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.

scholarly journals Improved Genetic Algorithm Integrated with Scheduling Rules for Flexible Job Shop Scheduling Problems

2021 ◽  
Vol 243 ◽  
pp. 02010
Author(s):  
Muhammad Kamal Amjad ◽  
Shahid Ikramullah Butt ◽  
Naveed Anjum
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Improved Genetic Algorithm ◽  
Priority Rules ◽  
Data Set ◽  
Shop Scheduling

This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.

scholarly journals An Improved Genetic Algorithm with Adaptive Variable Neighborhood Search for FJSP

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 243
Author(s):  
Xiaolin Gu ◽  
Ming Huang ◽  
Xu Liang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Method ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Neighborhood Search ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling

For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems.

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