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Dynamic order acceptance and capacity planning on a single bottleneck resource

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  • Jade Herbots
  • Willy Herroelen
  • Roel Leus

Abstract

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for aggregate regular as well as nonregular capacity. The stream of incoming order arrivals is the main source of uncertainty in dynamic order acceptance and the company only has forecasts of the main properties of the future incoming projects. Project proposals arrive sequentially with deterministic interarrival times and a decision on order acceptance and capacity planning needs to be made each time a proposal arrives and its project characteristics are revealed. We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as to deterministically allocate a single bottleneck resource to the accepted projects, both with an eye on maximizing the expected revenues within the problem horizon. We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selection and aggregate capacity usage. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007

Suggested Citation

  • Jade Herbots & Willy Herroelen & Roel Leus, 2007. "Dynamic order acceptance and capacity planning on a single bottleneck resource," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(8), pages 874-889, December.
    • Handle: RePEc:wly:navres:v:54:y:2007:i:8:p:874-889
    DOI: 10.1002/nav.20259
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    Cited by:

    1. Tianke Feng & Joseph C. Hartman, 2015. "The dynamic and stochastic knapsack Problem with homogeneous‐sized items and postponement options," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(4), pages 267-292, June.
    2. Nicholas G. Hall & Daniel Zhuoyu Long & Jin Qi & Melvyn Sim, 2015. "Managing Underperformance Risk in Project Portfolio Selection," Operations Research, INFORMS, vol. 63(3), pages 660-675, June.
    3. R. Micale & C. M. La Fata & M. Enea & G. La Scalia, 2021. "Regenerative scheduling problem in engineer to order manufacturing: an economic assessment," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1913-1925, October.

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