 2003.
  25. [25] S. F. Smith, A. Gallagher, T. Zimmerman, L. Barbulescu, and Z. Rubinstein, “Distributed Management of Flexible Times Schedules,” Proceedings 6th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 07), Honolulu Hawaii, May 2007.
  26. [26] R. T. Maheswaran, P. Szekely, M. Becker, S. Fitzpatrick, G. Gati, J. Jin, R. Neches et al., “Predictability & criticality metrics for coordination in complex environments,” In Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, Vol.2, pp. 647-654. International Foundation for Autonomous Agents and Multiagent Systems, 2008.
  27. [27] OCHA, INSARAG Guidelines and Methodology Manual, [accessed April, 2013]
  28. [28] M. J. Egenhofer and R. D. Franzosa, “Point-set topological spatial relations,” International Journal of Geographical Information System, Vol.5, No.2, pp. 161-174, 1991.
  29. [29] R. Nourjou, M. Hatayama, S. F. Smith, A. Sadeghi, and P. Szekely, “Design of a GIS-based Assistant Software Agent for the Incident Commander to Coordinate Emergency Response Operations,” arXiv preprint arXiv:1401.0282, 2014.
  30. [30] R. Nourjou, S. F. Smith, M. Hatayama, N. Okada, and P. Szekely, “Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-agent Systems,” International Journal of Machine Learning and Computing (IJMLC), Vol.4, No.1, pp. 39-46, 2014.
  31. [31] Y.-K. Kwok and I. Ahmad, “Benchmarking and comparison of the task graph scheduling algorithms,” Journal of Parallel and Distributed Computing, Vol.59, No.3, pp. 381-422, 1999.
  32. [32] R. Nourjou, S. F. Smith, M. Hatayama, and P. Szekely, “Intelligent Algorithm for Assignment of Agents to Human Strategy in Centralized Multi-agent Coordination,” Journal of Software, 2014.
  33. [33] R. Nourjou and M. Hatayama, “Simulation of an Organization of Spatial Intelligent Agents in the Visual C#.NET Framework.,” International Journal of Computer Theory and Engineering (IJCTE), Vol.6, No.5, pp. 426-431, 2014.
  34. [34] B. Mansouri, K. A. Hosseini, and R. Nourjou, “Seismic human loss estimation in Tehran using GIS,” In 14th World Conference on Earthquake Engineering, Beijing, 2008.
  35. [35] B. Mansouri, M. Ghafory-Ashtiany, K. Amini-Hosseini, R. Nourjou, and M. Mousavi, “Building seismic loss model for Tehran,” Earthquake Spectra, Vol.26, No.1, pp. 153-168, 2010.
  36. [36] I. Nakabayashi, “Disaster Management System forWide-Area Support,” Journal of Disaster Research, Vol.1, pp. 46-71.

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