Improved Multi-objective Composed Optimization Algorithm Studying For Flexible Job-shop Scheduling Problems

In production and life,job shop scheduling studying is a research hotspot.Flexible job shop scheduling is a difficult classification in job shop scheduling.In the actual production process,flexible job shop scheduling often needs to meet the requirements of multiple objectives,and these objectives may be contradictory.Therefore,this paper proposes a multi-objective optimization combination algorithm to solve the multi-objective flexible job shop scheduling problem.The main work of this paper is as follows:(1)four loading methods and four working procedures can be inserted forward on each machine are summarized in detail;the overall process loading and machine work arrangement are summarized systematically;(2)a new algorithm is established by combining the gravity search algorithm with the Memetic algorithm,which makes the algorithm not only solve multiple problems The objective combination optimization problem can still search the global optimal solution efficiently;(3)three mutation modes are designed: single point mutation on machine mutation and single point mutation on operation exchange,and compound mutation mode combining machine mutation and operation exchange;(4)three search strategies are introduced to improve performance: local search and all search At the same time,we also discuss the diversity preserving strategy which has not been introduced but is also worth studying.In this paper,firstly,the multi-objective flexible job shop scheduling problem is systematically described: the problem is described by text and mathematical model,and the three objectives of the maximum completion time,total machine load and maximum machine load are selected as the optimization objectives;secondly,the coding mode and Gantt chart are introduced;secondly,the four kinds of process loading methods and the situation that four kinds of processes can be inserted forward are elaborated in detail;finally,the whole task scheduling is systematically arrangedConclusion.Then,the principles of multi-objective optimization and optimization algorithm are described,including the definition of Pareto solution,evaluation method(convergence,distribution and comprehensiveness)and the way of obtaining Pareto solution;the gravitational search algorithm and Memetic algorithm used in the combined algorithm proposed in this paper are introduced.After that,the improved combinatorial algorithm proposed in this paper is focused on,and the mutation search method of discrete combinatorial problem is discussed.Then,three mutation methods combining flexible job shop scheduling problem are proposed in the algorithm: machine mutation and single point mutation of operation exchange,and their complex mutation.Three search strategies are introduced in the algorithm: local search and global search(Metropolis)Finally,the main sub functions are shown.Finally,based on the Kacam test set,the algorithm is tested and evaluated: firstly,the velocity change of particle particles during the running of the algorithm is analyzed,which indirectly reflects the convergence of the algorithm;secondly,the parameters of population division in the algorithm are discussed;thirdly,the Pareto solution set searched by the algorithm is compared with the solutions of other algorithms,and the results show that the algorithm proposed in this paper performs well.At the same time,it shows the Gantt chart of partial solution,compares the algorithm with different variants of the introduced search strategy,records and discusses the CPU time of several variants of the algorithm,so as to verify the effectiveness of the search strategy added in the algorithm.