A Pareto based multi-objective genetic algorithm for scheduling of FMS

Many real-world engineering and scientific problems involve simultaneous optimization of multiple objectives that often are competing. In this work, we have addressed issues relating to scheduling with multiple (and competing) objectives of Flexible Manufacturing System (FMS) and have developed a mechanism by employing a Pareto based GA to generate nearer optimal schedules. In the proposed method we have applied Pareto ranking to identify the elite solutions and their fitness values are derated using fitness sharing method. The procedure is evaluated with sample problem environment found in literature and results are compared with other available heuristics found in literature. The proposed Niched Pareto Genetic Algorithm (NPGA) exhibits a superiority over the other heuristics and scheduling rules.