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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Shrnutí: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.
ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2025.3568826