Finding optimum neighbor for routing based on multi-criteria, multi-agent and fuzzy approach

In this paper, a hierarchical multi-agent based routing has been introduced. In dynamic situations, the previously planned entire optimum path may not stay optimum over time. Thus the approach in this paper routes a job to the next optimum neighboring node from the current position, instead of deciding over the entire path before the journey begins. Whenever there is a need to choose the next optimum node for routing or whenever a job enters the system, the master agent calls the worker agents. The worker agents run in parallel and return the results to the master agent. The worker agents are killed after their tasks are completed. The master agent takes decision based on the data delivered by the worker agents through a multi-criteria decision analysis technique known as PROMETHEE. A total of five worker agents are used for seven criteria and fuzzy approach is applied in a fuzzy shortest path algorithm performed by a worker agent and in fuzzy weight calculation in PROMETHEE. Three examples with three different kinds of networks have been used to show the effectiveness of the entire approach. The motivation of the idea introduced in this paper has come from the mating behavior of a spider known as Tarantula where the female spider sometimes eats the male spider just after mating.

[1]  Dariusz Barbucha,et al.  Agent-based guided local search , 2012, Expert Syst. Appl..

[2]  Terry R. Payne,et al.  Developing Intelligent Agent Systems by Lin Padgham and Michael Winikoff, John Wiley and Sons, 230 pp., $45.00, ISBN 0-470-86120-7 , 2004, The Knowledge Engineering Review.

[3]  E. Grace Mary Kanaga,et al.  Multi-agent based Patient Scheduling Using Particle Swarm Optimization , 2012 .

[4]  Costin Badica,et al.  Multi-agent approach to distributed ant colony optimization , 2013, Sci. Comput. Program..

[5]  Nicholas R. Jennings,et al.  The Gaia Methodology for Agent-Oriented Analysis and Design , 2000, Autonomous Agents and Multi-Agent Systems.

[6]  Kamran Zamanifar,et al.  An agent-based parallel approach for the job shop scheduling problem with genetic algorithms , 2010, Math. Comput. Model..

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

[8]  Khalid Kouiss,et al.  Using multi-agent architecture in FMS for dynamic scheduling , 1997, J. Intell. Manuf..

[9]  Richard Y. K. Fung,et al.  Integrated process planning and scheduling by an agent-based ant colony optimization , 2010, Comput. Ind. Eng..

[10]  Chuan-Jun Su,et al.  Mobile multi-agent based, distributed information platform (MADIP) for wide-area e-health monitoring , 2008, Comput. Ind..

[11]  Omar López-Ortega,et al.  A multi-agent system to construct production orders by employing an expert system and a neural network , 2009, Expert Syst. Appl..

[12]  Fausto Giunchiglia,et al.  Tropos: An Agent-Oriented Software Development Methodology , 2004, Autonomous Agents and Multi-Agent Systems.

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

[14]  Bo Chen,et al.  Integrating mobile agent technology with multi-agent systems for distributed traffic detection and management systems , 2009 .

[15]  Feng Qian,et al.  A multi-agent immune network algorithm and its application to Murphree efficiency determination for the distillation column , 2011 .

[16]  Vasant Honavar,et al.  Autonomous agents for coordinated distributed parameterized heuristic routing in large dynamic communication networks , 2001, J. Syst. Softw..

[17]  Ramy Eltarras,et al.  Associative routing for wireless sensor networks , 2011, Comput. Commun..

[18]  Massimo Cossentino,et al.  Designing a multi-agent solution for a bookstore with the PASSI methodology , 2002, AOIS@CAiSE.

[19]  Liang Gao,et al.  An active learning genetic algorithm for integrated process planning and scheduling , 2012, Expert Syst. Appl..

[20]  Kazuo Miyashita,et al.  CAMPS: a constraint-based architecturefor multiagent planning and scheduling , 1998, J. Intell. Manuf..

[21]  Davy Monticolo,et al.  A collaborative Design for Usability approach supported by Virtual Reality and a Multi-Agent System embedded in a PLM environment , 2010, Comput. Aided Des..

[22]  Chih-Hsing Chu,et al.  Multi-agent hierarchical negotiation based on augmented price schedules decomposition for distributed design , 2012, Comput. Ind..

[23]  Chris Dollin,et al.  Object-oriented development: the fusion method , 1994 .

[24]  Jonathan Lee,et al.  Task-Based Specifications Through Conceptual Graphs , 1996, IEEE Expert.

[25]  Pedro Gómez-Gasquet,et al.  An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan , 2012, Expert Syst. Appl..

[26]  Amy J. C. Trappey,et al.  The design of a JADE-based autonomous workflow management system for collaborative SoC design , 2009, Expert Syst. Appl..

[27]  Duncan C. McFarlane,et al.  A component-based approach to the holonic control of a robot assembly cell , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[28]  Nicholas R. Jennings,et al.  Agent-Based Business Process Management , 1996, Int. J. Cooperative Inf. Syst..

[29]  Vincent Cheng-Siong Lee,et al.  A new multi-agent system framework for tacit knowledge management in manufacturing supply chains , 2009, J. Intell. Manuf..

[30]  Mohammad J. Tarokh,et al.  Intelligent evaluation of supplier bids using a hybrid technique in distributed supply chains , 2012 .

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

[32]  Weiming Shen,et al.  Enhancing the performance of an agent-based manufacturing system through learning and forecasting , 2000, J. Intell. Manuf..

[33]  Gong Li,et al.  Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions , 2012 .

[34]  Liang Gao,et al.  Integration of process planning and scheduling - A modified genetic algorithm-based approach , 2009, Comput. Oper. Res..

[35]  Nicholas R. Jennings,et al.  Using ARCHONTM to develop real-world DAI applications for electricity transportation management and particle accelerator control , 2007 .

[36]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[37]  Fu-Shiung Hsieh,et al.  Design of reconfiguration mechanism for holonic manufacturing systems based on formal models , 2010, Eng. Appl. Artif. Intell..

[38]  Mitsuo Gen,et al.  Network modeling and evolutionary optimization for scheduling in manufacturing , 2012, J. Intell. Manuf..

[39]  Leon Sterling,et al.  ROADMAP: extending the gaia methodology for complex open systems , 2002, AAMAS '02.

[40]  Kurosh Madani,et al.  An artificial negotiating agent modeling approach embedding dynamic offer generating and cognitive layer , 2011, Neurocomputing.

[41]  Abdelghani Bekrar,et al.  Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning , 2012, J. Intell. Manuf..

[42]  P. Leitao,et al.  ADACOR: a collaborative production automation and control architecture , 2005, IEEE Intelligent Systems.

[43]  Lei Wang,et al.  Pheromone-based coordination for manufacturing system control , 2012, J. Intell. Manuf..

[44]  Nurcin Celik,et al.  Hybrid agent-based simulation for policy evaluation of solar power generation systems , 2011, Simul. Model. Pract. Theory.

[45]  Manoj Kumar Tiwari,et al.  Agent oriented petroleum supply chain coordination: Co-evolutionary Particle Swarm Optimization based approach , 2011, Expert Syst. Appl..

[46]  Sankaran Mahadevan,et al.  Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment , 2012, Appl. Soft Comput..

[47]  Layuan Li,et al.  Utility-based QoS optimisation strategy for multi-criteria scheduling on the grid , 2007, J. Parallel Distributed Comput..

[48]  Paul Davidsson,et al.  TAPAS: A multi-agent-based model for simulation of transport chains , 2012, Simul. Model. Pract. Theory.

[49]  David Naso,et al.  A soft computing approach for task contracting in multi-agent manufacturing control , 2003, Comput. Ind..

[50]  Lei Gao,et al.  Ranking management strategies with complex outcomes: An AHP-fuzzy evaluation of recreational fishing using an integrated agent-based model of a coral reef ecosystem , 2012, Environ. Model. Softw..

[51]  Hossam S. Hassanein,et al.  Load-aware destination-controlled routing for MANETs , 2003, Comput. Commun..

[52]  Nicholas R. Jennings,et al.  Using Archon to Develop Real-World DAI Applications, Part 1 , 1996, IEEE Expert.

[53]  Wen-Chiung Lee,et al.  Branch-and-bound and simulated annealing algorithms for a two-agent scheduling problem , 2010, Expert Syst. Appl..

[54]  Nilesh Anand,et al.  GenCLOn: An ontology for city logistics , 2012, Expert Syst. Appl..

[55]  Kyu Ho Park,et al.  Shop-floor scheduling at shipbuilding yards using the multiple intelligent agent system , 1997, J. Intell. Manuf..

[56]  Musa Aydin,et al.  Finding optimum route of electrical energy transmission line using multi-criteria with Q-learning , 2011, Expert Syst. Appl..

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

[58]  Michael Winikoff,et al.  Developing intelligent agent systems - a practical guide , 2004, Wiley series in agent technology.

[59]  Karthik Vasudevan,et al.  Concurrent consideration of evacuation safety and productivity in manufacturing facility planning using multi-paradigm simulations , 2011, Comput. Ind. Eng..

[60]  Antoni Wibowo,et al.  A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm , 2013, J. Intell. Manuf..