A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem

2010 ◽  
Vol 51 (5-8) ◽  
pp. 757-767 ◽  
Author(s):  
Xiaojuan Wang ◽  
Liang Gao ◽  
Chaoyong Zhang ◽  
Xinyu Shao
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective Genetic Algorithm ◽  
Entropy Principle

An Improved Genetic Algorithm for Multi-Objective Flexible Job-Shop Scheduling Problem

2010 ◽  
Vol 97-101 ◽  
pp. 2449-2454 ◽  
Author(s):  
Chao Yong Zhang ◽  
Xiao Juan Wang ◽  
Liang Gao
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Entropy Principle

Flexible job shop scheduling problem (FJSP) is an extended traditional job shop scheduling problem, which more approximates to real scheduling problems. This paper presents a multi-objective genetic algorithm (GA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved multi-objective GA, the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Advanced crossover and mutation operators are proposed to adapt to this special chromosome structure. The proposed algorithm is evaluated on three representative instances and the computational results and comparison with some other approaches show that the proposed multi-objective algorithm is effective and potential.

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