Abstract
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. © 2006 IEEE.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Pages | 3100-3107 |
| Publication status | Published - 2006 |
| Externally published | Yes |