Driven by public opinion, increased health expenditures, an ageing population, and long waiting lists, a flood of changes in the healthcare system has been set in motion to try to make health care more efficient. This implies a wide variety of complex strategic decisions that contribute to the simultaneous optimisation of quality of care, costs and patient lead-time.
The nature of health care work does not allow copying success stories from industry, where logistical optimisation has a long history. Healthcare processes and supply chains show considerable differences, such as the high degree of uncertainty, the medical autonomy of clinicians, and the fact that patients cannot be treated as products. These projects aim to develop mathematical models and techniques to analyse and support the design of the optimal logistics structure. The output consists of decision support tools, (mathematical) models and techniques that capture the inherent complexity of healthcare processes, and aid users to analyse the relation between system configurations, optimized and robust system planning and control, and system performance.