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Logistics network design with inventory stocking for low-demand parts: Modeling and optimization

Author

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  • Vishv Jeet
  • Erhan Kutanoglu
  • Amit Partani

Abstract

This paper models, analyzes and develops solution techniques for a network design and inventory stocking problem. The proposed model captures important features of a real service part logistics system, namely time-based service level requirements, and stochastic demands satisfied by facilities operating with a one-for-one replenishment policy. In essence, along with usual decisions of location and allocation, the model considers stock levels and fill rates as decisions, varying across facilities to achieve system-wide target service levels. A variable substitution scheme is used to develop an equivalent convex model for an originally non-convex problem. An outer-approximation scheme is used to linearize the convex model. Exact solution schemes based on the linearized model are proposed and computationally less demanding lower and upper bounding techniques for the problem are devised. Results from extensive computational experiments on a variety of problem instances based on real-life industrial data show the effectiveness of the overall approach.[Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resources: An Appendix consisting of proofs of the propositions, explanation and effectiveness of valid inequalities obtained via binary representation, settings of CPLEX options and further insights and observations.]

Suggested Citation

  • Vishv Jeet & Erhan Kutanoglu & Amit Partani, 2009. "Logistics network design with inventory stocking for low-demand parts: Modeling and optimization," IISE Transactions, Taylor & Francis Journals, vol. 41(5), pages 389-407.
  • Handle: RePEc:taf:uiiexx:v:41:y:2009:i:5:p:389-407
    DOI: 10.1080/07408170802512602
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    Cited by:

    1. Jin, Tongdan & Tian, Zhigang & Xie, Min, 2015. "A game-theoretical approach for optimizing maintenance, spares and service capacity in performance contracting," International Journal of Production Economics, Elsevier, vol. 161(C), pages 31-43.
    2. Ghaddar, Bissan & Sakr, Nizar & Asiedu, Yaw, 2016. "Spare parts stocking analysis using genetic programming," European Journal of Operational Research, Elsevier, vol. 252(1), pages 136-144.
    3. Jeet, Vishv & Kutanoglu, Erhan, 2018. "Part commonality effects on integrated network design and inventory models for low-demand service parts logistics systems," International Journal of Production Economics, Elsevier, vol. 206(C), pages 46-58.
    4. Sinha, Ankur & Das, Arka & Anand, Guneshwar & Jayaswal, Sachin, 2023. "A general purpose exact solution method for mixed integer concave minimization problems," European Journal of Operational Research, Elsevier, vol. 309(3), pages 977-992.
    5. Fritzsche, R., 2012. "Cost adjustment for single item pooling models using a dynamic failure rate: A calculation for the aircraft industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(6), pages 1065-1079.
    6. Shu, Jia & Li, Zhengyi & Shen, Houcai & Wu, Ting & Zhong, Weijun, 2012. "A logistics network design model with vendor managed inventory," International Journal of Production Economics, Elsevier, vol. 135(2), pages 754-761.
    7. Wheatley, David & Gzara, Fatma & Jewkes, Elizabeth, 2015. "Logic-based Benders decomposition for an inventory-location problem with service constraints," Omega, Elsevier, vol. 55(C), pages 10-23.
    8. Candas, Mehmet Ferhat & Kutanoglu, Erhan, 2020. "Integrated location and inventory planning in service parts logistics with customer-based service levels," European Journal of Operational Research, Elsevier, vol. 285(1), pages 279-295.
    9. Iyoob, Ilyas Mohamed & Kutanoglu, Erhan, 2013. "Inventory sharing in integrated network design and inventory optimization with low-demand parts," European Journal of Operational Research, Elsevier, vol. 224(3), pages 497-506.

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