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Setting planned lead times for a make-to-order production system with master schedule smoothing

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  • Chee-Chong Teo
  • Rohit Bhatnagar
  • Stephen Graves

Abstract

This article considers a make-to-order manufacturing environment with fixed guaranteed delivery lead times and multiple product families, each with a stochastic demand process. The primary challenge in this environment is how to meet the quoted delivery times subject to fluctuating workload and capacity limits. The tactical planning parameters are considered, namely, the planning windows and planned lead times. The planning process that is modeled is the one in which the demand represents a dynamic input into the master production schedule. A planning window for each product family controls how the schedule of each product family is translated into a job release. It can be thought of as the slack that exists when the fixed quoted delivery lead time is longer than the total planned production lead time. Furthermore, the planned lead time of each station regulates the workflow within a multi-station shop. The model has underlying discrete time periods to allow the modeling of the planning process that is typically defined in time buckets; within each time period, the intra-period workflow that permits multiple job movements within the time period is modeled. The presented model characterizes key performance measures for the shop as functions of the planning windows and planned lead times. An optimization model is formulated that is able to determine the values of these planning parameters that minimize the relevant production-related costs. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for additional results of the simulation study in Section 6 of this manuscript.]

Suggested Citation

  • Chee-Chong Teo & Rohit Bhatnagar & Stephen Graves, 2011. "Setting planned lead times for a make-to-order production system with master schedule smoothing," IISE Transactions, Taylor & Francis Journals, vol. 43(6), pages 399-414.
  • Handle: RePEc:taf:uiiexx:v:43:y:2011:i:6:p:399-414
    DOI: 10.1080/0740817X.2010.523765
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    Cited by:

    1. Zahraei, Seyed Mehdi & Teo, Chee-Chong, 2018. "Optimizing a recover-and-assemble remanufacturing system with production smoothing," International Journal of Production Economics, Elsevier, vol. 197(C), pages 330-341.
    2. Liu, Liming & Xu, He & Zhu, Stuart X., 2020. "Push verse pull: Inventory-leadtime tradeoff for managing system variability," European Journal of Operational Research, Elsevier, vol. 287(1), pages 119-132.
    3. Zümbül Atan & Ton de Kok & Nico P. Dellaert & Richard van Boxel & Fred Janssen, 2016. "Setting Planned Leadtimes in Customer-Order-Driven Assembly Systems," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 122-140, February.
    4. Milne, R. John & Mahapatra, Santosh & Wang, Chi-Tai, 2015. "Optimizing planned lead times for enhancing performance of MRP systems," International Journal of Production Economics, Elsevier, vol. 167(C), pages 220-231.
    5. Rong Yuan & Stephen C. Graves, 2016. "Setting optimal production lot sizes and planned lead times in a job shop," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6105-6120, October.
    6. Gansterer, Margaretha & Almeder, Christian & Hartl, Richard F., 2014. "Simulation-based optimization methods for setting production planning parameters," International Journal of Production Economics, Elsevier, vol. 151(C), pages 206-213.

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