Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources

This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and number of required for each job rely on that does processing. Each j needed units (rjm) during its a m. These are limited, this made UPMR difficult problem to solve. In study, maximum completion jobs makespan must be minimized. Here, we proposed genetic algorithm solve because robustness success in solving many optimization problems. An enhancement GA (GGA) was also work. Generally, experiment involves tuning parameters GA. Additionally, an appropriate selection operators experimented. GGA is not used unspecified dynamic UPMR. Besides, utilization gave balance between exploration exploitation thus help search escape local optimum. Results show outperforms SGA, but it still didn't match results literature. On other hand, significantly all methods terms CPU time.