A variable neighborhood search based genetic algorithm for flexible job shop scheduling problem

2018 ◽  
Vol 22 (S5) ◽  
pp. 11561-11572 ◽  
Author(s):  
Guohui Zhang ◽  
Lingjie Zhang ◽  
Xiaohui Song ◽  
Yongcheng Wang ◽  
Chi Zhou
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

scholarly journals Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880409 ◽  
Author(s):  
Rui Wu ◽  
Yibing Li ◽  
Shunsheng Guo ◽  
Wenxiang Xu
Keyword(s):  
Genetic Algorithm ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker’s learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.

An improved SFLA for flexible job shop scheduling problem considering energy consumption

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840112 ◽  
Author(s):  
Xiaoxing Zhang ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.

Sign in / Sign up

Export Citation Format

Share Document