Learning-driven optimization of energy-efficient distributed heterogeneous hybrid flow shop lot-streaming scheduling
SHAO Wei-shi
PI De-chang
SHAO Zhong-shi
Abstract：This paper studies an energy-efficient distributed heterogeneous hybrid flow shop lot-streaming scheduling problem,where the processing efficiency of each factory is different and the jobs can be split into several sub-lots to access the manufacturing system.The mixed integer programming model is built with the makespan and total energy consumption objectives.A learning-driven multi-objective evolutionary algorithm is proposed,which includes learning-driven global search and local search.Q-learning is introduced as a learning engine,and the evaluation of population and non-dominated solution sets is used as an environmental feedback signal to dynamically guide the selection of search operations through continuous learning.Based on the characteristics of the problem,the state set,action set and reward mechanism of the algorithm are designed.The introduction of Q-learning can sense the current search state in time,reduce the blindness of search operations,and improve the efficiency of search.From the testing results on simulation data set,it is shown that the proposed algorithm can effectively solve the energy-efficient distributed heterogeneous hybrid flow shop lot-streaming scheduling problem.
Keywords：distributed heterogeneous hybrid flow shop schedulinglot-streaming schedulinglearning-driven multi-objective evolutionary algorithminteger programmingenergy-efficiency optimization
Publication Date：2024-06-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 1018-1028 )