Browsing by Author "Turysbay D."

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    Optimizing Task Assignment in Project Management Using Recommendation Algorithms and Improving Team Efficiency
    (SDU University, 2026) Turysbay D.
    Allocating software development tasks to the right team member is one of the most consequential decisions a project manager makes daily. This thesis presents the design, implementation, and empirical evaluation of a full-stack project management web application augmented by a machine learning recommendation engine that ranks developers for a given task. The problem is modelled as learning-to-rank: given a task description (skills, position, grade, deadline, effort, priority) and the team’s current state (skills, workload, performance history), the system produces a ranked list of candidate developers with interpretable explanations. Four strategies are compared under a single protocol on a held-out test set: (i) a transparent heuristic baseline combining seven priority-weighted component scores; (ii) an embedding approach using sentence-transformer cosine similarity; (iii) a LightGBM LambdaRank learning-to-rank model over thirty engineered features; and (iv) an LLM-augmented pipeline using a local Mistral model via Ollama for skill extraction and fit scoring. Seven additional classical algorithms provide a wider comparison. The system is implemented as a containerised microarchitecture: a FastAPI/PostgreSQL backend, a React (Vite) frontend with roleaware navigation, a sidecar Ollama service, and a GitLab CI pipeline deploying to Railway. Evaluation uses a synthetic dataset of 1,000 (task, developer) pairs over one hundred tasks and twenty developers, with a task-grouped train–test split, scored by NDCG@K, MAP, MRR and Precision@1. The tuned baseline is strong (NDCG@5= 0.847, MAP= 0.794); embeddings give a small consistent gain (MAP= 0.799); LambdaRank slightly underperforms on synthetic data (MAP= 0.717); and the LLM pipeline matches embeddings while adding feature-enrichment and explanation value.