Fuzzy BDI agents for supply chain monitoring in an uncertain environment

ABSTRACT The monitoring of supply chains (SCs) is a major challenge due to the increasing complexity of the global market, which increases the exposure of monitoring systems to disruptions, caused by technological innovations and consumer needs. Thus, a dynamic behaviour model to incorporate demand uncertainty in the SC of a Small and Medium Enterprise (SME) was designed in the present study. In our system, information uncertainty is modelled in a fuzzy manner based on Belief, Desire and Intention (BDI) architecture. To achieve our objective, we enhance the multi-agent knowledge model of the SC. Our contribution includes the development of a monitoring system. We first propose a dynamic reasoning model of fuzzy BDI agents faced with an uncertain situation. We then describe the collaborative behaviour between the agents of the SMEs. The challenge addressed in this study is how to make beneficial decisions given uncertain information.

[1]  Anand S. Rao,et al.  An Abstract Architecture for Rational Agents , 1992, KR.

[2]  Sankar K. Pal,et al.  Soft computing data mining , 2004, Inf. Sci..

[3]  MuDer Jeng,et al.  An unsupervised neural network approach for automatic semiconductor wafer defect inspection , 2009, Expert Syst. Appl..

[4]  Chunyan Miao,et al.  A cognitive approach for agent-based personalized recommendation , 2007, Knowl. Based Syst..

[5]  Chu‐Hua Kuei,et al.  Designing and Managing the Supply Chain Concepts, Strategies, and Case Studies , 2000 .

[6]  Wen-Yau Liang,et al.  Agent-based demand forecast in multi-echelon supply chain , 2006, Decis. Support Syst..

[7]  Gregory M. P. O'Hare,et al.  Decision-making of BDI agents, a fuzzy approach , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[8]  Chuen-Chien Lee.  FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[9]  Juite Wang,et al.  A possibilistic decision model for new product supply chain design , 2007, Eur. J. Oper. Res..

[10]  Gideon Cohen,et al.  Neural networks implementations to control real-time manufacturing systems , 1998 .

[11]  Yufei Yuan,et al.  Using agent technology to support supply chain management:potentials and challenges , 2001 .

[12]  C. P. Wang,et al.  An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination , 2002, Neural Networks.

[13]  C C Lee,et al.  FUZZY LOGIC IN CONTROL SYSTEM FUZZY LOGIC CONTROLLER-PART II , 1990 .

[14]  Muh-Cherng Wu,et al.  Design of BOM configuration for reducing spare parts logistic costs , 2008, Expert Syst. Appl..

[15]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[16]  David Z. Zhang,et al.  Agent-based model for optimising supply-chain configurations , 2008 .

[17]  Xiaohui Hu,et al.  Using Fuzzy Logic as a Reasoning Model for BDI Agents , 2010, 2010 International Conference on Computational Intelligence and Software Engineering.

[18]  Chiang Kao,et al.  Solving fuzzy transportation problems based on extension principle , 2004, Eur. J. Oper. Res..

[19]  I. B. Turksen,et al.  A FUZZY INTELLIGENT INFORMATION AGENT ARCHITECTURE FOR SUPPLY CHAIN , 2008 .

[20]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[21]  C. Moraga.  Introduction to Fuzzy Logic , 2005 .

[22]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[23]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[24]  Guanrong Chen,et al.  Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems , 2000 .

[25]  S. G. Li,et al.  The inventory management system for automobile spare parts in a central warehouse , 2008, Expert Syst. Appl..

[26]  Hayfa Zgaya,et al.  PAAN : Partial Agreement Negotiation Network based on Intelligent Agents in Crisis Situation , 2022 .

[27]  Georges Habchi,et al.  A Generic Knowledge Model for SME Supply Chain Based on Multiagent Paradigm , 2012 .

[28]  Arun Kumar,et al.  An agent-based framework for collaborative negotiation in the global manufacturing supply chain network , 2006 .

[29]  Paul D. Larson,et al.  Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies, David Simchi-Levi Philip Kaminsky Edith Simchi-Levi , 2001 .

[30]  Alain Martel,et al.  The design of robust value-creating supply chain networks , 2010, Eur. J. Oper. Res..

[31]  Peter F. Drucker,et al.  Management's New Paradigms , 2012, The Essential Drucker.

[32]  D. Lambert,et al.  Issues in Supply Chain Management , 2000 .

[33]  Jill Fain Lehman,et al.  A Gentle Introduction to Soar, an Architecture for Human Cognition. , 1996 .

[34]  Radivoj Petrovic,et al.  Modelling and simulation of a supply chain in an uncertain environment , 1998, Eur. J. Oper. Res..

[35]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[36]  H. Van Dyke Parunak,et al.  What Can Agents Do in Industry, and Why? An Overview of Industrially-Oriented R&D at CEC , 1998, CIA.

[37]  Reha Uzsoy,et al.  Executing production schedules in the face of uncertainties: A review and some future directions , 2005, Eur. J. Oper. Res..

[38]  Carine Bournez,et al.  Modèle de contrôle par émergence de coordinations dans un réseau de contrats multi-agents , 2001, JFIADSMA.

[39]  Amal El Fallah Seghrouchni,et al.  Learning in BDI Multi-agent Systems , 2004, CLIMA.