Parallel Batch Processor Machine Scheduling Using Multi-Population SPEA-II Algorithm

The increasing competition in the industrial sector requires companies to provide more optimal services, particularly in terms of production speed by increasing machine utilization. This can be achieved by implementing parallel batch scheduling. In conventional scheduling, a machine is only able to handle one job at a time, whereas in parallel batch scheduling, a machine can process a group of jobs simultaneously based on its capacity. Flexible Job Shop with parallel batch processor has been studied by several researchers, but the objective function has generally been limited to minimizing makespan. This research aims to minimize multi objective function that are energy consumption and makespan by using the Modified Strength Pareto Evolutionary Algorithm-II (SPEA2). Modifications of the algorithm are conducted by applying multi-population that run in parallel so that the optimization process can avoid local optima. The results of the research show that Multi-Population SPEA2 provides more optimal results compared to classical SPEA2 and benchmarks from previous research.