Multi-agent-based approach to solve part selection and task allocation problem in flexible manufacturing systems

Agent technology is currently being considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. A multi-agent-based approach for solving the part allocation problems in flexible manufacturing systems (FMS) is presented that can easily cope with the dynamic environment. Four agents were involved in carrying out the tasks of allocating parts on different machines: communicator, machine, part and material handling device (MHD). Upon arrival in the manufacturing facility, the part informs the communicator agent about the task requirements. The communicator agent divides the task into subtasks and sends a call-for-bids message to the machine and MHD agents. Each machine responds in accordance with its process capabilities and buffer limit. This response may be for the whole task or for one or more subtasks and it contains the price and cost details for these subtasks along with the performance index and acceptance ratio of the machine. The final allocation is made based on the objective function that includes processing and transportation costs and time. An algorithm is presented that is used by the communicator agent for allocating parts to different machines. An illustrative example is given to solve the task allocation on five machines, with each machine having different performance index and acceptance ratio.

[1]  Michael Fisher,et al.  Representing and Executing Agent-Based Systems , 1995, ECAI Workshop on Agent Theories, Architectures, and Languages.

[2]  Jerzy W. Rozenblit,et al.  Design for high autonomy: An overview , 1992, Appl. Artif. Intell..

[3]  M. K. Tiwari,et al.  Application of an Autonomous Agent Network to Support the Architecture of a Holonic Manufacturing System , 2002 .

[4]  Katia Sycara,et al.  Multiagent coordination in tightly coupled task scheduling , 1997 .

[5]  Andrew B. Whinston,et al.  Designing Collaborative Systems to Support Reactive Problem-Solving in Manufacturing , 1994 .

[6]  Nader Azarmi,et al.  Software Agents and Soft Computing Towards Enhancing Machine Intelligence , 1997, Lecture Notes in Computer Science.

[7]  Kap Hwan Kim,et al.  A negotiation based scheduling for items with flexible process plans , 1997 .

[8]  Alberto RibesAbstract,et al.  Multi agent systems , 2019, Proceedings of the 2005 International Conference on Active Media Technology, 2005. (AMT 2005)..

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

[10]  Tim Finin,et al.  KQML - A Language and Protocol for Knowledge and Information Exchange , 1994 .

[11]  Tuomas Sandholm,et al.  An Implementation of the Contract Net Protocol Based on Marginal Cost Calculations , 1993, AAAI.

[12]  David Naso,et al.  Evolutionary adaptation of dispatching agents in heterarchical manufacturing systems , 2001 .

[13]  Shimon Y. Nof,et al.  Formation of autonomous agent networks for manufacturing systems , 2000 .

[14]  Philip R. Cohen,et al.  Toward a Semantics for an Agent Communications Language Based on Speech-Acts , 1996, AAAI/IAAI, Vol. 1.

[15]  Kevin J. Tilley Machining task allocation in discrete manufacturing systems , 1996 .

[16]  Nicholas R. Jennings,et al.  Negotiation decision functions for autonomous agents , 1998, Robotics Auton. Syst..

[17]  Riyaz Sikora,et al.  Coordination mechanisms for multi-agent manufacturing systems: applications to integrated manufacturing scheduling , 1997 .

[18]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[19]  Pattie Maes,et al.  Modeling Adaptive Autonomous Agents , 1993, Artificial Life.

[20]  Krithi Ramamritham,et al.  Dynamic Task Scheduling in Hard Real-Time Distributed systems , 1984, IEEE Software.

[21]  Jie Zhang,et al.  A Multi-Agent-Based Agile Shop Floor Control System , 2002 .

[22]  Michael R. Genesereth,et al.  Software agents , 1994, CACM.

[23]  Nicholas R. Jennings,et al.  Applying agent technology , 1995, Appl. Artif. Intell..

[24]  Carlos Ramos,et al.  A dynamic scheduling holon for manufacturing orders , 1998, J. Intell. Manuf..

[25]  Francisco P. Maturana MetaMorph: an adaptive multi-agent architecture for advanced manufacturing systems , 1997 .

[26]  Andrew Wallace,et al.  Application of AI to AGV control?agent control of AGVs , 2001 .

[27]  Yun Peng,et al.  Agent-Based Approach for Manufacturing Integration: The Ciimplex Experience , 1999, Appl. Artif. Intell..

[28]  Albert D. Baker,et al.  A survey of factory control algorithms that can be implemented in a multi-agent heterarchy: Dispatching, scheduling, and pull , 1998 .

[29]  Brahim Chaib-draa,et al.  An overview of distributed artificial intelligence , 1996 .

[30]  Paul Davidsson,et al.  A Framework for Autonomous Agents Based on the Concept of Anticipatory Systems , 1994 .

[31]  Van BrusselHendrik,et al.  Reference architecture for holonic manufacturing systems , 1998 .

[32]  Jacques Ferber,et al.  Reactive distributed artificial intelligence: principles and applications , 1996 .

[33]  Manoj Kumar Tiwari,et al.  Intelligent agent framework to determine the optimal conflict-free path for an automated guided vehicles system , 2002 .

[34]  George Rzevski,et al.  A framework for designing intelligent manufacturing systems , 1997 .

[35]  Jo Wyns,et al.  Reference architecture for holonic manufacturing systems, the key to support evolution and reconfiguration , 1999 .

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

[37]  Byung-In Kim,et al.  Intelligent agent based framework for manufacturing systems control , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[38]  N. K.C. Krothapalli,et al.  Design of negotiation protocols for multi-agent manufacturing systems , 1999 .

[39]  Jong-Hwan Kim,et al.  Approximate analysis of finite fork/join queueing networks , 1997 .

[40]  Luc Bongaerts,et al.  Designing Holonic manufacturing systems , 1998 .

[41]  H. Van Dyke Parunak,et al.  Applications of distributed artificial intelligence in industry , 1996 .

[42]  Weiming Shen,et al.  MetaMorph: An adaptive agent-based architecture for intelligent manufacturing , 1999 .

[43]  Shimon Y. Nof Information and collaboration models of integration , 1994 .

[44]  Botond Kádár,et al.  An object-oriented framework for developing distributed manufacturing architectures , 1998, J. Intell. Manuf..

[45]  Luc Bongaerts,et al.  A conceptual framework for holonic manufacturing: Identification of manufacturing holons , 1999 .

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

[47]  Victor R. Lesser,et al.  Coalition Formation among Bounded Rational Agents , 1995, IJCAI.

[48]  M. R. Genesereth,et al.  Knowledge Interchange Format Version 3.0 Reference Manual , 1992, LICS 1992.

[49]  P Bourgine,et al.  Towards a Practice of Autonomous Systems , 1992 .

[50]  Hai Tan,et al.  Management of heterogeneous networks with intelligent agents , 1999, Bell Labs Technical Journal.

[51]  Lynne E. Parker Designing control laws for cooperative agent teams , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

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

[53]  Manoj Kumar Tiwari,et al.  Formulation of mobile agents for integration of supply chain using the KLAIM concept , 2003 .

[54]  Andrew Y. C. Nee,et al.  A distributed multi-agent environment for product design and manufacturing planning , 2001 .