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Network decomposition techniques for resource-constrained project scheduling

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  • A Sprecher

    (Christian-Albrechts-Universität zu Kiel)

Abstract

The purpose of this paper is to study an obvious but unexplored approach for scheduling resource-constrained projects. The approach combines elements of heuristic and exact solution procedures. The project considered is decomposed into subprojects, the subproblems are optimally solved, and the solutions are concatenated. The strategy is tested on the benchmark instances of ProGeu. Several of the best known makespans collected in PSPLIB are improved. The algorithm has reduced more best known makespans than the state-of-the-art heuristic for medium-sized projects. The decomposition approach outperforms the truncated version of the branch-and-bound algorithm employed. On average, the quality of the overall solution depends on the size of the subproblems, and on the quality of the solutions of the subproblems—if approximately solved. Consequently, on the one hand, the approach benefits from the progress made in the development of exact solution procedures. But, on the other hand, the results question the rigid construction of schedules by conventional algorithms relying on extensions of partial schedules, and thus provide fundamental insights into the development of exact solution procedures.

Suggested Citation

  • A Sprecher, 2002. "Network decomposition techniques for resource-constrained project scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(4), pages 405-414, April.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:4:d:10.1057_palgrave.jors.2601308
    DOI: 10.1057/palgrave.jors.2601308
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    Cited by:

    1. Sepehr Proon & Mingzhou Jin, 2011. "A genetic algorithm with neighborhood search for the resource‐constrained project scheduling problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 58(2), pages 73-82, March.
    2. Nadia Chaudry & Ingunn Vermedal & Kjetil Fagerholt & Maria Fleischer Fauske & Magnus Stålhane, 2020. "A decomposition solution approach to the troops-to-tasks assignment in military peacekeeping operations," The Journal of Defense Modeling and Simulation, , vol. 17(4), pages 357-371, October.
    3. Dieter Debels & Mario Vanhoucke, 2007. "A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem," Operations Research, INFORMS, vol. 55(3), pages 457-469, June.
    4. Kolisch, Rainer & Hartmann, Sonke, 2006. "Experimental investigation of heuristics for resource-constrained project scheduling: An update," European Journal of Operational Research, Elsevier, vol. 174(1), pages 23-37, October.
    5. Valls, Vicente & Ballestin, Francisco & Quintanilla, Sacramento, 2008. "A hybrid genetic algorithm for the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 185(2), pages 495-508, March.
    6. Debels, D. & Vanhoucke, M., 2006. "Meta-Heuristic resource constrained project scheduling: solution space restrictions and neighbourhood extensions," Vlerick Leuven Gent Management School Working Paper Series 2006-18, Vlerick Leuven Gent Management School.
    7. Arda Turkgenci & Huseyin Guden & Mehmet Gülşen, 2021. "Decomposition based extended project scheduling for make-to-order production," Operational Research, Springer, vol. 21(2), pages 801-825, June.
    8. Jürgen Kuster & Dietmar Jannach & Gerhard Friedrich, 2010. "Applying Local Rescheduling in response to schedule disruptions," Annals of Operations Research, Springer, vol. 180(1), pages 265-282, November.

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