×

A simulation-based genetic algorithm for inventory optimization in a serial supply chain. (English) Zbl 1060.90006

Int. Trans. Oper. Res. 12, No. 1, 101-127 (2005); erratum ibid. 12, 479 (2005).
Summary: One of the important aspects of supply chain management is inventory management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The main focus of this work is to study the performance of a single-product serial supply chain operating with a base-stock policy and to optimize the inventory (i.e. base stock) levels in the supply chain so as to minimize the Total Supply Chain Cost (TSCC), comprising holding and shortage costs at all the installations in the supply chain. A genetic algorithm (GA) is proposed to optimize the base-stock levels with the objective of minimizing the sum of holding and shortage costs in the entire supply chain. Simulation is used to evaluate the base-stock levels generated by the GA. The proposed GA is evaluated with the consideration of a variety of supply chain settings in order to test for its robustness of performance across different supply chain scenarios. The effectiveness of the proposed GA (in terms of generating base-stock levels with minimum TSCC) is compared with that of a random search procedure. In addition, optimal base-stock levels are obtained through complete enumeration of the solution space and compared with those yielded by the GA. It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposed GA.

MSC:

90B05 Inventory, storage, reservoirs
90C59 Approximation methods and heuristics in mathematical programming
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Ballou R.H., Business Logistics Management (1992)
[2] DOI: 10.1080/00207540110053156 · Zbl 1060.90502
[3] Campbell H.G., Management Science 16 pp B630– (1970)
[4] Christopher M., Logistics and Supply Chain Management. Financial Times (2000)
[5] Clark A.J., Management Science 6 pp 475– (1960)
[6] DOI: 10.1016/S0969-7012(99)00030-1
[7] Dannenbring D.G., Management Science 23 pp 1174– (1977)
[8] Deo N., System Simulation with Digital Computer (1999)
[9] DOI: 10.1108/09600039710170566
[10] DOI: 10.1287/opre.48.2.216.12376
[11] Ganeshan R., Quantitative Models for Supply Chain Management pp 841– (1999)
[12] Gen M., Genetic Algorithms and Engineering Optimisation (2000)
[13] Glasserman P., Management Science 41 pp 263– (1995)
[14] Goldberg D.E., Genetic Algorithms in Search, Optimisation and Machine Learning (1989) · Zbl 0721.68056
[15] DOI: 10.1016/S0925-5273(00)00171-7
[16] Lee H.L., Sloan Management Review 33 pp 65– (1992)
[17] Lee H.L., Operations Research 41 pp 835– (1993)
[18] DOI: 10.1016/S0377-2217(97)00118-5 · Zbl 0916.90095
[19] DOI: 10.1016/S0360-8352(02)00066-9
[20] Papoulis A., Probability, Random Variables, and Stochastic Processes (1985) · Zbl 0191.46704
[21] DOI: 10.1016/S0377-2217(98)00058-7 · Zbl 0937.90047
[22] DOI: 10.1287/opre.48.2.189.12380
[23] Simchi-Levi D., Design and Managing the Supply Chain Concepts, Strategies and Case Studies (2000)
[24] DOI: 10.1016/0377-2217(94)00354-F · Zbl 0907.90130
[25] DOI: 10.1108/09576060010335627
[26] Strader T.J., Journal of Artificial Societies and Social Simulation 1 (1998)
[27] DOI: 10.1016/0377-2217(93)90182-M · Zbl 0769.90052
[28] DOI: 10.1016/0377-2217(96)00098-7 · Zbl 0929.90004
[29] Towill D.R., International Journal of Physical Distribution and Logistics Mangement 22 pp 3– (1992)
[30] DOI: 10.1016/S0377-2217(97)80080-X · Zbl 0922.90062
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.