Multi-Objective Evolutionary Job-Shop Scheduling Using Jumping Genes Genetic Algorithm
K.S.N. Ripon, Chi-Ho Tsang, Sam Kwong · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
The Job-Shop Scheduling Problem (JSSP) is a hard combinatorial optimization problem. Several evolutionary approaches have been proposed to solve JSSP. But most of them are limited to single objective and fail in real-world applications, which naturally involve multiple objectives. In this paper, we present an evolutionary approach for solving multi-objective JSSP using Jumping Genes Genetic Algorithm (JGGA) that heuristically searches for the near-optimal solutions optimizing multiple criteria simultaneously. Experimental results reveal that our proposed approach can search for the near-optimal solutions by optimizing multiple criteria and also capable of finding a set of diverse and non-dominated scheduling solutions.