Högskolan i Skövde

In scheduling, not all processes can be scheduled equally and may present their own unique set of constraints. Solution approaches include meta-heuristics and exact methods. 

Two different approaches were chosen to generate schedules with constraints and compare their performance when implemented for a scheduling activity; Constraint Programming and the Genetic Algorithm. Quasi-experiments were conducted to evaluate the execution time and accuracy score of each solution using a dataset of 50 jobs. The baseline includes a completed scheduling of the jobs. 

The results indicate that the Genetic Algorithm solution offers the best results in terms of execution time and accuracy, exhibiting results comparable to the baseline. The Constraint Programming solution failed to find any optimal results, demonstrating lower accuracy compared to the Genetic Algorithm and the baseline. 

With the foundation laid by this study, further work may improve each model to a more usable degree.