Research On Flexible Job Shop Scheduling Problem Based On Improved Artificial Bee Colony Algorithm

Due to the development of the manufacturing industry,the proportion of workshop automation is increasing,whereas intelligent manufacturing is a major trend in the development of workshops,intelligent optimization plays an important role in various industries,which may directly affect the actual production efficiency and production costs of a factory,thus affecting the demand of different customers for orders,especially the demand for delivery time.While the optimization of flexible job shops scheduling problem(abbreviated as FJSP)has always been a hot topic in the research field of intelligent optimization problems,methods for solving this problem have evolved rapidly from the beginning of the exact solution approach to intelligent optimization algorithms that can be applied to the solution of large-scale problems.Therefore,this paper takes the production operation of a company’s machine shop as the research object,condenses the flexible job shop scheduling problem,and solves it with an improved algorithm for the problem.Firstly,In this paper,the mathematical model is established by minimizing the maximum processing time as the objective function of the flexible job scheduling problem.Second,the Artificial Bee Colony Algorithm(ABC)is introduced to solve the problem,but the Artificial Bee Colony Algorithm generally solves continuous optimization functions and cannot solve discrete problems,so the solution is encoded and the three stages of the algorithm are improved in order to improve the search accuracy of the algorithm in the solution process and to overcome the problems such as local optimality.In terms of encoding,in order to ensure the accurate description of the solution.In this paper,a two-layer coding approach is used,i.e.,the machining workpiece and the machine code present a one-to-one correspondence in the coding.The improvement of the algorithm is reflected in the three stages of hiring,observing and scouting bees,where the RPOX crossover operator and the variation operator of the process string and machine string are used in the hiring stage,and the number of times the food source is exploited is recorded.Onlookers stage for critical path search to obtain new solutions and compare the new solution with the old solution and select the better solution.At this point,the limitation on the number of times the food source can be readed,and eventually the employed foragers or onlookers that exceed the limit will be turned into scout bees and mined for a new food source.In this paper,two examples are used to verify the improved artificial bee colony algorithm.It can be found that the improved artificial bee colony algorithm is more stable and has the better solving ability.The improved artificial bee colony algorithm was finally applied to the flexible job shop problem in workshop M,and the result was 12.24% better than the manual scheduling problem and the effective processing time of the machine is also more balanced.This has enabled the company to increase its efficiency and demonstrated the value of the artificial bee colony algorithm in practical production.