Low-Carbon Flexible Job-Shop Scheduling Based on Improved Nondominated Sorting Genetic Algorithm-II

Abstract

Considering the impacts of multiple production objectives, makespan and low carbon factor on jobshop scheduling optimization, this paper puts forward a novel low carbon scheduling method for flexible job-shop based on the improved nondominated sorting genetic algorithm-II (NSGA-II). Firstly, a low-carbon scheduling optimization model was established for multi-objective, multi-speed job-shop. Then, the flow of the NSGA-II-based core algorithm was explained, and the new population selection was optimized through the calculation of congestion and nondominated level. Finally, multiple simulation examples were adopted to validate the proposed algorithm. The results show that the proposed NSGA-II low carbon optimization algorithm can converge to the global best Pareto solution rapidly, and lower the no-load and total energy consumption of the production line through automatic management while ensuring production efficiency.