Multi-Objective Optimization of Laboratory Technicians Scheduling using Binary Genetic Algorithm

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.