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Nervousness in inventory management: Comparison of basic control rules

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  • de Kok, Ton
  • Inderfurth, Karl

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  • de Kok, Ton & Inderfurth, Karl, 1997. "Nervousness in inventory management: Comparison of basic control rules," European Journal of Operational Research, Elsevier, vol. 103(1), pages 55-82, November.
    • Handle: RePEc:eee:ejores:v:103:y:1997:i:1:p:55-82
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    1. Joseph D. Blackburn & Dean H. Kropp & Robert A. Millen, 1986. "A Comparison of Strategies to Dampen Nervousness in MRP Systems," Management Science, INFORMS, vol. 32(4), pages 413-429, April.
    2. Jensen, Thomas, 1993. "Measuring and improving planning stability of reorder-point lot-sizing policies," International Journal of Production Economics, Elsevier, vol. 30(1), pages 167-178, July.
    3. Dean H. Kropp & Robert C. Carlson, 1984. "A Lot-Sizing Algorithm for Reducing Nervousness in MRP Systems," Management Science, INFORMS, vol. 30(2), pages 240-244, February.
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    Cited by:

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    2. Peidro, David & Mula, Josefa & Jiménez, Mariano & del Mar Botella, Ma, 2010. "A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment," European Journal of Operational Research, Elsevier, vol. 205(1), pages 65-80, August.
    3. Roberto Rossi & S. Armagan Tarim & Ramesh Bollapragada, 2012. "Constraint-Based Local Search for Inventory Control Under Stochastic Demand and Lead Time," INFORMS Journal on Computing, INFORMS, vol. 24(1), pages 66-80, February.
    4. Roberto Rossi & S. Tarim & Brahim Hnich & Steven Prestwich, 2012. "Constraint programming for stochastic inventory systems under shortage cost," Annals of Operations Research, Springer, vol. 195(1), pages 49-71, May.
    5. Heisig, Gerald, 2001. "Comparison of (s,S) and (s,nQ) inventory control rules with respect to planning stability," International Journal of Production Economics, Elsevier, vol. 73(1), pages 59-82, August.
    6. Tarim, S. Armagan & Smith, Barbara M., 2008. "Constraint programming for computing non-stationary (R, S) inventory policies," European Journal of Operational Research, Elsevier, vol. 189(3), pages 1004-1021, September.
    7. Carlos Herrera & Sana Belmokhtar-Berraf & André Thomas & Víctor Parada, 2016. "A reactive decision-making approach to reduce instability in a master production schedule," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2394-2404, April.
    8. Demirel, Edil & Özelkan, Ertunga C. & Lim, Churlzu, 2018. "Aggregate planning with Flexibility Requirements Profile," International Journal of Production Economics, Elsevier, vol. 202(C), pages 45-58.
    9. Kuzu, Kaan & Li, Wanxi, 2016. "Exploring supplier performance risk and the buyer's role using chance-constrained data envelopment analysisAuthor-Name: Ross, Anthony D," European Journal of Operational Research, Elsevier, vol. 250(3), pages 966-978.
    10. Kilic, Onur A. & Tarim, S. Armagan, 2011. "An investigation of setup instability in non-stationary stochastic inventory systems," International Journal of Production Economics, Elsevier, vol. 133(1), pages 286-292, September.
    11. van Donselaar, K. & van den Nieuwenhof, J. & Visschers, J., 2000. "The impact of material coordination concepts on planning stability in supply chains," International Journal of Production Economics, Elsevier, vol. 68(2), pages 169-176, November.
    12. Rossi, Roberto & Tarim, S. Armagan & Hnich, Brahim & Prestwich, Steven, 2011. "A state space augmentation algorithm for the replenishment cycle inventory policy," International Journal of Production Economics, Elsevier, vol. 133(1), pages 377-384, September.
    13. Mula, Josefa & Peidro, David & Poler, Raul, 2010. "The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand," International Journal of Production Economics, Elsevier, vol. 128(1), pages 136-143, November.
    14. Tarim, S. Armagan & Kingsman, Brian G., 2006. "Modelling and computing (Rn, Sn) policies for inventory systems with non-stationary stochastic demand," European Journal of Operational Research, Elsevier, vol. 174(1), pages 581-599, October.

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