Parallel Scheduling using Genetic Algorithm and Knowledge Based Approach - Gurusinga, Mentari Oktaria; Ginting, Rosnani; Sinulingga, Sukaria (TALENTA Publisher, 2025)
Production scheduling are very important considering the complexity of the production system. This study aims to solve parallel machine scheduling to get the best job sequence and minimize lateness. Genetic algorithm is optimization algorithms by implementing evolution process and eliminating bad solutions. Knowledge based approach (KBA) solve problems by creating a computing system to imitates human intelligent behavior. Genetic algorithm and KBA are combined with the earliest due date (EDD) rule to produce an inference engine to build more adaptive population initialization. The results of the proposed scheduling show that the rules successfully guide the search process more adaptively. The genetic operation increasing the fitness value when the job is overload or underload. When the job is underload fitness increases by 3.56%, there is no lateness and load capacity ratio (LCR) increase by 4.67%. When the overload fitness increases by 1%, lateness decreases by 4.57%, and LCR decreases by 7.56%. The increase of fitness value shows better results of the proposed job sequence with minimum lateness. The implementation of integration genetic algorithms and KBA using VB.Net language requires a reasonable computing time, which is an average of 32 seconds when running.