Research On Green Scheduling Of Hybrid Flow Shop Based On Multi-Objective Discrete Grey Wolf Optimization Algorithm

Under the policy of "accelerating the construction of a powerful manufacturing country" in the new era,China’s manufacturing enterprises are facing new development requirements.On the one hand,the fierce competition requires enterprises to continuously improve production efficiency and customer satisfaction.On the other hand,with the increasing depletion of energy,enterprises should also pay attention to energy conservation and emission reduction while pursuing their own benefits.Mixed flow workshop is a kind of production workshop that is not uncommon,which usually exists in chemical industry,steel smelting and other industries.The traditional research of hybrid flow shop often assumes that the machining speed of the machine is unchanged.However,in order to better carry out production activities,enterprises often adopt the method of adjusting the machining speed.Machine is the key to production scheduling.If the machine is not available,the production plan will not be completed on time.Therefore,combined with the actual production environment and enterprise requirements,the study of multi-objective hybrid flow shop scheduling problem considering machine speed and machine availability has practical and theoretical significance for production management and scheduling.The main research contents of this paper are as follows:Based on the basic gray wolf optimization algorithm,a multi-objective discrete gray wolf optimization algorithm is designed.Firstly,the coding scheme is designed based on machine allocation code and speed selection code,and the decoding scheme is designed based on the principle of shortest processing time and first come first processing;Secondly,a reverse learning strategy is added to the initial gray wolf population to improve the population quality;Thirdly,we rely on non dominated ranking and congestion distance to find leadership individuals;Then,the following mode is designed based on multi-point intersection and uniform two-point intersection,and the self-propelled mode is designed based on multi-point mutation.They form a search mode to balance the local search and global search capabilities of the algorithm;Finally,in order to preserve excellent individuals,an improved elite retention strategy is added to the algorithm.The multi-objective hybrid flow shop scheduling problem considering machine speed is studied.Taking the total energy consumption and the maximum completion time as the optimization objectives,the corresponding mathematical programming model is established,and the multi-objective discrete gray wolf optimization algorithm is proposed to solve the problem.Simulation experiments are carried out on different scale problems and compared with other algorithms.The experimental results show that the multi-objective discrete gray wolf optimization algorithm is effective in solving the multi-objective hybrid flow shop problem considering machine speed.The multi-objective hybrid flow shop scheduling optimization problem considering machine speed and availability is studied.Taking the minimization of total tardiness and total energy consumption as the optimization objectives,and considering the constraints of multiple processing speeds,inter process transportation and machine availability,a mathematical programming model is established.The decoding method of the above multi-objective discrete gray wolf optimization algorithm is improved so that it can solve the problem.Large scale simulation experiments are carried out on different parameter combinations.The results show that compared with other algorithms,the multi-objective discrete gray wolf optimization algorithm has better results in solving the multi-objective hybrid flow shop problem considering machine speed and machine availability,This proves that the multi-objective discrete gray wolf optimization algorithm is effective in solving the hybrid flow shop scheduling problem.