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An adaptive memory programming framework for the resource-constrained project scheduling problem

Author

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  • Dimitris C. Paraskevopoulos
  • Christos D. Tarantilis
  • George Ioannou

Abstract

The Resource-Constrained Project Scheduling Problem (RCPSP) is one of the most intractable combinatorial optimisation problems that combines a set of constraints and objectives met in a vast variety of applications and industries. Its solution raises major theoretical challenges due to its complexity, yet presenting numerous practical dimensions. Adaptive memory programming (AMP) is one of the most successful frameworks for solving hard combinatorial optimisation problems (e.g. vehicle routing and scheduling). Its success stems from the use of learning mechanisms that capture favourable solution elements found in high-quality solutions. This paper challenges the efficiency of AMP for solving the RCPSP, to our knowledge, for the first time in the literature. Computational experiments on well-known benchmark RCPSP instances show that the proposed AMP consistently produces high-quality solutions in reasonable computational times.

Suggested Citation

  • Dimitris C. Paraskevopoulos & Christos D. Tarantilis & George Ioannou, 2016. "An adaptive memory programming framework for the resource-constrained project scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4938-4956, August.
  • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:16:p:4938-4956
    DOI: 10.1080/00207543.2016.1145814
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    Cited by:

    1. Amalia I. Nikolopoulou & Panagiotis P. Repoussis & Christos D. Tarantilis & Emmanouil E. Zachariadis, 2019. "Adaptive memory programming for the many-to-many vehicle routing problem with cross-docking," Operational Research, Springer, vol. 19(1), pages 1-38, March.
    2. Lu, Da & Gzara, Fatma, 2019. "The robust vehicle routing problem with time windows: Solution by branch and price and cut," European Journal of Operational Research, Elsevier, vol. 275(3), pages 925-938.

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