Research On Flexible Assembly Shop Scheduling Problem Based On Improved Genetic Algorithm

The scheduling problem of the flexible assembly shop is a key problem in the manufacturing industry.This article takes the flexible assembly shop of the automobile as the research object and uses the improved genetic algorithm to solve the scheduling problem of the assembly shop.In the two cases without considering the workpiece handling time and the introduction of multiple AGVs for workpiece handling,different scheduling models are established for analysis,and the algorithm is adaptive to the problems of premature genetic algorithm early search,low search efficiency in the later period,and large randomness.Improve.The main research work and conclusions of the paper are as follows:(1)Describe the flexible job shop scheduling problem(FJSP)and flexible assembly shop scheduling problem(FAJSP)in detail,and analyze the differences and connections between the two scheduling problems.Establish a mathematical model of the flexible assembly shop that does not consider the workpiece handling time,and on the basis of determining the machine selection plan,establish an extended disjunction graph model of the flexible assembly shop scheduling problem.Experiments and analysis show that the two-layer coding method is suitable for solving the workshop scheduling problem with flexible production capacity;the FAJSP problem is compounded by multiple FJSP problems through assembly links,and The most important thing is to solve the constraints of the processing sequence of different workpieces.(2)Preliminary design of genetic algorithm.Adopt the idea of double-layer coding as a whole,and design a coding method based on the process constraint matrix to solve the problem of processing sequence constraints on different workpieces;choose different crossover and mutation operations for chromosome fragments of different coding methods;propose a Chromosome repair method to repair infeasible chromosomes that appear in the search process.By comparing the 20 sets of experimental data that use two different encoding methods to solve the scheduling problem,the genetic algorithm based on the process constraint matrix can better meet the processing sequence constraints between the processes,and can obtain a larger population space and increase In addition,the possibility of finding the optimal gene is obtained,so that a better scheduling scheme can be obtained.(3)Introduce multi-AGV to carry parts between different machines,and further study the scheduling problem of flexible assembly workshop.A multi-AGV scheduling strategy based on the principle of first processing first handling and nearest handling is proposed,and a multi-objective comprehensive scheduling mathematical model for FAJSP problems with multiple AGVs is established.Aiming at the shortcomings of genetic algorithm,a new selection strategy is designed to perform linear transformation on individual adaptation values and improve adaptive crossover/mutation operations.By comparing the results of using two genetic algorithms to solve the multi-objective integrated scheduling model: the improved adaptive genetic algorithm in this paper can significantly improve the search speed of the algorithm and enhance the search efficiency.