LPT scheduling for fuzzy tasks
Fuzzy Sets and Systems
Sequencing parallel machining operations by genetic algorithms
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
A multi-stage system in compilation environments
Fuzzy Sets and Systems
Fuzzy scheduling with application to real-time systems
Fuzzy Sets and Systems
Computers and Operations Research
The single machine ready time scheduling problem with fuzzy processing times
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
Computers and Industrial Engineering
Real-time task scheduling with fuzzy uncertainty in processing times and deadlines
Applied Soft Computing
Advances in Engineering Software
International Journal of Computer Applications in Technology
Fuzzy scheduling of job orders in a two-stage flowshop with batch-processing machines
International Journal of Approximate Reasoning
Computers and Industrial Engineering - Special issue: Selected papers from the 31st international conference on computers & industrial engineering
Advances in Engineering Software
Hi-index | 0.00 |
There are many scheduling problems which are NP-hard in the literature. Several heuristics and dispatching rules are proposed to solve such hard combinatorial optimization problems. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimization problems in view of its characteristic that has high efficiency and that is fit for practical application [1]. Two different scale numerical examples demonstrate the genetic algorithm proposed is efficient and fit for larger scale identical parallel machine scheduling problem for minimizing the makespan. But, even though it is a common problem in the industry, only a small number of studies deal with non-identical parallel machines. In this article, a kind of genetic algorithm based on machine code for minimizing the processing times in non-identical machine scheduling problem is presented. Also triangular fuzzy processing times are used in order to adapt the GA to non-identical parallel machine scheduling problem in the paper. Fuzzy systems are excellent tools for representing heuristic, commonsense rules. That is why we try to use fuzzy systems in this study.