Simulation And Scheduling Of Re-entrant Steel Pipes Product Lines Based On EM-Plant

The scheduling of the steel pipe product line, which is an important part of the manufacturing system, is closely related with the efficiency of equipment, cycle time of production and efficiency of the product line. In some small and medium-sized steel enterprises,which workpiece at different stages in the process use the same piece of equipment for processing, that is the existence of the phenomenon of re-entrant. As a result, lots strive to the access to the equipments and the waiting time prolongs accordingly. It is hard to solve this problem by single scheduling strategy. An effective way is to integrate all sorts of control and scheduling strategies and make use of discrete event dynamic simulation(DEDS)to evaluate whether the schedule is good or bad. Based on the intention, the simulation of product line is developed to solve complex steel pipe production workshop scheduling problem. The main works are as follows:â‘ For the multi-processing existed in cold-drawn seamless steel pipe production, the paper uses a third scheduling way that is different from the job shop and flow shop, re-entrant production scheduling, to study the steel production of re-entrant.â‘¡EM-Plant be used to make a simulation model of a actual steel pipe production line. Due to the reality of the complexity and instability, the paper propose the simple and constraint conditions. First, establish the sub-model of each process, then combinate the total model, and finally verify the feasibility and rationality of simulation model.â‘¢According to the nature of the re-entrant steel production systems, simplify the scheduling for optimize of material and dispatching strategies, and simulate scheduling strategies one by one, lastly compare and analyse the advantages and disadvantages in every strategies.â‘£Genetic algorithm will be applied to scheduling in pipe production line, and the GA module is imported into EM-Plant software. The paper design scheduling strategy based on genetic algorithm, and uses GA function to optimize material and dispatching strategies. Used the way, we can solve the optimal solution of multi-objective scheduling based on the average output rate, the average production cycle, and the utilization of key equipment.