MATLAB-based genetic algorithm to optimize hybrid flow shop scheduling problem
The hybrid flow shop scheduling problem is a common optimization problem in workshop production. It aims to maximize production efficiency and reduce production costs by reasonably arranging job sequences and machine allocation. This article will introduce how to use MATLAB to write a genetic algorithm to solve the hybrid flow shop scheduling problem, and provide the corresponding source code.
Problem description:
Suppose there is a hybrid flow shop consisting of multiple workstations (machines) and a batch of jobs to be processed. Each workstation has a certain amount of processing power and processing time, and each job has specific processing needs and priorities. Jobs need to be processed in order of their priority and distributed among workstations to maximize productivity across the shop floor.
The basic principle of genetic algorithm:
Genetic algorithm is an optimization algorithm based on the principle of biological evolution, which searches for the optimal solution by simulating mechanisms such as natural selection, crossover, and mutation. In the hybrid flow shop scheduling problem, the job sequence can be regarded as a chromosome, and each job corresponds to a gene on the chromosome. The genetic algorithm generates new chromosomes by performing crossover and mutation operations on chromosomes, and evaluates their quality according to the fitness of the chromosomes, thereby gradually optimizing the quality of the solution.
MATLAB code implementation:
The following is the source code for solving the hybrid flow shop scheduling optimal problem using a genetic algorithm written in MATLAB.
% 初始化参数
populationSize = 50; % 种群大小
maxGenerations = 100