'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
A multiobjective genetic algorithm (MOGA) is applied to a test batch scheduling problem to optimize five objectives simultaneously. The design of the MOGA allows an emphasis on human interaction with the optimization process, including the ability to change priorities of preferences and plant data interactively, and to allow the MOGA to make decisions regarding batch size and the rule task allocation. Experimental results demonstrate the development of this technique, allowing the combination of human expertise and MOGA optimization power to provide scheduling solutions to a highly complex problem
