Time and place
- Trial Lecture at 10.15 - 11.00 in room A-1.025
- Defence starts at 12.15 in room A-1.025
Trial lecture title
"Decision Support in Healthcare Logistics: Comparative Strengths and Limitations of Optimization, Simulation, and Machine Learning"
Adjudication committee
- Professor Kenneth Sørensen, University of Antwerp, Belgium
- Professor Nadia Lahrichi, Polytechnique Montreal, Canada
- Professor Paulo Cesar Ribas, Molde University College
Chair of defence
Professor Harald Martin Hjelle, Molde University College
Supervisors
- Primary supervisor, Professor Sebastián Urrutia, Molde University College, Faculty of Logistics
- Secondary supervisor, Professor Lars Magnus Hvattum, Molde University College, Faculty of Logistics
Background
Home healthcare has become one of the fastest-growing parts of the Norwegian healthcare system. As more people receive treatment and assistance in their own homes, municipalities must provide high-quality services with limited personnel and financial resources. In many cases, planning remains largely manual despite the increasing complexity of daily operations. Existing planning tools often fail to account for important Norwegian conditions such as multi-day workforce planning, varying patient needs, and transportation that depends on fixed ferry schedules. These challenges motivated the development of new planning methods tailored to the realities of Norwegian home healthcare services.
Summary
This doctoral thesis investigates how advanced planning and optimization can improve home healthcare services in Norway. The research addresses key challenges faced by municipalities, including staff shortages, increasing demand for care, long travel distances, and geographically complex service areas.
Special attention is given to coastal regions where caregivers depend on ferry connections to reach patients. The thesis develops new decision-support methods that help coordinate staffing, patient visits, travel routes, and daily schedules. It also examines how healthcare services can remain reliable when unexpected delays occur. The results demonstrate how smarter planning can improve resource utilization while maintaining high-quality patient care.
The research introduces new optimization methods for some of the most complex planning problems in home healthcare. In addition to supporting more efficient use of personnel and public resources, the work contributes new approaches for integrating workforce planning, routing, scheduling, transportation constraints, and operational uncertainty within a single decision framework. The methods developed in the thesis can support future digital decision-support systems for healthcare providers and may also be applied to other service, transportation, and logistics sectors that face similar planning challenges.
Abdalrahman Algendi (35) comes from Syria. He holds a Bachelor's degree from 2019 in Business Administration in Informatics at Norwegian University of Life Sciences (NMBU). He holds a Master’s degree from 2021 in Logistic at Molde University College. His doctoral work has been financed by Molde University College – Specialized University in Logistics.