Research On The Solution Of Complex Workshop Green Scheduling Problem Based On TLBO Intelligent Optimization Algorithm

Shop scheduling problem has been the research focus of manufacturing enterprises,and in recent years with the growth of global warming and environmental pollution,shop scheduling problem considering green indicators,namely green shop scheduling problems began to cause concern manufacturing system,it is becoming academic research,and it is a more difficult issue.Intelligent optimization algorithm is an important method to solve the complex shop green scheduling problems.The teaching-learning-based optimization algorithm is a new emerging intelligent optimization algorithm for simulating the teaching and learning processes,which can be used to solve complex shop scheduling problems.In this paper,the improved algorithm of teaching-learning-based optimization algorithm is applied to solve the complex flow shop green scheduling problems.The main work is as follows:(1)In this paper,a modified teaching-learning-based optimization algorithm is designed to solve the no-wait flow shop green scheduling problem with sequence-dependent The criterion is to minimize the total energy cost.Firstly,teachers carry out self-study and improve their teaching levels before the teacher stage.Then,an adaptive teaching factor is proposed to enable students to learn adaptively.Finally,a fast local search mechanism based on insert operation is designed to improve the local search ability of the algorithm.The simulation experiment and algorithm comparison verify the effectiveness of the proposed algorithm.(2)Based on the permutation model in(1),considering adding makespan for optimizing economic indicators,a modified multi-objective teaching-learning-based optimization algorithm is designed to solve the multi-objective no-wait flow shop green scheduling problem with sequence-dependent The criteria are to minimize the makespan and total electric consumption.First of all,teachers carry out self-study to improve the quality of teaching.Then,self-adapting teaching factors are improved so that students can learn the teachers' knowledge adaptively.Finally,local search based on double workpiece insert operation is introduced to enhance the local search ability of the algorithm.Simulation results show the effectiveness of the proposed algorithm.(3)Based on the permutation model in(2),considering the flexibility of the machines in different stages of processing,a more general hybrid multi-objective teaching-learning-based optimization algorithm is designed to solve the multi-objective no-wait flexible flow shop green scheduling problem with sequence-dependent The criteria are to minimize the makespan and total electric consumption.First of all,we use the crowded distance to select teachers.Secondly,we improve the self-adapting teaching factors.At the same time,we take the individual average of the worst solution set as the average of the whole class,and introduce the crossover strategy after the teachers' stage.In addition,the learner stage is improved to make it learn the teachers' knowledge more greatly.Finally,the neighborhood search based on the swap operation is introduced to improve the search depth and enhance the local search ability of the algorithm.Experimental results verify the effectiveness of the proposed algorithm.