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.

Solving Flow Shop Scheduling Problem Considering Waiting Time Using Genetic Algorithm

2010 ◽  
Vol 139-141 ◽  
pp. 1684-1687 ◽  
Author(s):  
Hong Bing Wang ◽  
Ai Jun Xu ◽  
Dong Feng He ◽  
Lin Yao
Keyword(s):  
Genetic Algorithm ◽  
Continuous Casting ◽  
Waiting Time ◽  
Flow Shop ◽  
Fitness Function ◽  
Flow Shop Scheduling ◽  
Initial Population ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Steel Making

The real production scheduling problem between steel-making and continuous-casting can be modeled as Flow Shop Scheduling Problem. Waiting time must be considered for the reason of the temperature decrease of molten steel. The genetic algorithm with an initial population generation mechanism and a piecewise fitness function is proposed for solving the problem. The test results show that the proposed algorithm can find better solutions. A real case of steel-making and continuous-casting scheduling problem is computed and it shows the algorithm is effective.

scholarly journals A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5440 ◽  
Author(s):  
Monique Simplicio Viana ◽  
Orides Morandin Junior ◽  
Rodrigo Colnago Contreras
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Crossover Operator ◽  
Local Optima ◽  
Shop Scheduling ◽  
Solution Methods

It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

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