An heuristic genetic algorithm for strategic university tuition planning and workload balancing. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Immonen, Eero; Putkonen, Ari
• Is Part Of: International journal of management science and engineering management. Volume 13:Issue 2(2018)
• Page Start: 118
• Page End: 128
• Publication Date: 2018-04-03
• Subjects: Tuition planning -- scheduling -- workload balancing -- genetic algorithm; CO2 Mathematical Methods -- C61 Optimization Techniques -- Programming Models -- Dynamic Analysis -- C44 Operations Research -- Statistical Decision Theory -- D81 Criteria for Decision-Making under Risk and Uncertainty -- M54 Labor Management; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2017.1316219
• Languages: English
• ISSNs: 1750-9661

Abstract

Abstract: The typical tuition planning challenge that universities face is to optimize students' and teachers' timetables on a weekly basis. While crucial for operational arrangement of tuition, such short-term planning is often done in relative isolation from longer-term strategic considerations, such as organizational competence development. This article presents an heuristic method, based on genetic algorithms, for long-term strategic planning of tuition for multi-year university degree programmes, at the semester level. The proposed method attempts to produce such a tuition schedule that satisfies given pre-requisite knowledge constraints, while honouring teacher-specific preferences, such that the workload across the teachers is as uniform as possible. The proposed method accommodates many real-world constraints, and can be applied to several distinct student groups advancing in parallel. As an application, the proposed method is used for optimizing the tuition schedule of a mechanical engineering degree programme at a Finnish university.