Open AccessResearch on Flexible Job Shop Scheduling Problem Based on Improved Genetic Algorithm
Author(s) -
Jingcao Cai,
Lei Wang,
Yi-Peng XING
Publication year - 2017
Publication title -
destech transactions on engineering and technology research
Language(s) - English
Resource type - Journals
ISSN - 2475-885X
DOI - 10.12783/dtetr/mdm2016/4877
Subject(s) - crossover , job shop scheduling , genetic algorithm , mathematical optimization , computer science , operator (biology) , mutation , mechanism (biology) , algorithm , evolutionary algorithm , mathematics , artificial intelligence , epistemology , transcription factor , gene , biochemistry , operating system , schedule , philosophy , chemistry , repressor
An improved algorithm is proposed based on a basic genetic algorithm to enhance solution quality for solving FJSP in this paper. The improvements include strategies for crossover and mutation operator, directed evolutionary mechanism and resurrection strategy. The improved algorithm is tested on an instance of 10 jobs and 10 machines for FJSP. The computational results indicate that the proposed improved algorithm is effective for solving FJSP.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom