Adaptive genetic algorithm for scheduling problem in flexible workshop with low carbon constraints

Abstract

Taking the completion time, energy carbon emission and total machine load as independent time factors into consideration, a flexible workshop scheduling model is established to minimise the maximum completion time, energy emissions and the total machine load. The population could be initialised by greedy algorithm and random number method, and this model could be solved by the crossover probability and genetic probability adaptive method. The feasibility and effectiveness of the improved genetic algorithm are verified by testing the data set and comparing several single-objective data and normalised multi-objective data.