A decomposition-based heuristic procedure for the Medical Student Scheduling problem

In this paper, we consider a real-life medical student scheduling problem in order to ensure students are able to complete the relevant training program to acquire the postulated medical proficiency. A training program includes mandatory and elective disciplines that students are able to select based on their interests and availability. These internship positions are offered by local hospitals that specify minimum and maximum staffing requirements. The curriculum manager tries to assign students to particular disciplines and hospitals while considering the objectives and the large number of requirements of different stakeholders, i.e. the educational requirements set by the medical school, the staffing requirements set by the involved hospitals and the student characteristics. We propose a heuristic solution methodology composed of a constructive heuristic and two local search heuristics to improve the initial solution. These heuristics embody different complementary neighbourhood structures derived based on the decomposition of the problem in order to find high-quality solutions very efficiently. In order to show the stable performance of the proposed solution methodology, we conducted computational experiments on a comprehensive synthetic dataset of smaller-sized instances and large-scale real-life instances. Results demonstrate that our approach can produce (near-)optimal solutions in a very short timespan. A comparison is made with the real-life approach, demonstrating significant improvements and the contribution to real-life decision-making.