An efficient job-shop scheduling algorithm based on particle swarm optimization
Expert Systems with Applications: An International Journal
An effective genetic algorithm for the flexible job-shop scheduling problem
Expert Systems with Applications: An International Journal
Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems
Computational Optimization and Applications
Non-identical parallel machine scheduling using genetic algorithm
Expert Systems with Applications: An International Journal
A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Optimizing replenishment polices using Genetic Algorithm for single-warehouse multi-retailer system
Expert Systems with Applications: An International Journal
A new hybrid genetic algorithm for job shop scheduling problem
Computers and Operations Research
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
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The genetic algorithm with search area adaptation (GSA) has a capacity for adapting to the structure of solution space and controlling the tradeoff balance between global and local searches, even if we do not adjust the parameters of the genetic algorithm (GA), such as crossover and/or mutation rates. But, GSA needs the crossover operator that has ability for characteristic inheritance ratio control. In this paper, we propose the modified genetic algorithm with search area adaptation (mGSA) for solving the Job-shop scheduling problem (JSP). Unlike GSA, our proposed method does not need such a crossover operator. To show the effectiveness of the proposed method, we conduct numerical experiments by using two benchmark problems. It is shown that this method has better performance than existing GAs.