Machine scheduling in the production planning activities of enterprises is an activity that is frequently repeated in short periods and is very important in terms of efficient use of resources. It is necessary to solve a parallel machine scheduling problem regardless of the workshop environment in workshops where more than one machine that can do the same job to increase the capacity and solve the bottleneck problems. Especially in non-identical parallel machines, it is desired to determine which machine will process the job depending on many factors. A general software cannot respond to such situations. In addition, it is important for planners to prepare charts that take into account sequence dependent setup times in parallel machine environments such as plastic injection and oven scheduling problems. In this context, the study is focused on designing a decision support system for non-identical parallel machine scheduling problems with sequence dependent setup times. By using the decision support system, the decision maker can obtain the schedule for the jobs need to be scheduled for the relevant period and purpose according to situation of workshop. The system makes it possible to find and compare schedules for different objective functions such as minimizing makespan and minimizing the number of tardy jobs. On the basis of the model of the decision support system, meta-heuristic algorithms that can produce a solution to large scale real-life scheduling problems in a short time have been used.