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Using multi-objective metaheuristics to solve the software project scheduling problem

Authors:

Francisco Chicano,

Francisco Luna,

Antonio J. Nebro,

Enrique AlbaAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1915 - 1922

https://doi.org/10.1145/2001576.2001833

Published: 12 July 2011 Publication History

Abstract

The Software Project Scheduling (SPS) problem relates to the decision of who does what during a software project lifetime. This problem has a capital importance for software companies. In the SPS problem, the total budget and human resources involved in software development must be optimally managed in order to end up with a successful project. Companies are mainly concerned with reducing both the duration and the cost of the projects, and these two goals are in conflict with each other. A multi-objective approach is therefore the natural way of facing the SPS problem. In this paper, a number of multi-objective metaheuristics have been used to address this problem. They have been thoroughly compared over a set of 36 publicly available instances that cover a wide range of different scenarios. The resulting project schedulings of the algorithms have been analyzed in order to show their relevant features. The algorithms used in this paper and the analysis performed may assist project managers in the difficult task of deciding who does what in a software project.

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Cited By

Ou CLi YChen CWu CTsai YYan ZChang C(2023)Quantum-Inspired Optimization for Task Scheduling in Software Development Projects2023 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE57702.2023.10276(348-349)Online publication date: 17-Sep-2023
https://doi.org/10.1109/QCE57702.2023.10276
Nigar NShahzad MIslam SOki OLukose J(2023)A Novel Multi-Objective Evolutionary Algorithm to Address Turnover in the Software Project Scheduling Problem Based on Best Fit Skills CriterionIEEE Access10.1109/ACCESS.2023.330683811(89742-89756)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3306838
Zhang JShen XYao C(2023)Evolutionary Algorithm for Software Project Scheduling Considering Team RelationshipsIEEE Access10.1109/ACCESS.2023.327016311(43690-43706)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3270163
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Using multi-objective metaheuristics to solve the software project scheduling problem

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Published In

cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Ou CLi YChen CWu CTsai YYan ZChang C(2023)Quantum-Inspired Optimization for Task Scheduling in Software Development Projects2023 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE57702.2023.10276(348-349)Online publication date: 17-Sep-2023
https://doi.org/10.1109/QCE57702.2023.10276
Nigar NShahzad MIslam SOki OLukose J(2023)A Novel Multi-Objective Evolutionary Algorithm to Address Turnover in the Software Project Scheduling Problem Based on Best Fit Skills CriterionIEEE Access10.1109/ACCESS.2023.330683811(89742-89756)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3306838
Zhang JShen XYao C(2023)Evolutionary Algorithm for Software Project Scheduling Considering Team RelationshipsIEEE Access10.1109/ACCESS.2023.327016311(43690-43706)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3270163
Nigar NShahzad MIslam SOki OLukose J(2023)Multi-Objective Dynamic Software Project Scheduling: A Novel Approach to Handle Employee’s AdditionIEEE Access10.1109/ACCESS.2023.326571611(39792-39806)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3265716
Singh SSambhav S(2023)Application of Artificial Intelligence in Software Development Life Cycle: A Systematic Mapping StudyMicro-Electronics and Telecommunication Engineering10.1007/978-981-19-9512-5_60(655-665)Online publication date: 2-Jun-2023
https://doi.org/10.1007/978-981-19-9512-5_60
Castañeda PMauricio D(2022)New Factors Affecting Productivity of the Software FactoryResearch Anthology on Agile Software, Software Development, and Testing10.4018/978-1-6684-3702-5.ch093(1951-1979)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3702-5.ch093
Li MChen TYao X(2022)How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological GuidanceIEEE Transactions on Software Engineering10.1109/TSE.2020.303610848:5(1771-1799)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1109/TSE.2020.3036108
Nigar NShahzad MIslam SKumar SJaleel A(2022)Modeling Human Resource Experience Evolution for Multiobjective Project Scheduling in Large Scale Software ProjectsIEEE Access10.1109/ACCESS.2022.316959610(44677-44690)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3169596
Kosztyán ZBogdány ESzalkai IKurbucz M(2021)Impacts of synergies on software project schedulingAnnals of Operations Research10.1007/s10479-021-04467-5312:2(883-908)Online publication date: 21-Dec-2021
https://doi.org/10.1007/s10479-021-04467-5
Castañeda PMauricio D(2020)New Factors Affecting Productivity of the Software FactoryInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.202001010113:1(1-26)Online publication date: Jan-2020
https://doi.org/10.4018/IJITSA.2020010101
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