Distributed Dynamic Scheduling for Cyber-Physical Production Systems Based on a Multi-Agent System

The world is experiencing a new industrial revolution characterized by intelligent manufacturing. Cyber-physical production systems (CPPSs) have become a research focus due to their proposed use as a solution to the development of flexible and reactive systems. The application of current centralized scheduling methods is difficult because of the enhanced precision control mode of a CPPS. Therefore, this paper focuses on distributed optimal scheduling based on multi-agent systems. First, the goals and constraints of the system are set, a two-layer decision model and the required indicators are designed to ensure the overall optimization effect, and the roles and functions of different agents are then set. Second, the dynamic decision cycle and the multistage negotiation mechanism based on the contract net protocol are studied to ensure the quality of negotiation. A rescheduling algorithm is designed to guarantee adaptability in the case of disturbance in the system. Finally, the applicability and superiority of the strategies are demonstrated via experiments and case studies.

[1]  Andrew Y. C. Nee,et al.  Advanced manufacturing systems: socialization characteristics and trends , 2015, Journal of Intelligent Manufacturing.

[2]  Lihui Wang,et al.  Combined strength of holons, agents and function blocks in cyber-physical systems , 2016 .

[3]  N. Jazdi,et al.  Cyber physical systems in the context of Industry 4.0 , 2014, 2014 IEEE International Conference on Automation, Quality and Testing, Robotics.

[4]  Randall Davis,et al.  Negotiation as a Metaphor for Distributed Problem Solving , 1988, Artif. Intell..

[5]  Lihui Wang,et al.  Current status and advancement of cyber-physical systems in manufacturing , 2015 .

[6]  Qiang Wang,et al.  Intelligent assembly system for mechanical products and key technology based on internet of things , 2014, Journal of Intelligent Manufacturing.

[7]  Fabrício Junqueira,et al.  Control architecture and design method of reconfigurable manufacturing systems , 2016 .

[8]  Paulo Leitão,et al.  Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges , 2016, Comput. Ind..

[9]  Paulo Leitão,et al.  ADACOR: A holonic architecture for agile and adaptive manufacturing control , 2006, Comput. Ind..

[10]  Kathryn E. Stecke,et al.  Design, planning, scheduling, and control problems of flexible manufacturing systems , 1985 .

[11]  Paulo Leitão,et al.  Intelligent products: The grace experience , 2015 .

[12]  Luc Bongaerts,et al.  Reference architecture for holonic manufacturing systems: PROSA , 1998 .

[13]  A. Gunasekaran,et al.  Agile manufacturing: The drivers, concepts and attributes , 1999 .

[14]  Chai Xu-dong,et al.  Cloud manufacturing:a new service-oriented networked manufacturing model , 2010 .

[15]  Alpika Tripathi,et al.  Multi Agent System in Job Shop Scheduling using Contract Net Protocol , 2014 .

[16]  Ray Y. Zhong,et al.  A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing , 2015, Adv. Eng. Informatics.

[17]  Patrick Pujo,et al.  PROSIS: An isoarchic structure for HMS control , 2009, Eng. Appl. Artif. Intell..

[18]  László Monostori,et al.  Cyber-physical production systems: roots from manufacturing science and technology , 2015, Autom..

[19]  Hongbin Huang,et al.  List Scheduling Algorithm for Static Task with Precedence Constraints for Cyber-physical Systems , 2012 .

[20]  László Monostori,et al.  ScienceDirect Variety Management in Manufacturing . Proceedings of the 47 th CIRP Conference on Manufacturing Systems Cyber-physical production systems : Roots , expectations and R & D challenges , 2014 .

[21]  Anne L'Anton,et al.  A modeling framework for manufacturing services in Service-oriented Holonic Manufacturing Systems , 2016, Eng. Appl. Artif. Intell..

[22]  N. Suresh Kumar,et al.  Simulation-based metamodels for the analysis of scheduling decisions in a flexible manufacturing system operating in a tool-sharing environment , 2010 .

[23]  Ali Vatankhah Barenji,et al.  A dynamic multi-agent-based scheduling approach for SMEs , 2017 .

[24]  Qiong Liu,et al.  An Application of Horizontal and Vertical Integration in Cyber-Physical Production Systems , 2015, 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.

[25]  Yan Yan,et al.  Reconfiguration point decision method based on dynamic complexity for reconfigurable manufacturing system (RMS) , 2018, J. Intell. Manuf..

[26]  Pieter J. Mosterman,et al.  Industry 4.0 as a Cyber-Physical System study , 2016, Software & Systems Modeling.

[27]  Rolf Steinhilper,et al.  The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆ , 2017 .