Genetic algorithms in integrated process planning and scheduling

This paper presents a review of genetic algorithms in integrated process planning and scheduling problems. According to the literature information, process planning and scheduling are two functions that were sequentially carried out in a manufacturing system, where scheduling was performed after process plans had been generated. Their integration highly improves the performance and efficiency of manufacturing systems. The integrated process planning and scheduling problem belongs to the class of most difficult combinatorial problems and it requires high efficient methods for finding optimal solutions. Genetic algorithms are one of the most famous metaheuristic algorithms based on the principles of artificial intelligence that found its use in various branches of science. Modern genetic algorithms proved to be very reliable in finding optimal process plans and schedules. Here, state of the art review of genetic algorithms for optimization of process planning, scheduling and their integration is shown. Many different modifications and hybrid approaches are briefly discussed. Mostly used genetic components and strategies are shortly presented with some sample parts that are often considered when testing genetic algorithm performances.