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Solving a multiobjective professional timetabling problem using evolutionary algorithms at Mandarine Academy
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AI
DOI：10.1111/itor.13276.png)
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Abstract
En 中文
In this paper, we propose a method for solving a real-world timetabling problem at Mandarine Academy. The primary motivation for this work is to provide an automated professional course scheduling tool to replace the time-consuming task of manually creating timetables that are constantly incorrect. Following a review of both scientific literature and company requirements, a mathematical model of the problem is provided, which includes 18 constraints (hard/soft) and five objectives, two of which are competing. We test a handful of multi-objective evolutionary algorithms (MOEA's) starting with the non-dominated sorting genetic algorithm (NSGA II and NSGA III), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the indicator-based evolutionary algorithm and finally the strength Pareto evolutionary algorithm . Two custom genetic operators (mutation and crossover) are proposed and compared to conventional operators (PMX and swap mutation). To obtain elite configurations, a tuning phase involving all of the aforementioned algorithms is carried out. Experiments were divided by problem size, with three to five objectives tested. Experiments include the use of real-world data from the company's catalog. This dataset was made available to the scientific community to serve as a testing ground for professional course scheduling, an underexploited field of scheduling. We discuss findings, including a comparison of each algorithm's performance using various metrics, as well as convergence graphs and population evolution.
Keywords:
evolutionary algorithms
multi-objective optimisation
timetabling
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