Research On The Collaborative Optimization Decision Making Method Of FJSP Production And Energy Consumption

With the continuous development of modern manufacturing industry,energy consumption is also increasing.Energy saving and consumption reduction has become the key to the sustainable development of manufacturing industry.In actual workshop production,a single target can no longer meet the demand,often need to consider multiple targets and the traditional workshop scheduling method has been difficult to solve the scheduling problem.At the same time,there is a lack of reasonable decision method to help the selection of multi-objective scheduling schemes.Therefore,it is of great practical significance to study the direction of multi-objective optimization scheduling and decision-making in manufacturing workshop.In this paper,static multi-objective scheduling and dynamic multi-objective scheduling in flexible shop production process are studied and solved by efficient optimization algorithm.Meanwhile,aiming at the problem of multi-objective scheduling scheme decision,a reasonable comprehensive decision method is designed for selection.Finally,according to the actual production needs of enterprises,a set of shop scheduling and decision system is developed,and good application results are obtained.The specific research work is as follows:(1)Analyze the production process of flexible job shop,establish mathematical model of multi-objective optimal scheduling for flexible job shop,and give constraint conditions and parameter definitions.Two modeling examples of static and dynamic flexible job shop are given and this paper adopted by the basic scheduling algorithm-the NSGA-Ⅱ is introduced.(2)Considering the problem of the multi-objective constrained flexible job-shop,the NSGA-Ⅱ algorithm based on hybrid mutation operator is proposed to solve in this paper.In view of NSGA-Ⅱ algorithm being prone to premature convergence,poisson average and gaussian operators are introduced to improve the global and local optimization ability of the algorithm.The optimal scheme is selected from the set of pareto solutions by adopting the strategy of FAHP-IEVM,which is the combination of subjective and objective evaluation method.The effectiveness of the algorithm’s optimization ability,the efficiency of the running time and the rationality of the decision algorithm are verified by an example simulation.(3)In order to solve the dynamic optimization problem effectively and improve the stability of the algorithm,a further improvement is made on the basis of the above algorithm for the dynamic flexible job shop scheduling problem.Firstly,Logistic chaos mapping iterative equation was introduced during initialization to avoid the algorithm falling into local optimum.In order to improve the quality of solution set,a fast non-dominated sorting method based on immunology principle and external storage as elite retention strategy are introduced.At the same time,an improved ITOPSIS-G1-IEVM comprehensive decision-making method is proposed.The comprehensive weight of G1-IEVM is calculated by Nash equilibrium theory.Then,the comprehensive weight and ITOPSIS evaluation system are combined to evaluate each dispatching scheme.Finally,the experimental results show that the optimal scheduling algorithm is superior in the optimization ability and the effectiveness of the comprehensive decision-making method.(4)On account of the proposed optimal scheduling and decision method,a set of optimal scheduling and decision system of a workshop was developed based on the information integration platform independently developed by the project and the actual production requirements of a certain enterprise workshop.Based on Oracle database,Matlab,IDEA and other development software to complete the development of optimization scheduling and decision system functional modules.At the same time,the practical application in the workshop of an enterprise verifies the feasibility of the theoretical method and the system in the guidance of multi-objective flexible production.