Inventory Optimization in Supply Chain Management using Genetic Algorithm

Summary Inventory management plays a vital role in supply chain management. The service provided to the customer eventually gets enhanced once the efficient and effective management of inventory is carried out all through the supply chain. Thus the determination of the inventory to be held at various levels in a supply chain becomes inevitable so as to ensure minimal cost for the supply chain. Minimizing the total supply chain cost is meant for minimizing holding and shortage cost in the entire supply chain. The minimization of the total supply chain cost can only be achieved when optimization of the base stock level is carried out at each member of the supply chain. A serious issue in the implementation of the same is that the excess stock level and shortage level is not static for every period. In this paper, we have developed a new and efficient approach that works on Genetic Algorithms in order to distinctively determine the most probable excess stock level and shortage level required for inventory optimization in the supply chain such that the total supply chain cost is minimized.

[1]  Luis Rabelo,et al.  Stability analysis of the supply chain by using neural networks and genetic algorithms , 2007, 2007 Winter Simulation Conference.

[2]  B. Beamon.  Supply chain design and analysis:: Models and methods , 1998 .

[3]  Michael J. Magazine,et al.  A Taxonomic Review of Supply Chain Management Research , 1999 .

[4]  Kesheng Wang,et al.  Applying Genetic Algorithms to Optimize the Cost of Multiple Sourcing Supply Chain Systems – An Industry Case Study , 2008 .

[5]  Michael J. Magazine,et al.  Quantitative Models for Supply Chain Management , 1998 .

[6]  Zach G. Zacharia,et al.  DEFINING SUPPLY CHAIN MANAGEMENT , 2001 .

[7]  Robert J. Vokurka,et al.  Defining supply chain management: a historical perspective and practical guidelines , 1999 .

[8]  Jeffrey A. Joines,et al.  Supply chain multi-objective simulation optimization , 2002, Proceedings of the Winter Simulation Conference.

[9]  Hui Gao,et al.  Air material inventory optimization model based on genetic algorithm , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[10]  C.M. Adams.  Inventory optimization techniques, system vs. item level inventory analysis , 2004, Annual Symposium Reliability and Maintainability, 2004 - RAMS.

[11]  Hau L. Lee,et al.  The Evolution of Supply-Chain-Management Models and Practice at Hewlett-Packard , 1995 .

[12]  Chih-Yao Lo.  Advance of Dynamic Production-Inventory Strategy for Multiple Policies Using Genetic Algorithm , 2008 .

[13]  Yoshiaki Shimizu,et al.  Evolutional Optimization on Material Ordering and Inventory Control of Supply Chain through Incentive Scheme , 2007 .

[14]  David P. Stone.  An Autonomous Agent for Supply Chain Management , 2007 .

[15]  Robin O. Roundy,et al.  Approximation Algorithms for Stochastic Inventory Control Models , 2007, Math. Oper. Res..

[16]  Maged Dessouky,et al.  A genetic algorithm approach to the integrated inventory-distribution problem , 2006 .

[17]  João Caldeira,et al.  Supply-Chain Management Using ACO and Beam-ACO Algorithms , 2007, 2007 IEEE International Fuzzy Systems Conference.

[18]  P. Radhakrishnan,et al.  Genetic Algorithm Based Inventory Optimization Analysis in Supply Chain Management , 2009, 2009 IEEE International Advance Computing Conference.

[19]  Roberto Rossi,et al.  Replenishment Planning for Stochastic Inventory Systems with Shortage Cost , 2007, CPAIOR.

[20]  S. Prestwich,et al.  Cost-based Filtering for Stochastic Inventory Systems with Shortage Cost , 2007 .