A Decomposition-Based Hybrid Algorithm for Large-Scale Project Portfolio Selection and Scheduling With Reaction to Changing Environments

The project portfolio selection and scheduling problem (PPSSP) aims to select and schedule a set of projects, known as a portfolio, to maximize their benefits while adhering to various constraints. However, addressing PPSSP in a reasonable time becomes increasingly challenging with a growing number...

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Vydáno v:	IEEE transactions on engineering management Ročník 72; s. 2409 - 2423
Hlavní autoři:	Liu, Jing, Sarker, Ruhul, Essam, Daryl, Elsayed, Saber
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	IEEE 2025
Témata:	Divide-and-conquer dynamic Dynamic scheduling genetic algorithm (GA) Genetic algorithms large-scale optimization Maintenance engineering Mathematical models Metaheuristics Optimization Portfolios Project management project portfolio selection and scheduling Schedules Stochastic processes
ISSN:	0018-9391, 1558-0040
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Abstract	The project portfolio selection and scheduling problem (PPSSP) aims to select and schedule a set of projects, known as a portfolio, to maximize their benefits while adhering to various constraints. However, addressing PPSSP in a reasonable time becomes increasingly challenging with a growing number of projects, especially in changing environments. This article proposes a decomposition-based hybrid algorithm with evolutionary algorithm-based global search and exact solver-based local search to optimize large-scale PPSSP, and integrates a reactive approach to reoptimize PPSSP when unexpected changes occur. Specifically, a heuristic solution repair method is designed to accelerate the evolutionary global search, which is then expanded to repair the baseline schedule when dealing with changes. A problem-specific grouping method and a constrained subproblem construction approach are developed for conducting the cooperative local search using the exact solver. Besides, a reactive approach is crafted to deal with changing circumstances by updating the problem structure, repairing the prescheduled solution, and reoptimizing the solutions. Experiments conducted on 28 large-scale PPSSPs and 28 changed PPSSPs have validated the superiority of the proposed hybrid algorithm in achieving competitive results in a shorter time, saving up to 57% computational time to achieve high-quality solutions when compared to the exact solver Gurobi.
AbstractList	The project portfolio selection and scheduling problem (PPSSP) aims to select and schedule a set of projects, known as a portfolio, to maximize their benefits while adhering to various constraints. However, addressing PPSSP in a reasonable time becomes increasingly challenging with a growing number of projects, especially in changing environments. This article proposes a decomposition-based hybrid algorithm with evolutionary algorithm-based global search and exact solver-based local search to optimize large-scale PPSSP, and integrates a reactive approach to reoptimize PPSSP when unexpected changes occur. Specifically, a heuristic solution repair method is designed to accelerate the evolutionary global search, which is then expanded to repair the baseline schedule when dealing with changes. A problem-specific grouping method and a constrained subproblem construction approach are developed for conducting the cooperative local search using the exact solver. Besides, a reactive approach is crafted to deal with changing circumstances by updating the problem structure, repairing the prescheduled solution, and reoptimizing the solutions. Experiments conducted on 28 large-scale PPSSPs and 28 changed PPSSPs have validated the superiority of the proposed hybrid algorithm in achieving competitive results in a shorter time, saving up to 57% computational time to achieve high-quality solutions when compared to the exact solver Gurobi.
Author	Essam, Daryl Elsayed, Saber Liu, Jing Sarker, Ruhul
Author_xml	– sequence: 1 givenname: Jing orcidid: 0000-0003-3600-4647 surname: Liu fullname: Liu, Jing email: jing.liu5@unsw.edu.au organization: School of Systems and Computing, University of New South Wales (UNSW), Canberra, ACT, Australia – sequence: 2 givenname: Ruhul orcidid: 0000-0002-1363-2774 surname: Sarker fullname: Sarker, Ruhul email: r.sarker@unsw.edu.au organization: School of Systems and Computing, University of New South Wales (UNSW), Canberra, ACT, Australia – sequence: 3 givenname: Daryl orcidid: 0000-0002-6923-7079 surname: Essam fullname: Essam, Daryl email: d.essam@unsw.edu.au organization: School of Systems and Computing, University of New South Wales (UNSW), Canberra, ACT, Australia – sequence: 4 givenname: Saber orcidid: 0000-0003-0836-6122 surname: Elsayed fullname: Elsayed, Saber email: s.elsayed@unsw.edu.au organization: Department of Computer Science, University of Sharjah, Sharjah, UAE
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SubjectTerms	Divide-and-conquer dynamic Dynamic scheduling genetic algorithm (GA) Genetic algorithms large-scale optimization Maintenance engineering Mathematical models Metaheuristics Optimization Portfolios Project management project portfolio selection and scheduling Schedules Stochastic processes
Title	A Decomposition-Based Hybrid Algorithm for Large-Scale Project Portfolio Selection and Scheduling With Reaction to Changing Environments
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