Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches

2012 ◽  
Vol 50 (24) ◽  
pp. 7327-7342 ◽  
Author(s):  
M. Rabiee ◽  
M. Zandieh ◽  
P. Ramezani
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Nsga Ii ◽  
Shop Scheduling

scholarly journals A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Qianwang Deng ◽  
Guiliang Gong ◽  
Xuran Gong ◽  
Like Zhang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Nsga Ii ◽  
Shop Scheduling ◽  
Nondominated Sorting

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm withTiteration times is first used to obtain the initial populationN, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm withGENiteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

The Optimization of Flexible Job-Shop Scheduling Problem Based on NSGA-II

2013 ◽  
Vol 651 ◽  
pp. 684-687 ◽  
Author(s):  
Chuan Peng Li ◽  
Huan Yong Cui ◽  
Gui Cong Wang
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Maximum Load ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Nsga Ii ◽  
Shop Scheduling

An improved genetic algorithm based on NSGA-Ⅱnon-dominated sorting is proposed to solve flexible job-shop scheduling problem. Makespan and machine maximum load are chosen as objective function to establish multi-objective optimization model. Real encoding and plug-in decoding are used to transform chromosomes and scheduling schemes. NSGA-Ⅱnon-dominated sorting with elite reserved strategy is designed to improve search efficiency, and different strategies for selection, cross and mutation are adopted. The feasibility and effectiveness of the algorithm are verified by simulation and results from comparison with other algorithms.

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