Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling

This paper presents an agent-based fuzzy constraint-directed negotiation (AFCN) mechanism to solve distributed job shop scheduling problems (JSSPs). The scheduling problem is modelled as a set of fuzzy constraint satisfaction problems (FCSPs), interlinked by inter-agent constraints. Each FCSP represents the perspective of the participants and is governed by autonomous agents. The novelty of the proposed AFCN is to bring the concept of a fuzzy membership function to represent the imprecise preferences of task start time for job and resource agents. This added information sharing is crucial for the effectiveness of distributed coordination. It not only can speed up the convergence, but also enforce a global consistency through iterative exchange of offers and counter-offers. The AFCN mechanism can also flexibly adopt different negotiation strategies, such as competitive, win-win, and collaborative strategies, for different production environments. The experimental results demonstrate that the proposed model can provide not only high-quality and cost-effective job shop scheduling (i.e., comparable to that of centralized methods) but also superior performance in terms of the makespan and average flow time compared with other negotiation models for agent-based manufacturing scheduling. As a result, the proposed AFCN mechanism is flexible and useful for distributed manufacturing scheduling with unforeseen disturbances. Display Omitted Agent-based fuzzy constraint-directed negotiation (AFCN) mechanism is proposed.To achieve autonomous cooperation for distributed job shop scheduling.AFCN mechanism is flexible to incorporate different negotiation strategies.AFCN mechanism outperforms both auction-based negotiation and the contract net protocol.

[1]  Juebang Yu,et al.  Fuzzy tabu search for solving the assignment problem , 2002, IEEE 2002 International Conference on Communications, Circuits and Systems and West Sino Expositions.

[2]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.

[3]  Sanjay B. Joshi,et al.  Auction-based distributed scheduling in a dynamic job shop environment , 2002 .

[4]  Lars Nordström,et al.  A multi-agent system for distribution grid congestion management with electric vehicles , 2015, Eng. Appl. Artif. Intell..

[5]  Kwang Mong Sim,et al.  Agent-Based Cloud Computing , 2012, IEEE Transactions on Services Computing.

[6]  Nicholas R. Jennings,et al.  A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments , 2003, Artif. Intell..

[7]  Can Saygin,et al.  Auction-based distributed scheduling and control scheme for flexible manufacturing systems , 2004 .

[8]  Vahit Kaplanoglu,et al.  Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance , 2014, Appl. Soft Comput..

[9]  Salwani Abdullah,et al.  Fuzzy job-shop scheduling problems: A review , 2014, Inf. Sci..

[10]  Fu-Ren Lin,et al.  The enhancement of solving the distributed constraint satisfaction problem for cooperative supply chains using multi-agent systems , 2008, Decis. Support Syst..

[11]  K. Robert Lai,et al.  Modeling Agent Negotiation Via Fuzzy Constraints in E‐Business , 2004, Comput. Intell..

[12]  Weiming Shen,et al.  DPP: An agent-based approach for distributed process planning , 2003, J. Intell. Manuf..

[13]  K. Robert Lai,et al.  Fuzzy constraint-directed negotiation mechanism as a framework for multi-agent scheduling , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[14]  James J. Solberg,et al.  INTEGRATED SHOP FLOOR CONTROL USING AUTONOMOUS AGENTS , 1992 .

[15]  Gordon G. Parker,et al.  Survey of multi-agent systems for microgrid control , 2015, Eng. Appl. Artif. Intell..

[16]  Jae Hyung Cho,et al.  Supply chain formation using agent negotiation , 2010, Decis. Support Syst..

[17]  Jing Huang,et al.  A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels , 2015, Comput. Ind. Eng..

[18]  Bo Chen,et al.  A Review of the Applications of Agent Technology in Traffic and Transportation Systems , 2010, IEEE Transactions on Intelligent Transportation Systems.

[19]  D. H. Norrie,et al.  Bidding-based process planning and scheduling in a multi-agent system , 1997 .

[20]  Chih-Hsing Chu,et al.  Multi-agent negotiation based on price schedules algorithm for distributed collaborative design , 2013, J. Intell. Manuf..

[21]  Abhinav Mittal,et al.  A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach , 2012, Eng. Appl. Artif. Intell..

[22]  Ryszard Kowalczyk,et al.  FeNAs: a fuzzy e-negotiation agents system , 2000, Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520).

[23]  P. Aravindan,et al.  A Tabu Search Algorithm for Job Shop Scheduling , 2000 .

[24]  Didier Dubois,et al.  Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge , 2003, Eur. J. Oper. Res..

[25]  Saeed Mansour,et al.  Dynamic flexible job shop scheduling with alternative process plans: an agent-based approach , 2011 .

[26]  Ilkka Seilonen,et al.  Agent-based modeling and simulation of a smart grid: A case study of communication effects on frequency control , 2014, Eng. Appl. Artif. Intell..

[27]  K. Lai.  Fuzzy constraint processing , 1992 .

[28]  Mohamed A. Khamis,et al.  Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework , 2014, Eng. Appl. Artif. Intell..

[29]  Nicholas R. Jennings,et al.  Using similarity criteria to make issue trade-offs in automated negotiations , 2002, Artif. Intell..

[30]  Michael P. Wellman,et al.  Auction Protocols for Decentralized Scheduling , 2001, Games Econ. Behav..

[31]  Weiming Shen,et al.  Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[32]  Giuseppe Bruno,et al.  Applications of agent-based models for optimization problems: A literature review , 2012, Expert Syst. Appl..

[33]  Didier Dubois,et al.  Fuzzy constraints in job-shop scheduling , 1995, J. Intell. Manuf..

[34]  Kwang Mong Sim,et al.  Complex and Concurrent Negotiations for Multiple Interrelated e-Markets , 2013 .

[35]  Lars Mönch,et al.  A decision support system for cooperative transportation planning: Design, implementation, and performance assessment , 2014, Expert Syst. Appl..

[36]  Kwang Mong Sim,et al.  Agent-based Cloud service composition , 2012, Applied Intelligence.

[37]  Paulo Leitão,et al.  A holonic approach to dynamic manufacturing scheduling , 2008 .

[38]  Soundar R. T. Kumara,et al.  Multiagent based dynamic resource scheduling for distributed multiple projects using a market mechanism , 2003, J. Intell. Manuf..

[39]  T. N. Wong,et al.  A hybrid multi-agent negotiation protocol supporting agent mobility in virtual enterprises , 2014, Inf. Sci..

[40]  George Q. Huang,et al.  Agent-based modeling of supply chains for distributed scheduling , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[41]  W. Xiang,et al.  Ant colony intelligence in multi-agent dynamic manufacturing scheduling , 2008, Eng. Appl. Artif. Intell..

[42]  Rebecca Y. M. Wong,et al.  N* - an agent-based negotiation algorithm for dynamic scheduling andn rescheduling , 2003, Adv. Eng. Informatics.

[43]  Weiming Shen,et al.  Distributed Manufacturing Scheduling Using Intelligent Agents , 2002, IEEE Intell. Syst..

[44]  Chuan-Jun Su,et al.  JADE implemented mobile multi-agent based, distributed information platform for pervasive health care monitoring , 2011, Appl. Soft Comput..

[45]  Pooja Dewan,et al.  Implementation of an auction-based distributed scheduling model for a dynamic job shop environment , 2001, Int. J. Comput. Integr. Manuf..

[46]  Stefano Riemma,et al.  A negotiation scheme for autonomous agents in job shop scheduling , 2002, Int. J. Comput. Integr. Manuf..

[47]  K. Robert Lai,et al.  Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation , 2010, Applied Intelligence.