A Multi-objective Genetic Algorithm for Optimizing the Nurse scheduling Problem
A. Wibowo1, Y. Lianawati2
1A. Wibowo, Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
2Y. Lianawati, Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
Manuscript received on 08 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 5409-5414 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6204098319/2019©BEIESP | DOI: 10.35940/ijrte.C6204.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper explains the use of Genetic Algorithms in overcoming nurses scheduling problems in St. General Hospital Elisabeth. In this paper, Multi-Objective is used to solve problems with mathematical models and is applied to overcome nurse scheduling problems. Health is one of the essential things in human life. There are some departments in the hospital which serve the patients in the hospital, and one of them is the nursing department. The nurses in this department serve the patients every time, but they have limited time as the working hour must not be more than schedule 8 hours. So, there will be a problem in this aspect, especially the working for each nurse because the nurses must be stand by to serve the patients every time. The scheduled operation will be complicated, so we propose a solution by Genetic Algorithm method. The scheduling making by applying Genetic Algorithm method will give more optimal and faster output comparing with a current or manual process.
Index Terms: Multi-Objective, Optimizing, Nurse, Nurse Scheduling, Scheduling Problem, Genetic Algorithm Stability, Rotor Angle, PMU, Sensitivity Analysis
Scope of the Article: Algorithm Engineering