Scheduling for the Flexible Job-Shop Problem Based on Genetic Algorithm (GA)

2012 ◽  
Vol 457-458 ◽  
pp. 616-619
Author(s):  
Shun Cheng Fan ◽  
Jin Feng Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimal Solution ◽  
Initial Population ◽  
Flexible Job Shop ◽  
Chromosome Representation ◽  
Local Optimal Solution ◽  
Crossover And Mutation ◽  
Flexible Job Shop Problem

In this paper, we analyze the characteristics of the flexible job-shop scheduling problem(FJSP). A novel genetic algorithm is elaborated to solve the FJSP. An improved chromosome representation is used to conveniently represent a solution of the FJSP. Initial population is generated randomly. The relevant selection, crossover and mutation operation is also designed. It jumped from the local optimal solution, and the search area of solution is improved. Finally, the algorithm is tested on instances of 4 jobs and 6 machines. Computational results prove the proposed genetic algorithm effective for solving the FJSP.

Research on Improved Genetic Algorithm Solving Flexible Job-Shop Problem

2012 ◽  
Vol 479-481 ◽  
pp. 1918-1921 ◽  
Author(s):  
Min Shuo Li ◽  
Ming Hai Yao
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Improved Genetic Algorithm ◽  
Total Delay ◽  
Retention Policy ◽  
Selection Operator ◽  
Flexible Job Shop Problem ◽  
Random Method

Based on the analyzing of the characteristic of the flexible job-shop scheduling problem (FJSP), we proposed an improved genetic algorithm. To consider the max finish-time, total delay-time, keeping workload balance among the machines, a new selection operator is proposed, which combines random method, proportion-based selection method with elitist retention policy. The improved genetic algorithm using the proposed selection operator is tested on some standard instances. The experimental results validate the effectiveness of the proposed algorithm.

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.

Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization

2013 ◽  
Vol 701 ◽  
pp. 364-369 ◽  
Author(s):  
Wayan F. Mahmudy ◽  
Romeo M. Marian ◽  
Lee H.S. Luong
Keyword(s):  
Genetic Algorithms ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Initial Population ◽  
Flexible Job Shop ◽  
Test Bed ◽  
Genetic Operators ◽  
Chromosome Representation ◽  
Proper Values ◽  
Real Coded Genetic Algorithms

This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple genetic operators and random initial population, the proposed RCGA can produce promising results comparable to those achieved by other best-known approaches in the literatures. These results demonstrate the robustness of the RCGA.

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