Semantic Modeling for the Knowledge Framework of Computational Experiments and Decision Making for Supply Chain Networks

The complexity of a supply chain network (SCN) is rooted in its complex structures, multiple decision-making (DM) entities, adaptive behaviors, and open environments. Due to its unique advantages, computational experiment (CE) has been increasingly adopted as one of the most important methods for SCN complexity research. Data that are generated by computational experiments must be analyzed using effective tools. Depending on this analysis, DM acts as an important analysis and selection mechanism for the optimization and design of SCNs. This optimization and design rely on the combination of CE and DM. This combination inevitably involves multiple types of knowledge in the domains of SCNs, CE, and DM, which have been less comprehensively considered in recent studies. It remains a challenge for researchers and practitioners to clarify the knowledge system of SCNs and select the most suitable research perspectives, paradigms, and methods for CEs and DM of SCNs. To confront this challenge, it is necessary to systematically model the semantics of the knowledge that is involved in CE and DM to realize the consistency and interoperability of models, methods, and processes. Therefore, this paper uses a semantic network approach to construct a semantic model to clarify the knowledge framework of CEs and DM of SCNs. This knowledge framework is composed of the important knowledge elements that are extracted from the domains of SCNs, CE, and DM. The application procedure of the semantic model is demonstrated on a four-echelon SCN case. The semantic model's understandability, consistency, reusability, procedure, systematization, and linkage analysis capability are evaluated. The results demonstrate that the semantic model is effective in providing a consistent, procedural, and systematic perspective for SCN complexity research and supporting linkage analysis among SCN modeling, CE, and DM.