Solving integrative matching model of inventory in continuous casting and hot rolling processes by improved genetic algorithm

2012 ◽  
Author(s):  
Haitao Li ◽  
Sujian Li ◽  
Di Wu
Keyword(s):  
Genetic Algorithm ◽  
Continuous Casting ◽  
Hot Rolling ◽  
Improved Genetic Algorithm ◽  
Matching Model

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.

Hot Rolling Batch Planning Problem Model Based on Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 2042-2046
Author(s):  
Hai Tao Li ◽  
Su Jian Li ◽  
Di Wu ◽  
Fang Han ◽  
Fang Wang
Keyword(s):  
Genetic Algorithm ◽  
Hot Rolling ◽  
Production Scheduling ◽  
Steel Production ◽  
Planning Problem ◽  
Planning Model ◽  
Improved Genetic Algorithm ◽  
Problem Model ◽  
Iron And Steel ◽  
Batch Planning

To solve the hot rolling batch planning problem in production scheduling of iron and steel enterprises, a hot rolling batch planning model is formulated based on multiple travelling salesmen problem(MTSP) model. The objective is to minimize the total limit penalty value of adjacent stripped steels in width, thickness and hardness. The main constraints include jumps in width, thickness and hardness between adjacent stripped steels, which are essential for steel production process. An improved genetic algorithm is designed to solve the model. A simulation example shows the reasonability of the model and validity of the algorithm.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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