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An inventory system with investment to reduce yield variability and set-up cost

Author

Listed:
  • L-C Lin

    (National Chinyi Institute of Technology)

  • K-L Hou

    (National Defense Management College)

Abstract

The set-up cost and yield variability are given and fixed in existing production/inventory models with random yields. However, in many practical situations, they can be reduced by investment in modern production technology. In this paper, we consider an inventory system with random yield in which both the set-up cost and yield variability can be reduced through capital investment. The objective is to determine the optimal capital investment and ordering policies that minimize the expected total annual costs for the system. In addition, an iterative solution procedure is presented to find the optimal order quantity and reorder point and then the optimal set-up cost and yield standard deviation. Numerical examples are given to illustrate the results obtained and assess the cost savings by adopting capital investments. Managerial implications are also included.

Suggested Citation

  • L-C Lin & K-L Hou, 2005. "An inventory system with investment to reduce yield variability and set-up cost," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(1), pages 67-74, January.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:1:d:10.1057_palgrave.jors.2601793
    DOI: 10.1057/palgrave.jors.2601793
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    References listed on IDEAS

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    Cited by:

    1. G. P. Kiesmüller & K. Inderfurth, 2018. "Approaches for periodic inventory control under random production yield and fixed setup cost," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(2), pages 449-477, March.
    2. Mehran Ullah & Biswajit Sarkar & Iqra Asghar, 2019. "Effects of Preservation Technology Investment on Waste Generation in a Two-Echelon Supply Chain Model," Mathematics, MDPI, vol. 7(2), pages 1-20, February.
    3. Kulkarni, Shailesh S., 2008. "Loss-based quality costs and inventory planning: General models and insights," European Journal of Operational Research, Elsevier, vol. 188(2), pages 428-449, July.
    4. Hsu, P.H. & Wee, H.M. & Teng, H.M., 2010. "Preservation technology investment for deteriorating inventory," International Journal of Production Economics, Elsevier, vol. 124(2), pages 388-394, April.
    5. Zhang, Qinhong & Zhang, Dali & Tsao, Yu-Chung & Luo, Jianwen, 2016. "Optimal ordering policy in a two-stage supply chain with advance payment for stable supply capacity," International Journal of Production Economics, Elsevier, vol. 177(C), pages 34-43.
    6. Baruah, Pundarikaksha & Chinnam, Ratna Babu & Korostelev, Alexander & Dalkiran, Evrim, 2016. "Optimal soft-order revisions under demand and supply uncertainty and upstream information," International Journal of Production Economics, Elsevier, vol. 182(C), pages 14-25.
    7. Baruah, Pundarikaksha, 2006. "Supply Chains Facing Atypical Demand: Optimal Operational Policies And Benefits Under Information Sharing," MPRA Paper 16101, University Library of Munich, Germany.
    8. ASADABADI Mehdi Rajabi, 2016. "A Revision On Cost Elements Of The Eoq Model," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 11(1), pages 5-14, April.
    9. Laslo, Zohar & Gurevich, Gregory & Keren, Baruch, 2009. "Economic distribution of budget among producers for fulfilling orders under delivery chance constraints," International Journal of Production Economics, Elsevier, vol. 122(2), pages 656-662, December.

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