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An efficient pseudo-polynomial algorithm for finding a lower bound on the makespan for the Resource Constrained Project Scheduling Problem

Authors :
Dmitry I. Arkhipov
Alexander A. Lazarev
Olga Battaïa
Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Moscow Institute of Physics and Technology - MIPT (RUSSIA)
Lomonosov Moscow State University - MSU (RUSSIA)
Trapeznikov Institute of Control Sciences (RUSSIA)
National Research University Higher School of Economics (RUSSIA)
Département d'Ingénierie des Systèmes Complexes - DISC (Toulouse, France)
Trapeznikov Institute of Control Sciences (ICS RAS)
Russian Academy of Sciences [Moscow] (RAS)
Lomonosov Moscow State University (MSU)
National Research University Higher School of Economics [St. Petersburg]
Moscow Institute of Physics and Technology [Moscow] (MIPT)
Département d'Ingénierie des Systèmes Complexes (DISC)
Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)
Source :
European Journal of Operational Research, European Journal of Operational Research, Elsevier, 2019, 275 (1), pp.35-44. ⟨10.1016/j.ejor.2018.11.005⟩
Publication Year :
2019
Publisher :
Elsevier BV, 2019.

Abstract

International audience; Several algorithms for finding a lower bound on the makespan for the Resource Constrained Project Scheduling Problem (RCPSP) were proposed in the literature. However, fast computable lower bounds usually do not provide the best estimations and the methods that obtain better bounds are mainly based on the cooperation between linear and constraint programming and therefore are time-consuming. In this paper, a new pseudo-polynomial algorithm is proposed to find a makespan lower bound for RCPSP with time-dependent resource capacities. Its idea is based on a consecutive evaluation of pairs of resources and their cumulated workload. Using the proposed algorithm, several bounds for the PSPLIB benchmark were improved. The results for industrial applications are also presented where the algorithm could provide good bounds even for very large problem instances.

Details

ISSN :
03772217
Volume :
275
Database :
OpenAIRE
Journal :
European Journal of Operational Research
Accession number :
edsair.doi.dedup.....ff2f8d57a643d5e6e8a7fcb4269c2f30
Full Text :
https://doi.org/10.1016/j.ejor.2018.11.005