Research On Flexible Flow Shop Scheduling Problem Under Fault Conditions

Flow shop scheduling problem(FSSP) is a classic combinatorial optimization problem, which is widely applied in the field of manufacturing. The FSSP aims to achieve satisfied performance indicator results through allocating processing time and order of jobs. However, the manufacturing environment of FSSP is under certain constraints on resource and technology. Though traditional FSSP assumes machines are available during the course of production, machines suffer breakdowns in practical manufacturing environment. Therefore, machine breakdown should be taken into consideration in order to effectively utilize the FSSP results in manufacturing. Flow shop scheduling problem with machine breakdowns receives interests from academy since it arises.An improved genetic algorithm and improved particle swam optimization algorithm to solve flexible flow shop scheduling problem with machine breakdown is proposed. Firstly, machine breakdown is introduced and a mixed integer programming model aimed at minimizing expectation of makespan is established based on characteristics of flexible FFSP. Then, in order to get a better research result on solving flexible FFSP with machine breakdown, both the GA(Genetic Algorithm) and PSO(Particle Swam Optimization) algorithm are modified. The modification is made on two aspects. Firstly, a modified method to generate initial population is proposed in the GA and PSO algorithm. Secondly, the improved PSO algorithm combined with the TLBO(Teaching-Learning Based Optimization) are proposed.The parameters in proposed GA and PSO algorithm is suggested by the experiment design based on orthogonal experiments. The experiment of proposed algorithms using instances with different scales and comparisons from aspects of robustness, capacity of getting superior result with existing algorithms verify the validity and effectiveness of proposed algorithms.