Learn about the basic concepts of data driven optimization and different methods, their advantages and application!

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this course is part of
Transforming Logistics with Analytics
Nienke Hofstra, Aicha Manuela-Martijn, Dennis Moeke, Sam Mosallaeipour & Eghe Osagie

About the course

In logistics, every day, many decisions have to be made. Questions underlying these decisions may be of a strategic nature, e.g.: "How much stock space is needed in our new warehouse?" while some are more tactical or operational, such as: "What is the optimal stock level of item X?" or "What is the optimal route for truck Y?". Data driven optimization can provide support in making these types of decisions in a more rational manner. The field of “data-driven optimization" is concerned with finding solutions for optimization problems by using quantitative models. Quantitative models can support decision making as they can help finding (relatively) accurate answers for complex real-life problems. In addition, as computing power is ever increasing, more complex quantitative decision models can be solved. At the same time, with the adoption of technological innovations the availability of digital data increases. This means that we can use (build and solve) more complex quantitative models that better reflect real-life systems. This course takes you through basic concepts of data driven optimization. Furthermore, the chapter discusses different types of optimization methods, considering their advantages, disadvantages and application. Several examples are used, including an explanation of how to build the underlying models in Python. 

Course subjects

Nienke Hofstra

Dr. Nienke Hofstra is senior researcher at HAN University of Applied Sciences and program coordinator Data Driven Logistics at the research group Logistics and Alliances. She facilitates learning communities where logistics and supply chain professionals from industry, education, and students develop and apply data analytics expertise and knowledge. Amongst others, she teaches in the minor Data Driven Decision Making in Business and is involved in the development of educational materials on data analytics.

Aicha Manuela-Martijn

Aicha Manuela-Martijn, MSc, currently serves as a lecturer and year coordinator for the Business IT & Management course at Rotterdam University of Applied Sciences. She also devoted nine years to the Logistic Management course at the same institution, where she developed simulation cases for the logistics field. Her teaching primarily focuses on mathematics, covering areas such as algebra, linear programming, optimization, data analysis, and simulation.

Dennis Moeke

Dennis Moeke is professor in Logistics and head of the Research Group Logistics and Alliances at the HAN University of Applied Sciences. He has a special interest in Healthcare- and Data-driven Logistics. In addition, he is also a member of the executive board of the National Centre of Expertise KennisDC Logistiek and represents the HAN in the executive committee of Logistics Valley.

Sam Mosallaeipour 

Dr. Sam M. Pour, who specializes in Data-Driven Decision Making and Optimization, is a senior lecturer at the International Business school of NHL Stenden University of Applied Sciences. His pedagogical approach is characterized by the integration of Design Based Education principles, fostering an environment where technology meets practical learning. His commitment to advancing educational methods is reflected in his leadership in developing and implementing cutting-edge academic programs.

Eghe Osagie

Eghe Osagie (PhD) is senior researcher at the research group Logistics and Alliances at HAN University. Her research interests include topics like data driven logistics, data-analytic competencies, human resource management and learning and development in organizations. She is the coordinator of the minor HR analytics and lectures various courses on research methodology and analytics. 

This publication is part of the project ‘small projects 2022 route transport and logistics' with project number NWA.1418.22.023 which is financed by the Dutch Research Council (NWO).