Research On Intelligent Algorithm Of No-wait Batch Production Process

No-wait batch production process,which requires adjacent processes of the same product to be performed continuously,is a common form of production in manufacturing industries.Since the scheduling scheme can largely determine the efficiency and profitability of an enterprise,it is of great practical importance to study the production scheduling algorithm in such cases.In this paper,we propose a Hyper-heuristic Differential Evolution(HHDE)algorithm to solve the no-wait batch production scheduling problem,and study the no-wait batch production process scheduling problem under single-objective and multi-objective.In addition,the decision analysis of the solution set in the multi-objective case is carried out.The following is the main research content:(1)The description of the optimal scheduling problem and the solution algorithm for the no-wait batch production process are given.First,the classification and characteristics of the batch production process are described;second,an algorithmic model is established for the no-wait intermittent production scheduling process,Then the model objective optimization function and common research methods are described and the constraints of the model under the no-wait condition are given.The basic principles and classification of hyper-heuristic algorithm are introduced.Furthermore,their optimization framework and advantages and disadvantages are analyzed.(2)A single-objective no-wait batch production process scheduling problem is developed to minimize the maximum production completion time,and a hyper-heuristic differential evolution algorithm is proposed to solve it.This algorithm takes the hyper-heuristic algorithm as a framework,and the high-level strategy domain is used to combine a series of heuristic operations on the low-level problem domain by an adaptive differential evolution algorithm to form a new algorithm for searching the solution space.Then the updated strategy is decided according to the quality of the solution.At the same time,a simulated annealing algorithm is introduced in the solution updating process to prevent the algorithm from falling into local optimum prematurely.Finally,the superiority of the algorithm is demonstrated by comparing it with other algorithms through a typical calculation example.The practicality and effectiveness of the algorithm are also verified in the scheduling cases of actual plants.(3)For the multi-objective no-wait batch production process scheduling problem,a multi-objective no-wait batch production scheduling model is developed with the objectives of minimizing the maximum production completion time,total process time,and delivery satisfaction,and a Multi-objective Hyper-heuristic Differential Evolution(MOHHDE)algorithm is proposed to perform the solution.First,the update strategy of the algorithm is decided based on the coverage of the Pareto front,and second,the simulated annealing algorithm is extended to the multi-objective case to prevent the algorithm from falling into local optimum prematurely.Finally,for the multi-objective case with multiple sets of solutions,the Nash equilibrium model in game theory is used to combine the subjective decision Attribute Hierarchical Model(AHM)and the objective decision Criteria Importance Though Intercriteria Correlation(CRITIC)method to synthesize the unique scheduling solution.The comparative experimental results show that the algorithm performs better than other algorithms in solving the problem of no-wait batch production process in realistic situations.According to the actual problem of quantitative analysis of enterprise production combined with the algorithm of this paper,the no-wait multi-product batch production process optimization scheduling model is built,and the application software of intermittent production scheduling system analysis is developed by using C# programming language in combination with Oracle database,Shilin platform,Matlab and Visual Studio software,and applied in a biological fermentation company in Tai’an,Shandong Province.The effectiveness and practicality of the application software developed according to the algorithm of this paper are verified through practical applications.