Research On Low-carbon Scheduling In Flow Shop Considering Worker Fatigue

The 2021 government work report of the State Council pointed out that it is necessary to do a good job in carbon peaking and carbon neutrality.As the hardest hit area of carbon emissions,the manufacturing industry urgently needs to reduce the carbon emissions of enterprises through low-carbon scheduling.With the in-depth application of human factors engineering in the workshop,the influence of worker factors can be fully considered in production scheduling,and workers will be physically exhausted in the production process,which will significantly reduce production efficiency and product qualification rate,and increase the production process.carbon emissions in.Therefore,how to consider worker fatigue and synergistically optimize production scheduling performance and carbon emission targets has become one of the key issues to be solved urgently in current production scheduling research.This paper takes the low-carbon scheduling problem of assembly-flow workshop considering worker fatigue as the object.The main research contents are as follows:First of all,facing the scheduling needs of modern manufacturing enterprises in terms of production efficiency,cost,environment,etc.,deeply analyze the production characteristics of the flow workshop,and establish a low-carbon scheduling mathematical model including carbon emissions,worker load balance and production cost goals: The energy consumption was investigated in detail;the maximum energy consumption of different workers was obtained based on the physiological characteristics of different workers.When the maximum consumption was exceeded,the processing time of the process became longer,and the concept of fatigue factor was proposed;the proposed multiobjective model,using the triangular fuzzy number fuzzy analytic hierarchy process to normalize the three targets and assign weights.Then,an improved adaptive genetic algorithm is proposed to solve the abovementioned low-carbon scheduling model of the assembly line: a double-layer coding method with workpieces and workers is adopted to simplify the programming operation and facilitate the problem solving;in order to control the changing trend of the population,obtain better Individuals are iterated to retain dominant genes,accelerate the convergence speed,and introduce a nonlinear adaptive crossover mutation operator.This parameter can improve the solution efficiency on the basis of ensuring the accuracy of the optimization results,and alleviate the shortcomings of the standard genetic algorithm,such as low solution efficiency and poor local optimization ability.Finally,in order to verify the reliability of the proposed method,the production data of the enterprise was selected for analysis,and the proposed model was solved by standard genetic algorithm and nonlinear adaptive genetic algorithm.The production scheduling results of the method are compared,and it is found that the results obtained by the nonlinear adaptive genetic algorithm are better than the other two results.It is verified that the model and solution algorithm proposed in this paper have practicability and superiority,and provide theoretical support for enterprises to formulate production scheduling plans.