Research On Optimization Algorithm Of Flexible Production Process With Fuzzy Features

Production scheduling is the central link during production process planning and management,and the rationality of scheduling schemes will affect the efficiency and reliability of manufacturing enterprises' production process to a large extent.With the increase of uncertainties in actual production activities,the analysis,modeling and solution of scheduling problems become more complex.With the development of fuzzy theory,it is possible to describe the uncertainty of scheduling parameters in flexible production process with fuzzy characteristics.Therefore,ways to use appropriate intelligent optimization algorithms to optimize and solve such problems,and obtain a scheduling plan in line with the requirements of enterprises,has become the key point of research in recent years.This paper mainly studies the fuzzy flexible job-shop scheduling problem models with single objective and multi-objectives,and then uses flower pollination algorithm to solve them and finally get optimization solutions.The main research contents are as follows:(1)This dissertation studies the application of fuzzy features in flexible production process.Firstly,the basic definition and representation of fuzzy set theory are given,and then the rules of fuzzy set and two types of fuzzy numers in common use are analysed.Secondly,aiming at flexible production process under uncertain conditions,the fuzzy number is used to describe the indefinite value in the flexible job-shop scheduling problem,and the fuzzy flexible job-shop scheduling model is established.Finally,one of the meta-heuristic intelligent optimization algorithms,the flower pollination algorithm,is studied.The basic principle,applications,the merits and demerits as well as steps to solve the flexible jobshop scheduling problem are also analysed.(2)A discrete flower pollination algorithm is proposed to solve the single objective fuzzy flexible job-shop scheduling problem.Firstly,the model of single objective fuzzy flexible job-shop scheduling is established by using triangular fuzzy number to represent the processing time so as to minimize the fuzzy maximum completion time.At the same time,the operation rules of the triangular fuzzy number are defined.Secondly,the basic performances of the flower pollination algorithm are compared and analyzed in detail under several test functions,and the algorithm is discretized by introducing the discrete operator.The machine adjustment step is added afterwards during iterations,in order to avoid infeasible solutions.Experimental results show that the algorithm is more effective in instances of partial fuzzy flexible job-shop scheduling problem.(3)An adaptive discrete flower pollination algorithm is proposed for multi-objective fuzzy flexible job-shop scheduling problem.Firstly,considering the complexity of optimal objectives in enterprises,the processing time,processing cost of machines and raw material cost are represented by triangular fuzzy number,and the joint optimization objective is to minimize the fuzzy maximum completion time and the fuzzy production cost.Then a multi-objective fuzzy flexible job-shop scheduling model is established.Secondly,after the discretization of initial population solutions,the adaptive mutation operator is introduced in the iterative process for enhancing the ability of global exploration and local exploitation.The experimental results show that compared with other algorithms,this method performs better in solving practical flexible workshop problems.