The Hybrid Cuckoo Algorithm Solves The Green Flow Shop Scheduling Problem

The scheduling problem has always been a research hotspot of manufacturing enterprises.In recent years,with global warming and environmental pollution becoming more and more serious,the green shop scheduling problem has begun to attract the attention of manufacturing systems,and it has gradually become a difficult problem in academic research.Therefore,this paper considers the flow shop scheduling problem with green indicators.The important method for solving the scheduling problem is generally the intelligent optimization algorithm.The cuckoo algorithm is a meta-heuristic intelligent optimization algorithm.The algorithm searches for the levy flight optimization mechanism when the cuckoo lays eggs,and can solve the continuous optimization problem quickly and effectively.Through the discretization of the algorithm,it can be better used to solve the discrete flow shop scheduling problem.In this paper,the improved algorithm of cuckoo algorithm is applied to solve the flow shop scheduling problem with green indicators.The main work is as follows:(1)For the permutation flow shop problem with energy consumption(PFSP?EC),the adaptive step size control factor is designed in the Levy flight exploration stage of the cuckoo algorithm.Based on the adaptive step size control factor,an adaptive cuckoo search algorithm(ACS)can be designed for discrete fields.The optimization target is total carbon Emission(TCE).First,the introduction of roulette rules in the initialization,increase the diversity of the population;then,using the LOV rules to convert the individual in the ACS algorithm from the real vector to the work-piece ordering,so that it can be searched in the solution space of the PFSP?EC;Finally,each individual in the population is updated according to an adaptive cuckoo algorithm.The effectiveness of the discrete algorithm is verified by simulation experiments.(2)Based on the permutation model and algorithm in(1),consider simultaneously optimizing the maximum completion time(make-span)of economic indicators and the total carbon emission(TCE)of environmental factors,and solving multi-objective flow shop scheduling problem with green indicators(MOFFSP).To solve MOFFSP,design a hybrid cuckoo algorithm(HCS).Firstly,based on the ACS algorithm proposed in Chapter 2,the MOPFSP problem is searched globally to obtain a high-quality solution region;Then,the multi-neighbor local search is designed to continue the deeper exploration in the current high-quality solution region,making the MOPFSP problem global and the local search has a better solution set;finally,the method is applied to multi-class cable processing,which can solve the excellent solution in a short time.The analysis and comparison of simulation experiments and case analysis verify the effectiveness of the hybrid cuckoo algorithm.(3)Based on the permutation model and algorithm in(2),consider the Multi-objective Mixed No-idle Permutation Flow shop Problem with Energy Consumption(MOMNPFSP),and optimize the target to minimize the maximum completion time(make-span)and Total Carbon Emission(TCE),and an improved hybrid cuckoo algorithm(DHCS)to solve this problem.Firstly,based on the multi-objective characteristics,in order to better judge the individual's advantages and disadvantages,the individuals are stratified in the algorithm;then,based on the Levy flight formula and the update mechanism of the abandonment probability,a new discrete global search operation is designed;at the same time,the quasi-entropy strategy is introduced.Based on the global search,a self-starting multi-neighbor local search is proposed to find the best time to enter the local search,thus enhancing the search depth.Simulation experiments and analysis prove the effectiveness of the improved algorithm.