In the 21 century, manufacturing companies face increasingly frequent and unpredictable market changes driven by global competition, including the rapid introduction of new products and constantly varying product demand. To remain competitive, companies must design manufacturing systems that not only produce high-quality products at low costs, but also which allow for rapid response to market changes and consumer needs. Complex Discrete Event Simulation Systems like Flexible Manufacturing systems necessitate the complete utilization of available resources to optimize their productivity. One important objective of scheduling in FMS systems is to increase resource utilization and reduce idle time. In this paper, scheduling is modeled as a Multi Objective Optimization Problem, with primary objective for picking a schedule which has very less Combined Objective Function (COF) value. Most of the optimization functions proposed in the literature have penalties incorporated in them when the scheduled job is not completed in the specified time. The authors have incorporated a reward for each job if the job is completed ahead of time. Such an approach has led to the increase in efficiency of the system.

Complex machining operations configured in the existing setup of FMS having 6 Machines producing 3 different parts through 3 alternative routes is considered for this work. An automated tool in the form of Graphical User Interface (GUI) is designed to automate the optimization of scheduling process by searching for solution in the search spaces using Bacterial Foraging Optimization Algorithm (BFOA), Genetic algorithm (GA) and Differential Evolution (DE) approaches and choose the best for that scenario. The inclusion of reward along with the penalty value as one of the parameters in the Combined Objective Function has yielded expected results in increasing the efficiency of the scheduling process in way of reduced machine idle time and reduced penalties.