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Multi-objective project portfolio scheduling with multi-skilled and inter-project dependency based on NSGA-Ⅱ: Case study

Heng Zhang et al.

Journal of Industrial and Management Optimization2026https://doi.org/10.3934/jimo.2026054article
AJG 1ABDC B
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0.50

Abstract

This study addresses the complex resource-constrained scheduling problem for software project portfolios, where baseline and customized projects with inter-dependencies are developed in parallel. We formulated a nonlinear integer programming model that simultaneously minimizes total duration, optimizes software quality, and balances engineer workload, explicitly incorporating multi-skilled human resources and cross-project dependencies. To solve this problem, we developed an improved Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) algorithm featuring localized coding, heuristic population initialization, and Pareto-based local search. A real-world case study from an AI-powered voice technology enterprise demonstrates the method's efficacy: the final population significantly outperforms the initial one, and our algorithm surpasses classic NSGA-Ⅱ, Ant Lion Optimization (ALO), and Simulated Annealing (SA) in convergence and diversity. Crucially, our approach achieves remarkable improvements—reducing duration by 16%, enhancing quality by 21.6%, and improving workload balance by 94.4% compared to skill-homogenized scenarios. Similarly, inter-project dependency-aware scheduling improves duration, quality, and workload balance by 5.2%, 3.9%, and 53.9%, respectively, compared to inter-project dependency-unaware scenarios. Managers can utilize the decoded Pareto optimal solutions to formulate detailed allocation plans, thereby achieving contextually optimized resource management.

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https://doi.org/https://doi.org/10.3934/jimo.2026054

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@article{heng2026,
  title        = {{Multi-objective project portfolio scheduling with multi-skilled and inter-project dependency based on NSGA-Ⅱ: Case study}},
  author       = {Heng Zhang et al.},
  journal      = {Journal of Industrial and Management Optimization},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3934/jimo.2026054},
}

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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R · text relevance †0.50 × 0.4 = 0.20

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