A decomposition-based heuristic procedure for the Medical Student Scheduling problem
Autor: | Babak Akbarzadeh, Broos Maenhout |
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Rok vydání: | 2021 |
Předmět: |
050210 logistics & transportation
021103 operations research Information Systems and Management General Computer Science Job shop scheduling Operations research Computer science Heuristic business.industry 05 social sciences 0211 other engineering and technologies Staffing Neighbourhood (graph theory) 02 engineering and technology Management Science and Operations Research Industrial and Manufacturing Engineering Modeling and Simulation Internship 0502 economics and business Local search (optimization) Heuristics Heuristic procedure business Curriculum |
Zdroj: | European Journal of Operational Research. 288:63-79 |
ISSN: | 0377-2217 |
DOI: | 10.1016/j.ejor.2020.05.042 |
Popis: | 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. |
Databáze: | OpenAIRE |
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