Genetic algorithm-based hybrid approach for optimal instance selection of minimising makespan in permutation flowshop scheduling

Abstract

Recently, the instance selection is getting more attention for the researchers to achieve enhanced performance of algorithms. A typical flowshop dataset can be represented in the form of a number of instances. The instances that are recorded during production process may not be a good example to learn useful knowledge. Therefore, the selection of high quality instances can be considered as a search problem and be solved by evolutionary algorithms. In this work, a genetic algorithm (GA) is proposed to select a sub-set of best instances. The selected instances are represented in the form of IF-Then else rules using a decision tree (DT) algorithm. The seed solution from DT is used as input to a scatter search (SS) algorithm for a few iterations, which acts as a local search to find the best value of the selected instances. The GA is used to select best instances in order to have a smaller tree size with good solution accuracy for minimizing makespan criterion in permutation flowshop scheduling. The computational experiments are performed with standard problems and compared against various existing literatures.