Jingyuan Zheng

Master of Science in Engineering · Industrial Engineering and Logistics Management

English-medium programme · Relevant coursework: Data-driven Optimization, Operational Research, Machine Learning and Applications, Physical Internet

Bachelor of Management · Logistics Management

GPA: 3.69/4.0 (Rank: 2/35) · Relevant coursework: Python, Database Technology and Application, Operational Research, Forecasting Methods, Logistics System Optimization, Modelling and Simulation of Logistics Systems

MSc Dissertation · The University of Hong Kong · Supervisor: Prof. Yao Cheng

• Developed a reliability-driven decision-support model for PV microinverter spare parts, linking stochastic failure behaviour, replenishment lead time, downtime loss, lifecycle cost, and carbon-emission impact.
• Applied CTMC-based reliability modelling, NSGA-II multi-objective optimisation, and Monte Carlo simulation to evaluate inventory policies under uncertain failures and supply conditions.
• Conducted sensitivity analysis and translated model outputs into practical recommendations for inventory levels, lead-time control, reliability improvement, and system resilience.

Undergraduate Dissertation · Fuzhou University · Supervisor: Prof. Haidong Yang

• Built a system dynamics model to simulate operational risk evolution in an international maritime supply-chain system using a three-tier risk indicator framework and questionnaire-based data.
• Applied questionnaire reliability testing and factor analysis to process survey responses, determine indicator weights, and convert qualitative risk factors into model inputs.
• Generated scenario-based recommendations for supplier reliability, logistics coordination, information-flow control, financial health, and operational resilience.

Academic Project · The University of Hong Kong · Directed by Dr. Shujia Qin

• Developed Python-based training and inference workflows for binary classification of 8,192-point time-series signals under limited domain information and a small labelled dataset.
• Extracted and compared time-domain, frequency-domain, and power spectral density features to guide feature engineering and model design.
• Implemented and compared a stacking ensemble, an RBF-kernel SVM, and a neural-network model with Bayesian hyperparameter tuning, documenting performance, limitations, and reproducibility.

Academic Project · The University of Hong Kong · Directed by Prof. Junwei Wang

• Developed a network-flow-based evacuation optimisation model for disaster-management scenarios, balancing evacuation efficiency with fairness in shelter allocation.
• Evaluated shelter utilisation, congestion, distribution balance, and resource-allocation performance using simulation-based results.
• Proposed improvements for bottleneck reduction, fairer flow distribution, and more resilient emergency planning.

Undergraduate Research Project · Fuzhou University · Directed by Prof. Qin Zhang

• Supported a questionnaire-based research project by applying factor analysis and AHP to construct and evaluate an indicator system for historical-community development.
• Analysed development conditions and improvement priorities, translating quantitative findings into policy-oriented recommendations.

Research Methods Project · Fuzhou University · Directed by Prof. Quan Cheng and Yong Hu

• Designed and processed questionnaire data using SPSS, Likert-scale variables, and ordinal regression to analyse factors influencing postgraduate study intentions.
• Converted survey responses into structured numerical variables and interpreted model results to support evidence-based recommendations for higher-education planning.

• Maintained shipment-status records by checking vessel-space allocation, ETD/ETA changes, booking updates, documentation issues, and cargo-information discrepancies against shipping-company and partner updates.
• Maintained structured communication with customers, customs brokers, shipping companies, and partner firms, supporting schedule visibility and exception follow-up.

• Used Python, SQL, and inventory data analysis to review historical product flows, identify demand patterns, and support material-requirements planning.
• Maintained structured inventory records and proposed stock adjustments that reduced excess stock for two seasonal products by approximately 25%.

• Applied data analysis and customer segmentation to build structured customer profiles and compile analytical reports supporting targeted sales follow-up.