Multi-Objective Optimization of Laboratory Technicians Scheduling using Binary Genetic Algorithm
Antoni Wibowo1, Filbert Ivander2
1Antoni Wibowo*, Computer Science Department, Bina Nusantara University, BINUS Graduate Program-Master in Computer Science, Jakarta, Indonesia.
2Filbert Ivander, Computer Science Department, Bina Nusantara University, BINUS Graduate Program-Master in Computer Science, Jakarta, Indonesia.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 1349-1354 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7204118419/2019©BEIESP | DOI: 10.35940/ijrte.D7204.118419
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© 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: A laboratory needs at least one technician to maintain the laboratory’s activity every day. The technicians should prevent any technical interference in a daily learning activity. The technicians must be placed in a different lab the next day to check the work of the technician previously. This scheduling model has assigned 4 technicians into 3 laboratories in a month. We proposed a mathematical model for multi-objective optimization of laboratory technicians scheduling since it has many objective functions such as avoid collisions, workload balancing of technicians, and works distribution in the laboratories. We presented a Binary Genetic Algorithm to find the best technicians scheduling that can be used to support daily operations. As a result, we noticed that Binary GA could effectively be used in daily operational since the computing time was relatively short in finding the best laboratory technicians scheduling. From ten times of testing, the best solution needs 285.406s to calculate with the minimum function value is 2.
Keywords: Genetic Algorithm, Laboratory Technicians Scheduling, Binary GA, Multi-objective.
Scope of the Article: Parallel and Distributed Algorithms.