Research On Flexible Job Shop Scheduling Based On Improved Marine Predator Algorithm

Job shop scheduling,as the main form of manufacturing production schedules,plays an important role in supporting the development of modern industrial technology and promoting enterprises to carry out international operations.Flexible job shop scheduling problem(FJSP)is widely used in the manufacturing industry.Its main purpose is to select the best machine for the workpiece process and arrange its processing sequence reasonably,to improve the processing efficiency of workshop production,and reduce the machine loss based on satisfying the constraints.FJSP increases the selectivity of machines,and each process of each workpiece can be selected by multiple machines,which is more in line with the actual production state than traditional job shop scheduling.Based on the flexible job-shop scheduling problem,this paper establishes a job-shop scheduling model and uses the improved marine predator algorithm to solve it.The research results are as follows:Firstly,the flexible job shop scheduling problem is deeply studied,and its basic principle,mathematical model,and solution method as well as the graphical expression of the job shop scheduling solution are understood,and the marine predator algorithm is selected to solve it.Due to the problems of the marine predator algorithm(MPA),such as weak adaptive ability and easy falling into local optimum,this paper proposes a marine predator algorithm(COMPA)based on disturbance and elite reverse mechanism.The search speed of the algorithm is improved by changing the step-size adaptive parameters of the algorithm;In the process of predator movement,the optimal neighborhood disturbance mechanism and elite reverse learning mechanism are used to update the population,to avoid the algorithm falling into local optimization due to the large predator step size.By comparing the improved algorithm with five heuristic algorithms and five improved MPA algorithms with 23 benchmark functions,the experimental results show that the COMPA algorithm has good solving performance.Secondly,the mathematical model of a flexible job shop scheduling problem to minimize the maximum completion time is established.During the production scheduling process,the environmental pollution factors are considered,and the production indexes are optimized,at the same time,the energy consumption and carbon emissions are effectively reduced,and a flexible job shop scheduling problem model with the optimization goal of minimizing the sum of processing cost and total energy consumption cost is established.As flexible job shop scheduling is a discrete problem,the marine predator algorithm cannot solve it directly,so an ICOMPA algorithm is designed.This algorithm first uses a hybrid method to generate a high-quality initial solution,uses a vector conversion mechanism to convert discrete variables and continuous variables,and uses greedy decoding rules to define a scheduling scheme for decoding.Selecting 13 examples from two FJSP test sets,the two scheduling models are simulated respectively,in which the processing cost and energy consumption cost are randomly generated for solving the cost-based job shop scheduling problem.Experimental results show that the stability and accuracy of the improved algorithm in solving FJSP problems are better than other comparative algorithms.Finally,to verify the practicability of the algorithm,considering the processing situation of the actual factory,taking the flexible job in the mechanical processing workshop of D company as the research object,a series of constraints on the flexible job scheduling of the company are set up,and two kinds of workshop scheduling models are solved by using the improved marine predator algorithm.The results show that the scheduling scheme meets the actual production scheduling requirements and is superior to the manual scheduling results,which verifies the feasibility of the improved algorithm.