The Research For Single Hoist Scheduling Of Automated Manufacturing Cell

Production scheduling problem has been a research focus in recent years. The efficiency of production scheduling is conducive to enhance the overall level of the manufacturing industry. With the rapid development of advanced automated manufacturing techniques, the use of computer-controlled automated manufacturing cell with the material handling device is widely used. In addition to consider job scheduling, reasonable scheduling of handling devices is needed,so the automated manufacturing cell scheduling is much more complicated than classical scheduling problem. Based on the printed circuit board electroplating production lines, we study the automated manufacturing cell scheduling of single hoist. We divided the research into single hoist cyclic scheduling and single hoist multi-product scheduling based on the characteristics of actual production.For single hoist cyclic scheduling, we presented an improved hybrid genetic algorithm according to the characteristics of the electroplating production line. To overcome slow convergence and being easy to fall into local optimal solution in the search for optimized hoist transport sequence,we combined the best indivudual saved strategy and local search with the genetic algorithm.The simulation results show the improvements enable the genetic algorithm solving the single hoist cyclic scheduling more efficiently.For the single hoist multi-product scheduling, taking into account of the importance of balanced production in multi-product mass production mode,we proposed a heuristic algorithm based on mininal part set.To optimize the hoist transport sequence in the mininal part set, we proposed a heuristic genetic algorithm, adding heuristic rules in population initialization.Also, we added neighborhood search which was based on the transport order rules. Through the simulation to heuristic genetic algorithm and comparing with another method, we improved that the heuristic algorithm based on mininal part set can solve the single hoist multi-product scheduling efficiently.