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Jiayuan Li

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EAAI Journal 2026 Journal Article

Coevolutionary software multi-project scheduling with matrix management and online skill training

Xiaoning Shen
Jiayuan Li
Liyan Song
Chengbin Yao

To address the challenges of concurrent multiple-project scheduling in software development, this paper introduces a matrix management framework and incorporates practical factors such as online skill training and inter-project priority relationships. Specifically, this paper establishes a software multi-project scheduling model, and proposes an algorithm called Multi-Population Cooperative Artificial Bee Colony (MPCABC) to solve it. The MPCABC algorithm initializes subpopulations based on functional groups to meet task skill requirements and employs a dynamic grouping strategy guided by sub-objective rankings across projects. It also integrates an interaction mechanism between subpopulations and a local search operator that prioritizes project-specific preferences for duration and cost. Experimental validations on three real-world software multi-project scheduling instances and nine synthetic instances with increasing sizes demonstrate that MPCABC outperforms six state-of-the-art algorithms in optimizing employee allocation across projects, which demonstrates that the proposed algorithm can enhance resource utilization and scheduling efficiency in complex software development environments. This paper not only contributes to the field of evolutionary computation but also makes a significant contribution to the field of Artificial Intelligence by providing a novel solution-seeking method for software project scheduling.

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AAAI Conference 2026 Conference Paper

Hierarchical Reinforcement Learning with Topology-Aware Exploration Framework for Multi-path Commodity Flow Problem

Jingchen Jiang
Xuan Zhou
Jiayuan Li
Geng Han
Xiang Shi
Fang Deng

The multi-path commodity flow problem (MPCFP) is crucial for ensuring reliable and high-speed data transmission in communication networks. However, existing studies that employ pre-generated routing paths neglect real-time load state and the coupling among decisions, thus hindering the achievement of high-quality solutions. To overcome this, we propose Hierarchical Reinforcement Learning with Topology-Aware Exploration (HRL-TAE), which is the first fully end-to-end framework that dynamically produces high-quality solutions based on real-time network states. HRL-TAE integrates an exploration mechanism and utilizes the State Transition Guiding List (STGL) to guide state transitions, thereby transforming topology exploration into a Markov decision process. Guided by STGL, two closely coupled layers in HRL-TAE, that is, the path construct layer and the ratio allocate layer, construct multiple subpaths for each flow and allocate traffic ratios among them. Subsequently, adaptive constraint-driven masks exclude infeasible actions during decision making, thereby guaranteeing that all constraints are satisfied. We also adopt a tailored training approach to obtain accurate gradient estimates and improve training efficiency. Simulations and real-world experiments demonstrate that HRL-TAE achieves superior performance.

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