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Production planning with load dependent lead times: an update of research

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  • Julia Pahl
  • Stefan Voß
  • David Woodruff

Abstract

Lead times impact the performance of the supply chain significantly. Although there is a large body of literature concerning queuing models for the analysis of the relationship between capacity utilization and lead times, and another body of work on control and order release policies that take lead times into consideration, there have been relatively few aggregate planning models that recognize the (nonlinear) relationship between the planned utilization of capacity and lead times. In this paper we provide an in-depth discussion of the state-of-the art in this area, with particular attention to those models that are appropriate at the aggregate planning level. Copyright Springer Science+Business Media, LLC 2007

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  • Julia Pahl & Stefan Voß & David Woodruff, 2007. "Production planning with load dependent lead times: an update of research," Annals of Operations Research, Springer, vol. 153(1), pages 297-345, September.
    • Handle: RePEc:spr:annopr:v:153:y:2007:i:1:p:297-345:10.1007/s10479-007-0173-5
    DOI: 10.1007/s10479-007-0173-5
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    Cited by:

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    2. Guhlich, Hendrik & Fleischmann, Moritz & Mönch, Lars & Stolletz, Raik, 2018. "A clearing function based bid-price approach to integrated order acceptance and release decisions," European Journal of Operational Research, Elsevier, vol. 268(1), pages 243-254.
    3. Fan, Yingjie & Schwartz, Frank & Voß, Stefan, 2017. "Flexible supply chain planning based on variable transportation modes," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 654-666.
    4. Jalali, Hamed & Carmen, Raïsa & Van Nieuwenhuyse, Inneke & Boute, Robert, 2019. "Quality and pricing decisions in production/inventory systems," European Journal of Operational Research, Elsevier, vol. 272(1), pages 195-206.
    5. Yarong Chen & Hongming Zhou & Peiyu Huang & FuhDer Chou & Shenquan Huang, 2022. "A refined order release method for achieving robustness of non-repetitive dynamic manufacturing system performance," Annals of Operations Research, Springer, vol. 311(1), pages 65-79, April.
    6. Katsumi Morikawa & Katsuhiko Takahashi & Daisuke Hirotani, 2018. "Performance evaluation of candidate appointment schedules using clearing functions," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 509-518, March.
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    8. Ebrahim Nejad, Alireza & Niroomand, Iman & Kuzgunkaya, Onur, 2014. "Responsive contingency planning in supply risk management by considering congestion effects," Omega, Elsevier, vol. 48(C), pages 19-35.
    9. Cannella, Salvatore & Dominguez, Roberto & Ponte, Borja & Framinan, Jose M., 2018. "Capacity restrictions and supply chain performance: Modelling and analysing load-dependent lead times," International Journal of Production Economics, Elsevier, vol. 204(C), pages 264-277.
    10. Sadia Samar Ali & Haripriya Barman & Rajbir Kaur & Hana Tomaskova & Sankar Kumar Roy, 2021. "Multi-Product Multi Echelon Measurements of Perishable Supply Chain: Fuzzy Non-Linear Programming Approach," Mathematics, MDPI, vol. 9(17), pages 1-27, August.
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    12. Uday Venkatadri & Shentao Wang & Ashok Srinivasan, 2021. "A Model for Demand Planning in Supply Chains with Congestion Effects," Logistics, MDPI, vol. 5(1), pages 1-24, January.

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