Journal
ENTERPRISE INFORMATION SYSTEMS
Volume -, Issue -, Pages -Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/17517575.2023.2188124
Keywords
Discrete event simulation; Genetic algorithms; Health care; Optimization; Scheduling; Multi-objective optimisation
Categories
Ask authors/readers for more resources
This paper presents a new scheduling problem for patient visits that aims to minimize patient waiting time and travel time. A novel encoding method for Genetic Algorithms (GA) is proposed, which is found to reduce optimization iterations by 17% compared to conventional methods. The GA can significantly decrease waiting time by up to 58.2% and travel time by up to 89.3% for specific examples. The main contributions of this work are the novel scheduling problem and the proposed encoding method.
This paper proposes a new scheduling problem for patient visits with two objectives: minimizing patient waiting time and travel time. It also presents a novel encoding method for Genetic Algorithms (GA) that is well-suited for this problem. Experiments demonstrate that the proposed encoding method reduces optimization iterations by 17% compared to conventional methods, and the GA can decrease waiting time by up to 58.2% and travel time by up to 89.3% for specific examples. The novel scheduling problem and the encoding method are two main contributions of this work.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available