Multi-Objective Parallel Machine Scheduling Considering Learning Effect and Energy Consumption
Learning effect exists widely in workshop production,and the accuracy of shop scheduling optimization based on learn-ing effect is higher.The parallel machine scheduling problem is studied by theoretical analysis and genetic algorithm.A schedul-ing model of multi-objective parallel machines with maximal completion time and minimum energy consumption is established.A second-generation non-dominated sorting genetic algorithm based on greedy decoding algorithm is designed.Based on process and machine double real number encoding,decoding process to insert the greedy algorithm,based on the workpiece process,get the procedure in the current equipment for processing the serial number and the machining gap existing in the current equipment,the equipment matrix to judge the position of the workpiece process can be inserted into the gap,and then update the start and fin-ish time and the energy consumption of machine usage.A numerical example is given to verify the effectiveness of the model and algorithm.
Parallel Machine SchedulingNSGA-ⅡGreedy AlgorithmLearning EffectMinimum Energy Con-sumption
万方数据
维普
