An improved genetic algorithm for the integrated satellite imaging and data transmission scheduling problem

2021 ◽  
pp. 105626
Author(s):  
Jiawei Zhang ◽  
Lining Xing
Keyword(s):  
Genetic Algorithm ◽  
Data Transmission ◽  
Scheduling Problem ◽  
Transmission Scheduling ◽  
Improved Genetic Algorithm ◽  
Satellite Imaging

Solving Steel-Making and Continuous-Casting Scheduling Problem with Fuzzy Processing and Delivery Time Using Genetic Algorithm

2010 ◽  
Vol 139-141 ◽  
pp. 1679-1683 ◽  
Author(s):  
Hong Bing Wang ◽  
Ai Jun Xu ◽  
Dong Feng He
Keyword(s):  
Genetic Algorithm ◽  
Continuous Casting ◽  
Production Scheduling ◽  
Scheduling Problem ◽  
Test Results ◽  
Delivery Time ◽  
Crossover Operator ◽  
Improved Genetic Algorithm ◽  
Steel Making ◽  
Early Maturing

The real production scheduling problem between steel-making and continuous-casting can be modeled as JSSP with fuzzy processing and delivery time. An improved genetic algorithm is proposed for solving this problem and the improved aspects include the mechanism for preventing early-maturing and the job filter order-based crossover operator. The test results show that the improved genetic algorithm can find better solutions than other three algorithms. A real production scheduling problem of steel-making and continuous-casting is computed using the improved genetic algorithm and it shows the algorithm is effective.

Improved Genetic Algorithm Based on Operation Order Matrix Encoding for Job Shop Scheduling Problem

2011 ◽  
Vol 189-193 ◽  
pp. 4212-4215
Author(s):  
Hong Zhan ◽  
Jian Jun Yang ◽  
Lu Yan Ju
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Order Matrix ◽  
Fitness Value ◽  
Matrix Encoding

This paper presents an improved genetic algorithm for the job shop scheduling problem. We designed a new encoding method based on operation order matrix, a matrix correspond to a chromosome, the value of elements is not repetitive, that means a processing order number in all operations of all jobs. Aiming at the features of the matrix encoding, we designed the crossover and mutation methods based on jobs, and the infeasible solutions are avoided. Through adjusting the computing method of fitness value, the improved genetic algorithm takes on some self adapting capability. The proposed approach is tested on some standard instances and compared with two other approaches. The computation results validate the algorithm is efficient.

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