Abstract—We propose a technique based on genetic algorithm to optimize inventory in supply chain management .We focus on to specifically 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. The complexity of the problem increases when more products and multiple agents are involved in inventory management process that has been resolved in this work. We apply our method on six member of supply chain studied model for optimization.
Index Terms—Genetic algorithm, inventory optimization, supply chain management, base stock level, agents.
Tarun Kumar is with Computer Science Engg. Dept., Bansthali University Bansthali (Raj.) India (e-mail: taruncdac@gmail.com)
S. R. Singh is with Mathematics Dept, D. N. College, Meerut (U.P.) India (e-mail: shivrajpundir@gmail.com)
C. B. Gupta is with Mathematics Dept., BITS, Pilani, Rajasthan, India (e-mail : cbgupta @ bits-pilani.ac.in)
Cite: Tarun Kumar, S. R. Singh, and C. B. Gupta, "Genetic Algorithm Based Multi Product and Multi Agent Inventory Optimization in Supply Chain Management," International Journal of Modeling and Optimization vol. 2, no. 6, pp. 653-657 , 2012.
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