Dual-enhanced Memetic Algorithm for Multi-task Scheduling in Sustainable Production
It is of great significance to comprehensively enhance the sustainability of production scheduling with economic,environmental and social demand.A scheduling model for parallel machine production is established with consideration of four decision tasks:Machine assignment,processing sequence,personnel arrangement,and on/off machine control.To solve this complex problem,a dual-enhanced memetic algorithm(DMA)that integrates two local optimization strategies is proposed.In a random manner,a one-step variable neighborhood search(1S-VNS)suitable for decision-making tasks is designed.For targeted optimization,a sustainable goals-oriented strategy(SGS)is constructed after analyzing the matching relationship between objectives and key tasks.Based on the dif-ferent characteristics of the two optimization strategies,the 1S-VNS acts on the entire population,and the SGS strengthens the elite individuals,achieving dual optimization of the output solution set.The simulation experiment-al results show that the dual optimization strategies effectively enhance the algorithm performance,and the pro-posed DMA has superiority in diversity and convergence of non-dominated solutions.
万方数据
维普
