Optimizing Task Assignment in Project Management Using Recommendation Algorithms and Improving Team Efficiency
| dc.contributor.author | Turysbay D. | |
| dc.date.accessioned | 2026-11-12T05:00:36Z | |
| dc.date.available | 2026-11-12T05:00:36Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | 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. | |
| dc.identifier.citation | Turysbay D / Optimizing Task Assignment in Project Management Using Recommendation Algorithms and Improving Team Efficiency / SDU University / Department of Computer Sciences | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/2709 | |
| dc.language.iso | en | |
| dc.publisher | SDU University | |
| dc.subject | project management | |
| dc.subject | task assignment | |
| dc.subject | learning-to-rank | |
| dc.subject | LightGBM | |
| dc.subject | LambdaRank | |
| dc.title | Optimizing Task Assignment in Project Management Using Recommendation Algorithms and Improving Team Efficiency | |
| dc.type | Thesis |