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An Exact Procedure for the Resource-Constrained Weighted Earliness–Tardiness Project Scheduling Problem

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  • Mario Vanhoucke
  • Erik Demeulemeester
  • Willy Herroelen

Abstract

In this paper we study the resource-constrained project scheduling problem with weighted earliness–tardinesss penalty costs. Project activities are assumed to have a known deterministic due date, a unit earliness as well as a unit tardiness penalty cost and constant renewable resource requirements. The objective is to schedule the activities in order to minimize the total weighted earliness–tardinesss penalty cost of the project subject to the finish–start precedence constraints and the constant renewable resource availability constraints. With these features the problem becomes highly attractive in just-in-time environments. We introduce a depth-first branch-and-bound algorithm which makes use of extra precedence relations to resolve resource conflicts and relies on a fast recursive search algorithm for the unconstrained weighted earliness–tardinesss problem to compute lower bounds. The procedure has been coded in Visual C++, version 4.0 under Windows NT. Both the recursive search algorithm and the branch-and-bound procedure have been validated on a randomly generated problem set. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • Mario Vanhoucke & Erik Demeulemeester & Willy Herroelen, 2001. "An Exact Procedure for the Resource-Constrained Weighted Earliness–Tardiness Project Scheduling Problem," Annals of Operations Research, Springer, vol. 102(1), pages 179-196, February.
    • Handle: RePEc:spr:annopr:v:102:y:2001:i:1:p:179-196:10.1023/a:1010958200070
    DOI: 10.1023/A:1010958200070
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    Citations

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    Cited by:

    1. Feifei Li & Zhe Xu, 2018. "A multi-agent system for distributed multi-project scheduling with two-stage decomposition," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-24, October.
    2. Hartmann, Sönke & Briskorn, Dirk, 2010. "A survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 207(1), pages 1-14, November.
    3. Lei Lei & Michael Pinedo & Lian Qi & Shengbin Wang & Jian Yang, 2015. "Personnel scheduling and supplies provisioning in emergency relief operations," Annals of Operations Research, Springer, vol. 235(1), pages 487-515, December.
    4. E. Demeulemeester & F. Deblaere & J. Herbots & O. Lambrechts & S. Van de Vonder, 2007. "A Multi-level Approach to Project Management under Uncertainty," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(3), pages 391-409.
    5. Androutsopoulos, Konstantinos N. & Manousakis, Eleftherios G. & Madas, Michael A., 2020. "Modeling and solving a bi-objective airport slot scheduling problem," European Journal of Operational Research, Elsevier, vol. 284(1), pages 135-151.
    6. Fernando Jaramillo & Busra Keles & Murat Erkoc, 2020. "Modeling single machine preemptive scheduling problems for computational efficiency," Annals of Operations Research, Springer, vol. 285(1), pages 197-222, February.
    7. Carlo Mannino & Alessandro Mascis, 2009. "Optimal Real-Time Traffic Control in Metro Stations," Operations Research, INFORMS, vol. 57(4), pages 1026-1039, August.

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