Multi-Objective Flexible Workshop Low-Carbon Scheduling Based on Improved GJO Algorithm
To reduce carbon emissions and energy consumption in multi-objective flexible shop scheduling,considering the impact of setup and termination processes on low-carbon workshop scheduling,a workshop scheduling model was established to optimize maximum completion time,workshop carbon emissions and machine load.An improved golden jackal optimization(I-GJO)algorithm was proposed to solve the objective functions.Firstly,a Tent chaotic mapping combined with beta distribution was introduced for population initialization to improve population quality.Then,a refraction opposition-based learning strategy was adopted to expand the optimization range of the algorithm,so as to significantly improve its ability to escape local extreme value.Finally,adaptive inertia weights were incorporated to improve the convergence rate and optimization accuracy of the algorithm by setting appropriate inertia weights.Scheduling example data shows that the improved GJO algorithm has better applicability than the original algorithm and grey wolf algorithm for multi-objective flexible workshop low-carbon scheduling problems,and can obtain higher quality solutions.
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