Key details
Dr Samuel C Zelibe
Lecturer in Mathematics (Data Science)
Samuel Zelibe is a lecturer in Mathematics (Data Science) at the University of Greenwich's School of Computing and Mathematical Sciences. His research focuses on areas like operational research, mathematical optimisation, probabilistic modelling, and lifetime processes, with real-world applications in supply chains, logistics, and healthcare. He is particularly interested in integrated multi-echelon supply chain systems with response-time constraints, aiming to develop mathematical models that improve efficiency and resilience in complex logistics networks. His work has contributed to more cost-effective decision-making , better resource management and improvement in service levels. Currently, he is exploring the connections between mathematical optimisation and data science.
Beyond research, Samuel is an experienced educator dedicated to creating an engaging and inclusive learning environment. He has designed and taught courses on optimisation, operational research, and data-driven decision-making, incorporating innovative teaching techniques to help students succeed. He is passionate about bridging the gap between theoretical mathematics and practical applications, ensuring his work has a meaningful impact across different industries. He also actively promotes interdisciplinary collaboration and supports students in achieving their academic and professional goals.
Responsibilities within the university
Module Instructor
Advanced Mathematics for Computer Science
Operational Research: Linear Programming
Vector Calculus
Recognition
Member of the Operational Research Society
Member of Association for Learning Development in Higher Education
Research / Scholarly interests
Samuel Zelibe's research focuses on solving complex decision-making problems using mathematical modelling, operations research, and optimisation. He develops efficient algorithms and frameworks to improve logistics, supply chain management and inventory systems. His work includes multi-echelon inventory models, probabilistic location-inventory problems, and service parts logistics.
Beyond optimisation, he explores applied probability and statistical distributions, particularly in lifetime data analysis and risk assessment. By combining analytical and computational techniques, he aims to enhance decision-support systems for industries that rely on effective resource management and demand forecasting.
Recently, Samuel has expanded his research into data science and sustainability, exploring how optimisation, probabilistic modelling, and artificial intelligence can drive sustainable decision-making. He is particularly interested in using data-driven approaches to improve predictive analytics, enhance supply chain resilience and develop environmentally responsible solutions.
Passionate about bridging the gap between theory and real-world applications, he collaborates with industry partners to create practical solutions that improve business operations and logistics planning. His work is driven by the goal of making decision-making more efficient, data driven, and sustainable.