Feature Selection for Evolving Many-Objective Job Shop Scheduling Dispatching Rules with Genetic Programming
Atiya Masood, Gang Chen, Mengjie Zhang · 2021
JSS (Job Shop Scheduling) is a significant and challenging combinatorial optimization issue. Dispatching rules have been successfully used to determine scheduling decisions in the JSS challenges. Genetic programming (GP) has been widely used to discover and develop dispatching rules for various scheduling problems. However, there has been relatively little research into feature selection in GP-HH for many-objective JSS. In many conflicting objective contexts, it's also vital to quantify the contribution of features. This work presents a new two-stage GP-HH methodology for many-objective JSS with feature selection for changing rules. The quality of the solutions (dispatching rules) after incorporating the many-objective algorithm with feature selection is investigated in this paper. On a four-objective JSS problem, the suggested algorithm (FS-GP-NSGA-III) is compared to the standard GP-NSGA-III. The experimental results show that using GP to pick relevant features improves the algorithm's performance. Furthermore, the proposed technique generates rules that are minimal in size and easy to understand.