The Effect of Crossover and Mutation Operators on Genetic Algorithm for Job Shop Scheduling Problem

2012 ◽  
Vol 542-543 ◽  
pp. 1251-1259
Author(s):  
Long Xu ◽  
Wen Bin Hu
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
Scheduling Plan ◽  
Crossover And Mutation

Job Shop Scheduling Problem (JSSP) is a famous NP-hard problem in scheduling field. The concentration of JSSP is to find a feasible scheduling plan to figure out the earliest completion time under machine and processing sequence constraints. At present, genetic algorithm has been widely adopted in varies of operation research problems including JSSP, and good performance have been achieved. However, few work have stress the selection of varies operators when implemented for JSSP. Using benchmark problems, this paper compares the effect of crossover and mutation operators on genetic algorithm for JSSP.

scholarly journals Extended Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problem

2021 ◽  
Vol 10 (02) ◽  
pp. 017-020
Author(s):  
Sumaia E. Eshim ◽  
Mohammed M. Hamed
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Solution Quality ◽  
Shop Scheduling

In this paper, a hybrid genetic algorithm (HGA) to solve the job shop scheduling problem (JSSP) to minimize the makespan is presented. In the HGA, heuristic rules are integrated with genetic algorithm (GA) to improve the solution quality. The purpose of this research is to investigate from the convergence of a hybrid algorithm in achieving a good solution for new benchmark problems with different sizes. The results are compared with other approaches. Computational results show that a hybrid algorithm is capable to achieve good solution for different size problems.

Research of an Improved Genetic Algorithm for Job Shop Scheduling

2014 ◽  
Vol 1078 ◽  
pp. 417-421
Author(s):  
Guo Hua Zhou
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Three Dimensional ◽  
Scheduling Problem ◽  
Np Hard ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
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.

scholarly journals A layered genetic algorithm with iterative diversification for optimization of flexible job shop scheduling problems

2020 ◽  
Vol 15 (4) ◽  
pp. 377-389
Author(s):  
M.K. Amjad ◽  
S.I. Butt ◽  
N. Anjum ◽  
I.A. Chaudhry ◽  
Z. Faping ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Benchmark Problems ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Local Minima ◽  
Shop Scheduling

Flexible job shop scheduling problem (FJSSP) is a further expansion of the classical job shop scheduling problem (JSSP). FJSSP is known to be NP-hard with regards to optimization and hence poses a challenge in finding acceptable solutions. Genetic algorithm (GA) has successfully been applied in this regard since last two decades. This paper provides an insight into the actual complexity of selected benchmark problems through quantitative evaluation of the search space owing to their NP-hard nature. A four-layered genetic algorithm is then proposed and implemented with adaptive parameters of population initialization and operator probabilities to manage intensification and diversification intelligently. The concept of reinitialization is introduced whenever the algorithm is trapped in local minima till predefined number of generations. Results are then compared with various other standalone evolutionary algorithms for selected benchmark problems. It is found that the proposed GA finds better solutions with this technique as compared to solutions produced without this technique. Moreover, the technique helps to overcome the local minima trap. Further comparison and analysis indicate that the proposed algorithm produces comparative and improved solutions with respect to other analogous methodologies owing to the diversification technique.

Genetic Algorithm Design and Simulation for Job-Shop Scheduling Problem

2012 ◽  
Vol 157-158 ◽  
pp. 1436-1440
Author(s):  
Gui Cong Wang ◽  
Xi Jie Tian ◽  
Chuan Peng Li ◽  
Na Na Yang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Algorithm Design ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Scheduling Model ◽  
Chromosome Representation ◽  
Crossover And Mutation

This paper proposes an effective genetic algorithm for the job-shop scheduling problem (JSP) to minimize makespan time. An effective chromosome representation based on real coding is used to conveniently represent a solution of the JSP, and different strategies for selection, crossover and mutation are adopted. Simulation experimental results have shown that the scheduling model using the algorithm can allocate jobs efficiently and effectively.

SOLVING A MULTI-OBJECTIVE FLEXIBLE JOB SHOP SCHEDULING PROBLEM WITH TIMED PETRI NETS AND GENETIC ALGORITHM

2010 ◽  
Vol 02 (02) ◽  
pp. 221-237 ◽  
Author(s):  
HEJIAO HUANG ◽  
TAIPING LU
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Crossover And Mutation

The method presented in this paper is used to solve flexible job shop scheduling problem (JSP) with multiple objectives, which is much more complex than the classical JSP. Based on timed Petri net model, genetic algorithm is applied to solve the scheduling problems. The chromosomes are composed by sequences of transitions, the crossover and mutation operations are based on transition sequences. The experiment result shows that a definite solution to a specific flexible job shop scheduling problem can be found.

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