Adaptive Fuzzy Inventory Control Algorithm for Replenishment Process Optimization in an Uncertain Environment

This paper presents a real case study of warehouse replenishment process optimization on a selected sample of representative materials. Optimization is performed with simulation model supported by inventory control algorithms. The adaptive fuzzy inventory control algorithm based on fuzzy stock-outs, highest stock level and total cost is introduced. The algorithm is tested and compared to the simulation results of the actual warehouse process and classic inventory control algorithms such as Least-unit cost, Part period balancing and Silver-Meal algorithm. The algorithms are tested on historic data and assessed using the Fuzzy Strategy Assessor (FSA). Simulation results are presented and advantages of fuzzy inventory control algorithm are discussed.

[1]  Jack Dongarra,et al.  Computational Science — ICCS 2003 , 2003, Lecture Notes in Computer Science.

[2]  Igor Bernik,et al.  Simulation Approach to Decision Assessment in Enterprises , 2000, Simul..

[3]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[4]  Hiroaki Ishii,et al.  Some inventory problems with fuzzy shortage cost , 2000, Fuzzy Sets Syst..

[5]  S. Minner.  Strategic Safety Stocks in Supply Chains , 2000 .

[6]  Jerry D. Smith,et al.  The Warehouse Management Handbook , 1988 .

[7]  Radivoj Petrovic,et al.  Supply chain modelling using fuzzy sets , 1999 .

[8]  Hannu Koivisto,et al.  Research on Fuzzy Inventory Control under Supply Chain Management Environment , 2003, International Conference on Computational Science.

[9]  A. P. Rotshtein,et al.  Inventory control as an identification problem based on fuzzy logic , 2006 .

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

[11]  T. T. Shannon,et al.  Adaptive critic based adaptation of a fuzzy policy manager for a logistic system , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[12]  G. Reiner,et al.  Customized supply chain design: Problems and alternatives for a production company in the food industry. A simulation based analysis , 2004 .

[13]  David F. Pyke,et al.  Inventory management and production planning and scheduling , 1998 .

[14]  D. Petrovic,et al.  Multicriteria ranking of inventory replenishment policies in the presence of uncertainty in customer demand , 2001 .

[15]  M. Darwish,et al.  Production and shipment lot sizing in a vendor-buyer supply chain with transportation cost , 2007, Eur. J. Oper. Res..

[16]  Manoranjan Maiti,et al.  An interactive method for inventory control with fuzzy lead-time and dynamic demand , 2005, Eur. J. Oper. Res..

[17]  S. A. Al-Araimi,et al.  Application of an Adaptive Neuro-Fuzzy Inference System in Inventory Control , 2003 .