Improved genetic algorithm for robotic cell scheduling problem with flexible processing times

An improved Genetic Algorithm (GA) was proposed to overcome premature convergence and redundant iterations by using traditional GA to solve the scheduling problem in robotic cell with flexible processing time. This algorithm adopted the encoding scheme based on part moving sequence. According to the characterstics of this scheduling problem, a new constructive heuristic method was designed to generate initial populations which eliminated large amout of infeasible chromosomes and improved the solution quality in the subsequent operations. At the same time, a local search was introduced to improve the efficiency of algorithm in the crossover and mutation operations. Finally, the proposed algorithm was compared to the traditional GA by solving six benchmark problems. Computation results proved the effectiveness of the improved GA.