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New method enhances AI-driven project scheduling heuristics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method using surrogate-assisted genetic programming (GP) to improve heuristic rules for dynamic multi-mode project scheduling. The study focuses on phenotypic characterization (PC) to enhance fitness estimation within a limited budget of full simulations. Three encoding schemes—priority-value, rank, and binary—were tested, with binary encoding combined with Euclidean distance showing the most effective and robust guidance for surrogate-assisted GP. AI

IMPACT This research could lead to more efficient and effective AI-driven solutions for complex project scheduling problems.

RANK_REASON The cluster contains an academic paper detailing a new research methodology.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method enhances AI-driven project scheduling heuristics

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yuan Tian, Yi Mei, Mengjie Zhang ·

    Surrogate-Assisted Genetic Programming with Phenotypic Characterisation in Dynamic Multi-Mode Project Scheduling

    arXiv:2609.14418v1 Announce Type: cross Abstract: Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple execution modes, and uncertain activity durations. Genetic programming (GP) can aut…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Mengjie Zhang ·

    Surrogate-Assisted Genetic Programming with Phenotypic Characterisation in Dynamic Multi-Mode Project Scheduling

    Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple execution modes, and uncertain activity durations. Genetic programming (GP) can automatically evolve heuristic rules for such problem…

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