Research on the Re-entrant Batch Discrete Flow Shop Scheduling for Periodic Annealing Furnace as Batch Processor

Abstract

Aiming at the flow shop scheduling problem of reentrant batch discrete machines with periodic annealing furnace as batch processor in seamless steel tube cold drawing production, a bi-objective optimal scheduling model is established, which minimizes the total work completion time and the total energy consumption of batch machines. A multi-objective particle swarm optimization algorithm, fast non-dominant ranking, congestion comparison and particle mutation operation algorithm are designed. In this algorithm, non-dominant ranking and congestion comparison are used to select the optimal particle and the combination of early and late mutation is used. The experimental results show that, compared with the Variation- Multi-Objective Particle Swarm Optimization algorithms and Non-Dominated Sorting Particle Swarm Optimization algorithms, the algorithm finds a better minimum on both objective functions, and the average level of the results is closer to the front of Pareto solution set, which effectively improves the optimization ability of the algorithm. Through Pareto solution, the algorithm can get a set of Pareto solution which comprehensively weighs the completion time and energy consumption of annealing furnace. It can provide a variety of alternative scheduling schemes. When the production time is sufficient, the scheme with lower energy consumption of annealing furnace can be selected as far as possible. When the enterprise has many orders to pursue production efficiency, the scheme with smaller completion time can be selected as far as possible to effectively solve the problem. Such practical problems have been solved.