Optimization of a Vendor Managed Inventory Supply Chain Based on Complex Fuzzy Control Theory

This paper recommends a scaling factors fine-tuning fuzzy logic control approach to optimize the dy- namic performance of one typical vendor managed inventory supply chain with automatic pipeline, inventory and order based production control system(VMI-APIOBPCS), based on complex fuzzy control theory. The first thing is to embed a dual-input single-output fuzzy logic controller into the system based on the classic control engineer- ing model. Then, the fuzzy inputs are given different weights by the way of scaling factors in order to optimize the system further. This methodology can make good use of managers' experience accumulated in perennial practice and the managers' rational estimation of different circumstances. Lastly, the simulation results show that, this method can improve the dynamic performance of VMI-APIOBPCS, especially the inventory dynamic behaviors.

[1]  Christos D. Tarantilis,et al.  Dynamic modeling and control of supply chain systems: A review , 2008, Comput. Oper. Res..

[2]  Denis Royston Towill,et al.  Dynamic analysis of an inventory and order based production control system , 1982 .

[3]  Swarup Medasani,et al.  An overview of membership function generation techniques for pattern recognition , 1998, Int. J. Approx. Reason..

[4]  A. Ancarani,et al.  A human experiment on inventory decisions under supply uncertainty , 2013 .

[5]  Russian Federation,et al.  Open research issues on Nonlinear Dynamics, Dynamical Systems and Processes , 2014 .

[6]  Filippo Neri,et al.  Open research issues on Computational Techniques for Financial Applications , 2014 .

[7]  Libor Peka,et al.  An Introduction to the Special Issue on Advanced Control Methods : Theory and Application , 2013 .

[8]  Filippo Neri,et al.  Open research issues on Modeling, Simulation and Optimization in Electrical Systems , 2014 .

[9]  FILIPPO NERI,et al.  Foreword to the Special Issue on Collaborative Systems , 2012 .

[10]  Hamid R. Berenji,et al.  Learning and tuning fuzzy logic controllers through reinforcements , 1992, IEEE Trans. Neural Networks.

[11]  F. Neri,et al.  An Introduction to the Special Issue on Time Delay Systems : Modelling , Identification , Stability , Control and Applications , 2012 .

[12]  Mohammed M. Naim,et al.  The System Simplification Approach in Understanding the Dynamic Behaviour of a Manufacturing Supply Chain , 1992 .

[13]  Chin-Teng Lin,et al.  A neural fuzzy system with fuzzy supervised learning , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Chwen Sheu,et al.  The impact of replenishment policy deviations in a decentralized supply chain , 2008 .

[15]  Denis Royston Towill,et al.  A procedure for the optimization of the dynamic response of a Vendor managed inventory system , 2002 .

[16]  Mark T. Leung,et al.  Stocking policy in a two-party vendor managed channel with space restrictions , 2009 .

[17]  Calin Ciufudean,et al.  Open research issues on Multi-models for Complex Technological Systems , 2014 .

[18]  Stephen M. Disney,et al.  Measuring and avoiding the bullwhip effect: A control theoretic approach , 2003, Eur. J. Oper. Res..

[19]  Roger Jianxin Jiao,et al.  Production, Manufacturing and Logistics Adaptive Fuzzy Vendor Managed Inventory Control for Mitigating the Bullwhip Effect in Supply Chains , 2022 .

[20]  Filippo Neri.  Open research issues on Advanced Control Methods: Theory and Application , 2014 .

[21]  Christer Carlsson,et al.  A position paper on the agenda for soft decision analysis , 2002, Fuzzy Sets Syst..

[22]  Haldun Süral,et al.  A single supplier-single retailer system with an order-up-to level inventory policy , 2008, Oper. Res. Lett..

[23]  Ying Li,et al.  The Study on the Relationship between Hypercompetition and Organizational Innovation: The Role of Organizational Learning , 2011 .

[24]  Lionel Jouffe,et al.  Fuzzy inference system learning by reinforcement methods , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[25]  F. Neri,et al.  An Introduction to the Special Issue : Recent Advances in Defense Systems : Applications , Methodology , Technology , 2012 .

[26]  Denis Royston Towill,et al.  Genetic algorithm optimisation of a class of inventory control systems , 2000 .

[27]  Filippo Neri,et al.  An Introduction to the special issue on computational techniques for trading systems, time series forecasting, stock market modeling, financial assets modeling , 2013 .

[28]  Angappa Gunasekaran,et al.  A decision support system for integrating manufacturing and product design into the reconfiguration of the supply chain networks , 2012, Decis. Support Syst..

[29]  A. S. White.  Management of inventory using control theory , 1999 .

[30]  S. Prabhakar Karthikeyan,et al.  Open research issues on Deregulated Electricity Market: Investigation and Solution Methodologies , 2014 .

[31]  Marko Jaksic,et al.  The effect of replenishment policies on the bullwhip effect: A transfer function approach , 2008, Eur. J. Oper. Res..

[32]  Ping-Feng Pai,et al.  A simulation of vendor managed inventory dynamics using fuzzy arithmetic operations with genetic algorithms , 2010, Expert Syst. Appl..

[33]  S. Disney,et al.  On the equivalence of control theoretic, differential, and difference equation approaches to modeling supply chains , 2006 .

[34]  CLAUDIO GUARNACCIA.  An Introduction to the Special Issue on Recent Methods on Physical Polluting Agents and Environment Modeling and Simulation , 2012 .

[35]  Mohamed Mohamed Naim,et al.  Dynamic performance of a hybrid inventory system with a Kanban policy in remanufacturing process , 2006 .

[36]  David Collier.  The human factors of project team decision-making for radioactive waste management , 2012, Cognition, Technology & Work.

[37]  B. Samanta,et al.  An inventory control model using fuzzy logic , 2001 .

[38]  Martin Mönnigmann,et al.  Optimization of a vendor managed inventory supply chain with guaranteed stability and robustness , 2011 .