Research On Fuzzy Parallel Machine Batch Scheduling Problem Based On Fruit Fly Algorithm

As one of the significant research field of operations research,production scheduling problem has abundant applications in metal manufacturing,logistics freight and food manufacturing.With the development of industry,the traditional working mode is difficult to meet the complex scheduling environment.The batch scheduling production mode can process materials in batches,which has attracted widespread attention.Making efficient batch scheduling scheme can realize reasonable allocation of limited resources,reduce industrial costs and improve production efficiency,which is an important way for enterprises to maintain long-term development.Based on the operation mode of batch processing,this thesis studies the single-objective and multi-objective batch scheduling problem on non-identical parallel machines in the semiconductor chip manufacturing and smart logistics industries.Due to the existence of multiple uncertain factors in reality,fuzzy theory and batch scheduling problems are combined in this thesis.The mathematic model is created and the algorithm is put forward to provide enterprises with more reasonable and effective decision-making support.The main research contents of this thesis are as follows:(1)Dealing with single-objective parallel machine batch scheduling problems in fuzzy environment.The objective is to minimize the maximum completion time.The processing time of job is considered to be a fuzzy number,which is affected by both learning and deterioration effects during production.A fuzzy mathematic model is constructed and a multi-population fruit fly cooperative optimization algorithm is presented by researching and analyzing the characteristics of the problem.In the algorithm,the initial solution is generated by an efficient heuristic algorithm according to the batching characteristics.The population individuals are updated based on the multiple step length search strategy.The diversity of the algorithm is improved through the collaborative search of the elite population.Finally,a large number of comparative experiments are implemented to verify the performance of the algorithm.(2)A multi-objective batch scheduling problem is extended to optimize the maximum completion time and the satisfaction degree simultaneously in the fuzzy environment.In the research,the fuzzy deadline of the job is added as a constraint condition.The difference between completion time and deadline time is transformed into user satisfaction degree in fuzzy manufacturing system.A multi-objective fruit fly optimization algorithm based on historical information is proposed to addressing the studied problem.Firstly,a initialization strategy is put forward according to different objectives.Secondly the historical information of fruit flies is used to guide the search of the population.In addition,the objective is locally optimized by analyzing the structure of the solution.Subsequently the feasibility and efficiency of the algorithm are proved.