Multi-objective cigarette production scheduling problem

Cigarette production scheduling in a tobacco enterprise is an optimisation problem that can directly reflect the revenue and production status of the enterprise. Therefore, it is necessary to propose a more accurate scheduling model based on the real tobacco enterprise. Firstly, this paper proposes a high-dimensional multi-objective cigarette production scheduling model considering time, cost, energy consumption, number of card changes, and load balancing. Secondly, a self-adaptive NSGA-III algorithm (SA_NSGA-III) based on indicator guidance is proposed to better solve the modified model and generate a scheduling scheme that can effectively improve the production performance. SA_NSGA-III introduces PD diversity evaluation indicators to guide the population evolution and solve the problem of poor diversity in the late convergence stage of the algorithm. Finally, the proposed algorithm is experimentally verified by real data examples from enterprises, and the results show that compared with other comparative algorithms, the SA_NSGA-III algorithm achieves optimal results.