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.

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.

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.

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.

Association Rule Mining for Job Shop Scheduling Problem Based on Genetic Algorithm

2012 ◽  
Vol 629 ◽  
pp. 730-734 ◽  
Author(s):  
Cun Liang Yan ◽  
Wei Feng Shi
Keyword(s):  
Genetic Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Rule Mining ◽  
Concept Hierarchy ◽  
Shop Scheduling

Job shop scheduling problem (JSP) is the most typical scheduling problem, In the process of JSP based on genetic algorithm (GA), large amounts of data will be produced. Mining them to find the useful information is necessary. In this paper dividing, hashing and array (DHA) association rule mining algorithm is used to find the frequent itemsets which contained in the process, and extract the corresponding association rules. Concept hierarchy is used to interpret the rules, and lots of useful rules appeared. It provides a new way for JSP study.

The Study of Job Shop Scheduling Based on Genetic Algorithm

2011 ◽  
Vol 201-203 ◽  
pp. 795-798
Author(s):  
Jun Xing Xiong ◽  
Jin Ping Zhao ◽  
Hai Ning Tu
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Aiming at Job Shop Scheduling Problem with Minimal Makespan, This paper is designed to use genetic algorithm to solve the problem of job shop scheduling, and also achieves the algorithm by using the C#. Application shows that the genetic algorithm to solve job shop scheduling problem is efficient and has good application value.

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