Research On Blocking Hybrid Flow Shop Scheduling Optimization

As the economic structure continues to be optimized and upgraded,the competition among manufacturing companies has become more and more prominent.If manufacturers want to outstand from the fierce competition,it is necessary to reduce the cost of production.Therefore,it is of great significance to study the shop scheduling and improve production efficiency.This paper focuses on the Blocking Hybrid Flow Shop Scheduling Problem(BHFSSP),in which the machine will be blocked on the current machine if it is not available in the next stage.Compared with the traditional Hybrid Flow Shop Scheduling Problem,it is much closer to the actual environment considering the situation that there is no buffer between processes.As an intelligent algorithm,genetic algorithm is widely used because of its short large-scale computing time,strong search ability and robustness.However,when it comes to solving some complicated problems,the algorithm still has problems such as being easy to fall into local optimum,premature convergence and so on.To this end,this paper takes BHFSSP as the research background,proposes a related scheduling model,and studies the improved genetic algorithm to make the performance better.Firstly,analyzing the shop scheduling problem and summarizing the algorithm.Studying the research status of HBFSSP and the methods used at home and abroad as well as the research results.Then,improving the genetic algorithm.And the related theories and reviews of genetic algorithms are analyzed.The improvement mechanism of genetic algorithm is emphasized: the coding adopts real number method,and the initial population is improved by mixed heuristic rules;the selection of fitness function and introduction of selection operations;methods of crossover and mutation operations and giving adaptive probabilities;proposing embedded local search,neighborhood exchange rules and specific processes of local search.Next,this paper studies the BHFSSP considering different constraints: First,the release time BHFSS makespan problem.Considering the start release time of the workpiece in actual production,the mathematical model is established on the goal of the makespan,introducing the adaptive genetic parameter and the local search.Then Local Search based Adaptive Genetic Algorithm is proposed,using the traditional genetic algorithm and the improved genetic algorithm to solve the problem studied in this paper.Simulation on MATLAB and the comparative analysis shows that the proposed LS&AGA has great effectiveness.Second,BHFSS total weighted completion time considering machine failure and transportation time.When the transportation time among the actual production stages is mentioned and the possible failure of the processing machine shows up,adaptive genetic parameters and the heuristic rule are introduced,aiming at minimizing the total weighted completion time.Thus this research proposes Heuristic Rules based Adaptive Genetic Algorihm(HR&AGA),which uses traditional genetic algorithms and improved genetic algorithms to solve this problem.Simulations were performed on MATLAB,and the results of comparative analysis shows the effectiveness of the proposed HR&AGA.