Research of an Improved Genetic Algorithm for Job Shop Scheduling

Shop Scheduling ◽

Crossover And Mutation

job shop scheduling is one of the most difficult NP-hard combinatorial optimize problems, in order to solve this problem, an improved Genetic Algorithm with three- dimensional coded model was put forward in this paper. In this model, the gene was coded with 3-D space, and self-adapting plot was drawn into conventional GA, then the probability of crossover and mutation can automatic adjust by fit degree. The instance shows that this algorithmic is effective to solve job shop scheduling problem.

2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT) ◽

An improved genetic algorithm using opposition-based learning for flexible job-shop scheduling problem

10.1109/cciot.2016.7868294 ◽

2016 ◽

Cited By ~ 3

Author(s):

Huang Ming ◽

Mingxu Wang ◽

Xu Liang

Keyword(s):

Genetic Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Flexible Job Shop ◽

Scheduling Problem ◽

Flexible Job Shop Scheduling ◽

Shop Scheduling ◽

Opposition Based Learning

An Improved Genetic Algorithm for Flexible Job Shop Scheduling Problem with Buffer Constraints

10.23919/icac50006.2021.9594239 ◽

2021 ◽

Author(s):

Mingzhu Hu ◽

Haojie Chen ◽

Huan Luo ◽

Jian Zhang ◽

Shuying Wang

Keyword(s):

Genetic Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Flexible Job Shop ◽

Scheduling Problem ◽

Flexible Job Shop Scheduling ◽

Shop Scheduling

Solving Flexible Job Shop Scheduling Problem in Cloud Manufacturing Environment Based on Improved Genetic Algorithm

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/612/4/042065 ◽

2019 ◽

Vol 612 ◽

pp. 042065

Author(s):

Yunlong Li ◽

Guofu Luo

Keyword(s):

Genetic Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Cloud Manufacturing ◽

Flexible Job Shop ◽

Scheduling Problem ◽

Flexible Job Shop Scheduling ◽

Shop Scheduling

Improved Genetic Algorithm Based on Operation Order Matrix Encoding for Job Shop Scheduling Problem

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.189-193.4212 ◽

2011 ◽

Vol 189-193 ◽

pp. 4212-4215

Author(s):

Hong Zhan ◽

Jian Jun Yang ◽

Lu Yan Ju

Keyword(s):

Genetic Algorithm ◽

Job Shop ◽

Job Shop Scheduling ◽

Scheduling Problem ◽

Shop Scheduling ◽

Order Matrix ◽

Fitness Value ◽

Matrix Encoding

This paper presents an improved genetic algorithm for the job shop scheduling problem. We designed a new encoding method based on operation order matrix, a matrix correspond to a chromosome, the value of elements is not repetitive, that means a processing order number in all operations of all jobs. Aiming at the features of the matrix encoding, we designed the crossover and mutation methods based on jobs, and the infeasible solutions are avoided. Through adjusting the computing method of fitness value, the improved genetic algorithm takes on some self adapting capability. The proposed approach is tested on some standard instances and compared with two other approaches. The computation results validate the algorithm is efficient.