Integrated modelling of agile enterprise networks

Agile enterprise networks, also called as Collaborative Networks (CN) emerge in a decentralised and dynamical way instead of former static hierarchical cooperation and value chains. The CN modelling and optimisation issues differ from those in traditional supply chains and require combined application of various modelling techniques. The paper aims to elaborate an approach Decentralised Integrated Modelling Approach (DIMA) of the CN integrated modelling with the use of elements drawn from different approaches such as control theory, systems theory, operations research and distributed artificial intelligence. The basics of the multi-disciplinary treatment of the CN are developed to contribute to the establishing foundations of the agile networks theory as called for by increasing number of researchers. Our research elaborates a special multi-disciplinary technique for CN modelling to provide flexible application of various modelling frameworks (analytical, simulation and heuristics) as well as their combinations (multiple-model complexes). The results show that the multi-disciplinary treatment of agile production networks allows a comprehensive and realistic planning and control problems formulation and solution. These techniques should allow solving various CN management problems with available data, interlinking with the other relevant problems, etc. as well avoiding the isolated problems solution and incoherent and non-consistent model fragments.

[1]  Boris V. Sokolov,et al.  Stability Analysis in the Framework of Decision Making Under Risk and Uncertainty , 2006, PRO-VE.

[2]  Toshiya Kaihara,et al.  Enterprise Negotiation Algorithm with Walrasian Virtual Market , 2004, Virtual Enterprises and Collaborative Networks.

[3]  S. Graves,et al.  Supply chain management : design, coordination and operation , 2003 .

[4]  Hamideh Afsarmanesh,et al.  Collaborative Networks and Their Breeding Environments - IFIP TC5 WG 5.5 Sixth IFIP Working Conference on Virtual Enterprises, PRE-VE 2005, 26-28 September, 2005, Valencia, Spain , 2005, PRO-VE.

[5]  Layek Abdel-Malek,et al.  An analytical approach for evaluating and selecting vendors with interdependent performance in a supply chain , 2004 .

[6]  J. Hallikas,et al.  Risk management processes in supplier networks , 2004 .

[7]  Hermann Kühnle,et al.  A system of models contribution to production network (PN) theory , 2007, J. Intell. Manuf..

[8]  Boris V. Sokolov,et al.  Intelligent planning and control of manufacturing supply chains in virtual enterprises , 2007, Int. J. Manuf. Technol. Manag..

[9]  Weiming Shen,et al.  Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing , 2000 .

[10]  rer. pol. Tobias Teich Extended Value Chain Management (EVCM) als Betreibermodell hierarchieloser Produktionsnetzwerke , 2001 .

[11]  Boris V. Sokolov,et al.  Quantitative Models of Collaborative Networks , 2005, PRO-VE.

[12]  Hamideh Afsarmanesh,et al.  The Emerging Discipline of Collaborative Networks , 2004, Virtual Enterprises and Collaborative Networks.

[13]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[14]  Michael J. Magazine,et al.  Quantitative Models for Supply Chain Management , 1998 .

[15]  Boris V. Sokolov,et al.  Intelligent Supply Chain Planning in Virtual Enterprises , 2004, Virtual Enterprises and Collaborative Networks.

[16]  Ricardo J. Rabelo,et al.  A Multi-Agent System for Smart Coordination of Dynamic Supply Chains , 2002, PRO-VE.

[17]  John D. Sterman,et al.  Business dynamics : systems thinking and modelling for acomplex world , 2002 .

[18]  Dmitry Ivanov,et al.  DIMA—A research methodology for comprehensive multi-disciplinary modeling of production and logistics networks , 2009 .

[19]  Jayashankar M. Swaminathan,et al.  Modeling Supply Chain Dynamics: A Multiagent Approach , 1998 .