Distributed Assembly Flow Shop Scheduling Based On Whale Optimization Algorithm

"Made in China 2025",released in recent years,and pointed out that China’s manufacturing industry should be vigorously developed.Through the combination of manufacturing and information industry,promote intelligent development.Meanwhile,intelligent clothing factories came into being and became one of the most widely used fields in intelligent manufacturing.The application efficiency of the factory significantly improved by distributed flow shop scheduling problem(DFSP).Meanwhile the factory provides the application background for the DFSP,which makes the problem more practical.To solve the problem of the DFSP with assembly constraints and batch delivery constraints,the characteristics of DFSP were analyzed,and mathematical modeling was carried out.Then improved the whale swarm optimization algorithm(WOA).And its performance is enhanced at the same time.The main contributions and achievements of the paper are as follows:1.For the DFSP which have assembly and batch delivery constraints,the problem model is set up.Firstly,the problem characteristics,objective characteristics and constraints of DFSP with assembly constraints are analyzed.The crane handling process under assembly constraints are is considered.The constraints of this kind of problems are improved.The multi-objective of minimizing completion time and energy consumption is modeled.Then,based on the above model,the batch delivery constraint are considered.And the delivery cost is increased as the optimization objective.2.Based on the above problem model,WOA is studied.Firstly,to solve the DFSP with assembly constraints,the basic framework of WOA is constructed.The simulated annealing and clustering algorithm are combined with it.Secondly,coding and decoding are carried out.Thirdly,the right shift strategy is proposed,and two crossover strategies and mutation operations are designed.Finally,according to the DFSP modeling with batch delivery constraints,based on the above algorithm,the coding mode of product batches is considered.The decoding scheme of batch sequence arrangement is added.The neighborhood structures such as insertion exchange are designed.And the local search is proposed to ensure the balance of the improved algorithm in global and local search.3.Based on the above model and optimization algorithm,application verification research is carried out with the background of intelligent clothing factory.Firstly,according to the actual production data,analyzed the result value obtained by the multi-objective value running 30 times independently.Then,the convergence curve proves that the multi-objective value tends to stabilize when solving the problem in this study,so as to verify the effectiveness of the improved algorithm.Finally,according to the production data,the Gantt chart is generated.Meanwhile the scheduling arrangement after the improved algorithm is analyzed to obtain the optimal solution.