A hyper-heuristic multi-criteria decision support system for eco-efficient product life cycle

Decision support is required when complex situations arise during product development which takes into account the whole product life cycle. This is especially true when impacted by the ill-defined consequences on the environment in an ever increasingly eco-conscious world. Analytical Hierarchy process (AHP) is one method of providing decision support, and is an instance of a decision support heuristic. Machine learning methods have proved themselves on many well defined problems with clearly defined objectives. In particular, we focus on the recently emerging field of hyper-heuristics which is a blend of human designed heuristics, with the extension of machine designed heuristics. In essence humans can operate at the higher concept or abstract level, while machine heuristics can operate at a lower level. There are a number of issues within the proposed framework, including visualizing a multi-dimensional surface of designs along the Pareto front, as well as dealing with different types of data during the decision making process. It is proposed that Hyper-heuristics, supplemented with other methodologies to deal with vague or missing data, offer a framework in which to begin to address several of the complex compromises that arise during product development.