Comparison Of Periodic-Review Inventory Control Policies In A Serial Supply Chain

Nihan KABADAYI, Timur KESKİNTÜRK
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Abstract


Supply chain management provides customers with the right product or service at a reasonable price, in the right place, at the right time, and with the best quality possible, thus increasing customer satisfaction. The inventory is held at the multiple sites in a supply chain. Effective and efficient management of inventory in the supply chain process has a significant impact on improving the ultimate customer service provided to the customer. Reducing inventory cost, which is a major part of total supply chain costs, will help provide products or services at a better price. This study aims to compare (R, S) and (R, S, Qmin) inventory control policies in a serial supply chain.  We develop a simulation based genetic algorithm (GA) in order to find the optimal numerical "S" value that minimizes the total supply chain cost (TSCC) and compare our results between two methods.


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DOI: http://dx.doi.org/10.17093/aj.2015.3.2.5000148311

References


Axsäter, S. (2007). Inventory control (Vol. 90). Springer Science & Business Media.

Axsäter, S., & Rosling, K. (1993). Notes: Installation vs. echelon stock policies for multilevel inventory control. Management Science, 39(10), 1274-1280.

Azadivar, F., & Tompkins, G. (1999). Simulation optimization with qualitative variables and structural model changes: A genetic algorithm approach. European Journal of Operational Research, 113(1), 169-182.

Chambers, J. (1995). Practical Handbook of Genetic Algorithms: Volume 2: New Frontiers, CRC-Press; 1 edition.

Chen, F. (1999). On (R, NQ) policies in serial inventory systems. In Quantitative models for supply chain management (pp. 71-109). Springer US.

Chopra S. & Meindl P. (2010). Supply Chain Management: Strategy, Planning and Operation, Prentice-Hall Inc., New Jersey.

Ding, H., Benyoucef, L., & Xie, X. (2006). A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization. Engineering Applications of Artificial Intelligence, 19(6), 609-623.

Kapuscinski, R., & Tayur, S. (1999). Optimal policies and simulation-based optimization for capacitated production inventory systems. In Quantitative Models for Supply Chain Management (pp. 7-40). Springer US.

Kiesmüller, G. P., De Kok, A. G., & Dabia, S. (2011). Single item inventory control under periodic review and a minimum order quantity. International Journal of Production Economics, 133(1), 280-285.

Lee, H. L., & Billington, C. (1992). Managing supply chain inventory: pitfalls and opportunities. Sloan management review, 33(3).

Marseguerra, M., Zio, E., & Podofillini, L. (2002). Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation. Reliability Engineering & System Safety, 77(2), 151-165.

Nahmias S. (2009). Production and Operation Analysis, McGraw-Hill International edition, New York.

Petrovic, D., Roy, R., & Petrovic, R. (1998). Modelling and simulation of a supply chain in an uncertain environment. European journal of operational research, 109(2), 299-309.

Simchi-Levi D., Kaminsky P., Simchi-Levi E. (2000). Designing and Managing the Supply Chain, Irwin McGraw-Hill.

Talbi El-G. (2009). Metaheuristics, John Wiley & Sons, Inc.

Zhou, B., Zhao, Y., & Katehakis, M. N. (2007). Effective control policies for stochastic inventory systems with a minimum order quantity and linear costs. International Journal of Production Economics, 106(2), 523-531.




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