Research On Flow Shop Scheduling Problem Optimization Based On Intelligent Computing

The problem of flow shop scheduling is common in modern production and manufacturing.Reasonable arrangement of production scheduling can save some resources for production and manufacturing enterprises and reduce production costs.At present,there are many factories whose workshops are not very good,and even the processing route of the flow shop is unreasonable,which not only reduces the processing efficiency of the workshop,but also causes damage to the equipment in the workshop.Therefore,how to get the most reasonable workpiece processing flow through the limited resources of the workshop is a real problem that needs to be solved urgently.Since the flow shop scheduling has proved to be an NP-hard problem,the traditional optimization method is difficult to solve,and the intelligent optimization algorithm is the most commonly used method to solve the flow shop scheduling problem.The intelligent algorithm with excellent performance can often get better optimization effect.Based on this,this paper improves the two intelligent optimization algorithms,and optimizes the algorithm to solve the two flow shop scheduling problems.The main research contents are as follows:(1)Aiming at the problems of basic flower pollination algorithm,such as easy to fall into local optimum and low accuracy,a flower pollination algorithm with memory information(MFPA)is proposed.The improved algorithm designs the time-delay adjustment operator and the adaptive weight for the global pollination process and the local pollination process of the basic flower pollination algorithm.The performance of the algorithm is tested by multiple sets of standard functions.The simulation results show that the proposed MFPA has better search efficiency and accuracy.(2)Aiming at the defects and shortcomings of water wave optimization algorithm which is easy to fall into local optimum,an improved water wave optimization algorithm(DEWWO)with differential evolution strategy is proposed based on the basic water wave optimization algorithm.The process of differential evolution effectively increases the population diversity of the algorithm,avoids the algorithm falling into local optimum,and improves the efficiency and accuracy of the algorithm search.By benchmarking the performance of the algorithm and comparing it with the classical optimization algorithm,the experimental results show that the proposed DEWWO has obvious advantages and good robustness.(3)The proposed flower pollination algorithm(MFPA)with memory information is used to solve the permutation flow shop scheduling problem.Using the random key technique based on LOV rules to encode and decode the proposed algorithm,in the process of generating initial populations,a population quality improvement strategy based on the NEH algorithm is used to improve the algorithm's optimization results.Car test set and Rec test set are used to test the performance of the proposed MFPA and compared with the classical algorithm.The experimental results show that the proposed algorithm has better solution performance and provides a solution for PFSP.(4)The improved water wave optimization algorithm with differential evolution strategy proposed in this paper is used to solve the problem of no-wait flow shop scheduling.The no-wait flow shop scheduling problem is a kind of discrete combinatorial optimization problem,and it is impossible to directly update the workpiece processing sequence.Therefore,this paper redefines the three operational processes of the improved water wave optimization algorithm: propagation,refraction,and breaking waves,and realizes the continuous continuous vector conversion into discrete processing steps in the water wave optimization algorithm,so that the continuous water wave optimization algorithm can solve Discrete NWFSP.The performance of DEWWO proposed in this paper is tested by the Car test set in OR-Library,and compared with the results of classical algorithm.The results show that the proposed algorithm has higher global search ability and has higher accuracy in solving.The obvious advantage is to broaden the application range of the algorithm.