Collaborative e-Work and Collaborative Control Theory for Disruption Handling and Control

  • Hao ZhongEmail author
  • Shimon Y. Nof
Part of the Automation, Collaboration, & E-Services book series (ACES, volume 6)


The important role and motivation of collaborative handling of disruptions are the focus of this chapter. First, a number of collaborative disruptions handling examples are explored for their unique features and advantages: In disasters; emergencies; production, supply, and service networks; and computer and communications security. An overview of CCT, the Collaborative Control Theory is provided, to introduce the design principles and automation of effective, collaborative handling systems and methods. The phases of CLM, Collaborative Lifecycle Management, are then explained as the steps needed in the design of collaborative handling and control.


  1. 1.
    Becker A, Ng AKY, McEvoy D, Mullett J (2018) Implications of climate change for shipping: ports and supply chains. Wiley Interdiscip Rev Climate Change 9(2) (2018)Google Scholar
  2. 2.
    Behal S, Krishan K, Sachdeva M (2017) Characterizing DDoS attacks and flash events: review, research gaps and future directions. Comput Sci Rev 25:101–114CrossRefGoogle Scholar
  3. 3.
    Berman S, Nof SY (2011) Collaborative control theory for robotic systems with reconfigurable end-effectors. In: Proceedings of 21st international conference on production research, Stuttgart, GermanyGoogle Scholar
  4. 4.
    Castro AJM, Rocha AP (2017) Managing disruptions with a multi-agent system for airline operations control. In: Proceedings of advances in practical applications of cyber-physical multi-agent systems conference, Porto, Portugal, pp 307–310Google Scholar
  5. 5.
    Ceroni JA, Nof SY (2002) A workflow model based on parallelism for distributed organizations. J Intell Manuf 13(6):439–461CrossRefGoogle Scholar
  6. 6.
    Chen XW, Nof SY (2010) A decentralized conflict and error detection and prediction model. Int J Prod Res 48(16):4829–4843CrossRefGoogle Scholar
  7. 7.
    Chen XW, Nof SY (2012) Conflict and error prevention and detection in complex networks. Automatica 48(5):770–778MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chituc CM, Nof SY (2007) The join/leave/remain (JLR) decision in collaborative networked organizations. Comput Ind Eng 53(1):173–195CrossRefGoogle Scholar
  9. 9.
    Gonul KC, Nowicki DR, Sauser B, Randall WS (2018) Impact of cloud-based information sharing on hospital supply chain performance: a system dynamics framework. Int J Prod Econ 195:168–185CrossRefGoogle Scholar
  10. 10.
    Huang CY, Nof SY (2002) Evaluation of agent-based manufacturing systems based on a parallel simulator. Comput Ind Eng 43(3):529–552CrossRefGoogle Scholar
  11. 11.
    Jafer S (2014) Collaborative modeling, simulation, and visualization framework for airport emergency. In: Proceedings of the 4th international defense and homeland security simulation workshop (DHSS 2014), pp 13–20Google Scholar
  12. 12.
    Jedari B, Xia F, Ning Z (2018) A survey on human-centric communications in non-cooperative wireless relay networks. IEEE Commun Surveys Tutorials 20(2)CrossRefGoogle Scholar
  13. 13.
    Jeong W, Nof SY (2008) Performance evaluation of wireless sensor network protocols for industrial applications. J Intell Manuf 19(3):335–345CrossRefGoogle Scholar
  14. 14.
    Khan FA, Imran M, Abbas H, Durad MH (2017) A detection and prevention system against collaborative attacks in mobile ad hoc networks. Future Gener Comp Sy 68:416–427CrossRefGoogle Scholar
  15. 15.
    Liu Y, Nof SY (2008) Fault-tolerant sensor integration for micro flow-sensor arrays and networks. Comput Ind Eng 54(3):634–647CrossRefGoogle Scholar
  16. 16.
    Liu Z, Suzuki T (2018) Using agent simulations to evaluate the effect of a regional BCP on disaster response. J Disaster Res 13(2):387–395CrossRefGoogle Scholar
  17. 17.
    Lu L, Wang X, Ouyang Y, Roningen J, Myers N, Calfas G (2018) Vulnerability of interdependent urban infrastructure networks: equilibrium after failure propagation and cascading impacts. Comput Aided Civil Infrastructure Eng 33(4):300–315CrossRefGoogle Scholar
  18. 18.
    Mattsson L-G, Jenelius E (2015) Vulnerability and resilience of transport systems—a discussion of recent research. Transp Res Part A Policy Practice 81:16–34CrossRefGoogle Scholar
  19. 19.
    Monostori L, Valckenaers P, Dolgui A, Panetto H, Brdys M, Csáji BC (2015) Cooperative control in production and logistics. Ann Rev Control 39:12–29CrossRefGoogle Scholar
  20. 20.
    Moran T, Molnar JJ, Desourdis RI, Kurgan WM, Cloutier J-F (2015) Speeding power restoration. In: IEEE international symposium on technologies for homeland security, HSTGoogle Scholar
  21. 21.
    Nof SY (2007) Collaborative control theory for e-work, e-production, and e-service. Ann Rev Control 21(2):281–292CrossRefGoogle Scholar
  22. 22.
    Nof SY, Ceroni J, Jeong W, Moghaddam M (2015) Design with collaborative control theory. In: Revolutionizing collaboration through e-work, e-business, and e-service. Springer, Berlin, pp 33–75CrossRefGoogle Scholar
  23. 23.
    Pérez ATE, Camargo M, Rincón PCN, Marchant MA (2017) Key challenges and requirements for sustainable and industrialized biorefinery supply chain design and management: a bibliographic analysis. Renew Sust Energ Rev 69:350–359Google Scholar
  24. 24.
    Radakov DV (1973) Schooling in the ecology of fish. Wiley and Israel Program for Scientific Translations, JerusalemGoogle Scholar
  25. 25.
    Rajan VN, Nof SY (1996) Minimal precedence constraints for integrated assembly and execution planning. IEEE Trans Robot Autom 12(2):175–186CrossRefGoogle Scholar
  26. 26.
    Rajan VN, Nof SY (1996) Cooperation requirement planning for multiprocessors: optimal assignment and execution planning. J Intell Rob Syst 15:419–435CrossRefGoogle Scholar
  27. 27.
    Rege A (2016) Incorporating the human element in anticipatory and dynamic cyber defense. In: IEEE international conference on cybercrime and computer forensic (ICCCF 2016), 9 Nov 2016Google Scholar
  28. 28.
    Reimann F, Kosmol T, Kaufmann L (2017) Responses to supplier-induced disruptions: a fuzzy-set analysis. J Supply Chain Manage 53(4):37–66CrossRefGoogle Scholar
  29. 29.
    Reyes Levalle R, Nof SY (2015) Resilience by teaming in supply network formation and re-configuration. Int J Prod Econ 160:80–93CrossRefGoogle Scholar
  30. 30.
    Seok H, Kim K, Nof SY (2016) Intelligent contingent multi-sourcing model for resilient supply networks. Expert Syst Appl 51:107–119CrossRefGoogle Scholar
  31. 31.
    Stewart GL, Barrick MR (2000) Team structure and performance: assessing the mediating role of intrateam process and the moderating role of task type. Acad Manage J 43(2):135–148Google Scholar
  32. 32.
    Stone P, Veloso M (1999) Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork. Artif Intell 110(2):241–273CrossRefGoogle Scholar
  33. 33.
    Van de Walle B, Comes T (2014) Risk accelerators in disasters: insights from the Typhoon Haiyan response on humanitarian information management and decision support. In: Proceedings of advanced information systems engineering, 26th International Conference (CAiSE 2014), pp 12–23Google Scholar
  34. 34.
    Velasquez J, Yoon S, Partridge B, Nof SY (2005). Organizational knowledge learning and decision support for emergency and security challenges. In: 18th international conference on production research, Salerno, Italy, Aug 2005Google Scholar
  35. 35.
    Velasquez JD, Yoon SW, Nof SY (2010) Computer-based collaborative training for transportation security and emergency response. Comput Ind 61(4):380–389CrossRefGoogle Scholar
  36. 36.
    Yang TJ, Fan W (2016) Information management strategies and supply chain performance under demand disruptions. Int J Prod Res 54(1):8–27CrossRefGoogle Scholar
  37. 37.
    Zhang S, Wong TN (2017) Flexible job-shop scheduling/rescheduling in dynamic environment: a hybrid MAS/ACO approach. Int J Prod Res 55(11):3173–3196CrossRefGoogle Scholar
  38. 38.
    Zhong H, Nof SY (2015) The dynamic lines of collaboration model: collaborative disruption response in cyber–physical systems. Comput Ind Eng 87:370–382CrossRefGoogle Scholar
  39. 39.
    Zhong H, Nof SY, Filip FG (2014) Dynamic lines of collaboration in CPS disruption response. In: Proceedings of the 19th IFAC World Congress, Cape Town, South AfricaGoogle Scholar
  40. 40.
    Zhong H, Wachs JP, Nof SY (2014) Telerobot-enabled HUB-CI model for collaborative lifecycle management of design and prototyping. Comput Ind 65(4):550–562CrossRefGoogle Scholar
  41. 41.
    Zhong H, Nof SY, Berman S (2015) Asynchronous cooperation requirement planning with reconfigurable end-effectors. Robot Comput Integr Manuf 34(8):95–104CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Facebook Inc.Menlo ParkUSA
  2. 2.PRISM CenterPurdue UniversityWest LafayetteUSA

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