CoMM: a consensus algorithm for multi-agent-based manufacturing system to deal with perturbation

The emergence of Cyber Physical System has dramatically impacted the use of traditionally centralized control system in responding to unexpected events. Rush order is a quite common unexpected event in the current dynamic market characteristics and has significant perturbing ability to a centrally predictive schedule. This paper is aimed to propose a consensus algorithm for multi-agent-based manufacturing system (CoMM) to control the rush order and henceforth minimize a makespan. Consensus is an algorithmic procedure applied in control theory which allows convergence of state between locally autonomous agents collaborating for their common goal. Leader-follower communication approach was used among the multi-agent to deal with the perturbing event. Each agent decides when to broadcast its state to neighbor agents, and the controlling decision depends on the behavior of this state. The consensus algorithm is initially modeled by networking all contributing agents. After this, it is validated with simulation experiment based on academic full-sized ap plication platform called TRACILOGIS platform. The results showed that the consensus algorithm has significantly minimized the impact of rush order on makespan of manufacturing orders launched on a system.

[1]  Dimos V. Dimarogonas,et al.  On the Rendezvous Problem for Multiple Nonholonomic Agents , 2007, IEEE Transactions on Automatic Control.

[2]  Kevin L. Moore,et al.  Decentralized adaptive scheduling using consensus variables , 2007 .

[3]  André Thomas,et al.  Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges , 2017, J. Intell. Manuf..

[4]  Wenwu Yu,et al.  An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination , 2012, IEEE Transactions on Industrial Informatics.

[5]  Damien Trentesaux,et al.  ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling , 2014, Comput. Ind..

[6]  Xiao Fan Wang,et al.  Synchronization of coupled harmonic oscillators in a dynamic proximity network , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[7]  Wei Xiong,et al.  A new immune multi-agent system for the flexible job shop scheduling problem , 2018, J. Intell. Manuf..

[8]  Dongjun Lee,et al.  Stable Flocking of Multiple Inertial Agents on Balanced Graphs , 2007, IEEE Transactions on Automatic Control.

[9]  Paul Valckenaers and Handrik Van Brussel Design for the Unexpected : From Holonic Manufacturing Systems Towards A Humane Mechatronics Society - 978-0-12-803662-4 , 2015 .

[10]  Game-Theoretic Analysis of Cooperation Among Supply Chain Agents: Review and Extensions , 2006 .

[11]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .

[12]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[13]  Wen-Pai Wang,et al.  A neuro-fuzzy based forecasting approach for rush order control applications , 2008, Expert Syst. Appl..

[14]  Petter Ögren,et al.  Cooperative control of mobile sensor networks:Adaptive gradient climbing in a distributed environment , 2004, IEEE Transactions on Automatic Control.

[15]  Sergio Cavalieri,et al.  Multi-agent systems in production planning and control: an overview , 2004 .

[16]  Randal W. Beard,et al.  Distributed Consensus in Multi-vehicle Cooperative Control - Theory and Applications , 2007, Communications and Control Engineering.

[17]  Abdelghani Bekrar,et al.  The control of myopic behavior in semi-heterarchical production systems: A holonic framework , 2013, Eng. Appl. Artif. Intell..

[18]  Paulo Leitão,et al.  Pollux: a dynamic hybrid control architecture for flexible job shop systems , 2017, Int. J. Prod. Res..

[19]  David Sánchez,et al.  Organizational structures supported by agent-oriented methodologies , 2011, J. Syst. Softw..

[20]  Sarit Kraus,et al.  Negotiation and Cooperation in Multi-Agent Environments , 1997, Artif. Intell..

[21]  José Barbosa,et al.  Bio-inspired multi-agent systems for reconfigurable manufacturing systems , 2012, Eng. Appl. Artif. Intell..

[22]  André Thomas,et al.  Another interpretation of stigmergy for product-driven systems architecture , 2012, J. Intell. Manuf..

[23]  Hermann Lödding,et al.  Throughput Time Characteristics of Rush Orders and their Impact on Standard Orders , 2012 .

[24]  Damien Trentesaux,et al.  Reducing myopic behavior in FMS control: A semi-heterarchical simulation-optimization approach , 2014, Simul. Model. Pract. Theory.

[25]  Raul Espejo,et al.  Organizational Systems: Managing Complexity with the Viable System Model , 2011 .

[26]  N. R. Jennings,et al.  To appear in: Int Journal of Group Decision and Negotiation GDN2000 Keynote Paper Automated Negotiation: Prospects, Methods and Challenges , 2022 .

[27]  Jeffrey S. Rosenschein,et al.  Rules of Encounter - Designing Conventions for Automated Negotiation among Computers , 1994 .

[28]  Hui Zhang,et al.  Evaluation of Bus Networks in China: From Topology and Transfer Perspectives , 2015 .

[29]  Cees Witteveen,et al.  Plan coordination by revision in collective agent based systems , 2002, Artif. Intell..

[30]  Jinliang Shao,et al.  Consensus for Discrete-Time Multiagent Systems , 2015 .

[31]  Damien Trentesaux,et al.  Distributed control of production systems , 2009, Eng. Appl. Artif. Intell..

[32]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[33]  Sarvapali D. Ramchurn,et al.  Argumentation-based negotiation , 2003, The Knowledge Engineering Review.

[34]  Paulo Leitão,et al.  Agent-based distributed manufacturing control: A state-of-the-art survey , 2009, Eng. Appl. Artif. Intell..

[35]  Luc Bongaerts,et al.  Holonic manufacturing systems , 1997 .

[36]  Debasish Ghose,et al.  Generalization of Linear Cyclic Pursuit With Application to Rendezvous of Multiple Autonomous Agents , 2006, IEEE Transactions on Automatic Control.

[37]  H. El Haouzi,et al.  Design and validation of a product-driven control system based on a six sigma methodology and discrete event simulation , 2009 .

[38]  Hind El Haouzi,et al.  A Negotiation-based control approach for disturbed industrial context , 2018 .

[39]  R. G. Petrakian,et al.  Trade-offs in cycle time management: hot lots , 1992 .