ArticleScientific reports2025
Multi skill project scheduling optimization based on quality transmission and rework network reconstruction.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Quality deficiencies are widely acknowledged as a primary driver of project rework, with personnel skill levels serving as a critical determinant of activity quality. This study presents a scheduling model that integrates quality transmission mechanisms and dynamic rework subnet reconstruction within the Multi-Skill Resource-Constrained Project Scheduling Problem (MSRCPSP) framework. The proposed model aims to optimize project duration while mitigating rework risks. To address the computational complexity of the model, an Improved Gazelle Optimization Algorithm (GOAIP) was developed, incorporating dynamic operators, shuffle crossover, and Gaussian mutation strategies to balance global and local optimization. Experimental validation across diverse case scales demonstrates that the proposed model and algorithm outperform mainstream optimization techniques in solution accuracy and convergence efficiency, highlighting their robust applicability and practical significance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.