Bridging the Gap between Rich Supply Chain Problems and the Effective Application of Metaheuristics through Ontology-Based Modeling

Supply chains (SC) are exposed to dynamic markets and enlarged network structures. This induces abundant decision complexity and the need to frequently adapt decisions. Metaheuristics are most suitable for rich SC optimization problems. However, effectiveness and adaptability of these approaches are impaired through extensive modeling efforts and intricate data representation issues. Therefore we propose using ontological modeling to mitigate these disadvantages.