Standard

An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem. / Goncharov, Evgenii N.

Communications in Computer and Information Science. Vol. 1739 Springer Science and Business Media Deutschland GmbH, 2022. p. 35-47.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Goncharov, EN 2022, An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem. in Communications in Computer and Information Science. vol. 1739, Springer Science and Business Media Deutschland GmbH, pp. 35-47. https://doi.org/10.1007/978-3-031-22990-9_3

APA

Goncharov, E. N. (2022). An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem. In Communications in Computer and Information Science (Vol. 1739, pp. 35-47). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-22990-9_3

Vancouver

Goncharov EN. An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem. In Communications in Computer and Information Science. Vol. 1739. Springer Science and Business Media Deutschland GmbH. 2022. p. 35-47 doi: 10.1007/978-3-031-22990-9_3

Author

Goncharov, Evgenii N. / An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem. Communications in Computer and Information Science. Vol. 1739 Springer Science and Business Media Deutschland GmbH, 2022. pp. 35-47

BibTeX

@inproceedings{2f28a76aeafe4bf8bacb029fda83efb1,
title = "An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem",
abstract = "This paper presents an improved genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The schedules are constructed using a heuristic that builds active schedules based on priorities that takes into account the degree of criticality for the resources. The degree of resource{\textquoteright}s criticality is derived from the solution of a relaxed problem with a constraint on accumulative resources. The computational results with instances from the PCPLIB library validate the effectiveness of the proposed algorithm. We have obtain some of the best average deviations of the solutions from the critical path value. The best known solutions have been improved for some instances from the PCPLIB.",
author = "Goncharov, {Evgenii N.}",
note = "The study was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project FWNF-2022-0019).",
year = "2022",
doi = "10.1007/978-3-031-22990-9_3",
language = "English",
isbn = "978-3-031-22989-3",
volume = "1739",
pages = "35--47",
booktitle = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
address = "Germany",

}

RIS

TY - GEN

T1 - An Improved Genetic Algorithm for the Resource-Constrained Project Scheduling Problem

AU - Goncharov, Evgenii N.

N1 - The study was carried out within the framework of the state contract of the Sobolev Institute of Mathematics (project FWNF-2022-0019).

PY - 2022

Y1 - 2022

N2 - This paper presents an improved genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The schedules are constructed using a heuristic that builds active schedules based on priorities that takes into account the degree of criticality for the resources. The degree of resource’s criticality is derived from the solution of a relaxed problem with a constraint on accumulative resources. The computational results with instances from the PCPLIB library validate the effectiveness of the proposed algorithm. We have obtain some of the best average deviations of the solutions from the critical path value. The best known solutions have been improved for some instances from the PCPLIB.

AB - This paper presents an improved genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The schedules are constructed using a heuristic that builds active schedules based on priorities that takes into account the degree of criticality for the resources. The degree of resource’s criticality is derived from the solution of a relaxed problem with a constraint on accumulative resources. The computational results with instances from the PCPLIB library validate the effectiveness of the proposed algorithm. We have obtain some of the best average deviations of the solutions from the critical path value. The best known solutions have been improved for some instances from the PCPLIB.

UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85148040365&partnerID=40&md5=9e4d0764e3d678d27e2118c8dc3ec3d2

UR - https://www.mendeley.com/catalogue/dc740862-15e2-3572-b50c-61191c20a608/

U2 - 10.1007/978-3-031-22990-9_3

DO - 10.1007/978-3-031-22990-9_3

M3 - Conference contribution

SN - 978-3-031-22989-3

VL - 1739

SP - 35

EP - 47

BT - Communications in Computer and Information Science

PB - Springer Science and Business Media Deutschland GmbH

ER -

ID: 45615056