Research On Garment Sewing Hybrid Flow Shop Scheduling With Learning And Forgetting Effects

Nowadays,facing the increasingly personalized needs of customers,garment manufacturers should not only adapt to the production mode of small order batch and short delivery cycle,but also face the operational dilemma of rising labor costs and high mobility of front-line employees.The complex and changeable environment requires further improvement of the ability of enterprises to manage complexity.In order to better adapt to changes in the workshop production environment and improve the adaptability of production scheduling to complexity and uncertainty,this paper proposes a new management scheduling method that considers the differences in workers’ cognition and learning,and studies the scheduling problem of garment sewing workshops that considers workers’ learning and forgetting effects.In this paper,the sewing shop scheduling problem is regarded as a hybrid flow shop scheduling problem.Based on literature research,learning effect is characterized by the number of repeated processing and the accumulated processing time,forgetting effect is characterized by the length of processing interruption,and heterogeneity of workers is distinguished by coefficient of unfamiliar skill and learning rate.A learning and forgetting effects function based on coefficient of unfamiliar skill,learning rate,and length of processing interruption is comprehensively constructed.Based on the environment and layout of the sewing workshop,the composition and principle of the hanging system and the production characteristics of the workshop under the hanging production line,the constraints such as flexibility of process sequence,batch transmission,flexibility of equipment use,and uniqueness of processing are considered,A multi-objective hybrid flow batch scheduling model considering learning and forgetting effects in sewing shop is constructed,whose optimization goals is minimizing makespan and minimizing idle mean square error.In this paper,a two-layer search framework based non-dominated genetic algorithm(NSGA-II)is designed to solve the scheduling model.The outer search framework of the algorithm executes the batch strategy based on the idea of traversal,while the inner nondominated genetic algorithm selects the optimal scheme based on adaptive crossover and mutation,fast non-support sorting and other operations.Then simulation results show that the proposed algorithm is effective.Finally,the paper uses the production data of the actual enterprise to implement the algorithm simulation experiment.The simulation experiments compared and analyzed the effects of considering the learning and forgetting effects,different learning rates and different forgetting factors on the target.The experimental results show that the production scheduling considering the learning and forgetting effects has better adaptability to the complex uncertain environment,and also shows the positive relationship between the learning rate and optimization goal and the negative relationship between the forgetting factor and optimization goal.