Due to the rapid development of information technology and the impetus for more efficient and adaptive manufacturing processes, the concept of advanced manufacturing has become an increasingly prominent research topic across academia and industry in recent years. One critical aspect of advanced manufacturing is how to optimally cope with the complexities of multiple-objective decision-making to implement advanced manufacturing technologies with currently available enterprise resources and the realistic manufacturing conditions of a company. Generally, to successfully fulfill an advanced manufacturing plan, decision-makers must align short-term objectives with long-term strategies. In addition, the decision making process usually has to prioritize multiple-objective goals under a considerable number of uncertainties. This requirement presents new challenges for both planning and implementing advanced manufacturing technologies, and thus calls for new approaches for to better support such tasks. This paper proposes a knowledge representation and computational active learning-based framework for dealing with complex, multiple-objective decision-making problems for advanced manufacturing under realistic conditions. Through this study, we hope to shed light on using a simulation framework for multiple-objective decision support, thereby providing an alternative for manufacturing enterprises, which could lead to an acceptable optimal decision with reasonable cost and accuracy. First, we describe the scope of an advanced manufacturing system for industrial manufacturing. Next, we introduce systematic analysis of the complexities of the decision-making to implement advanced manufacturing. Finally, we propose a simulation model for the decision-making and formulate a computational active learning-based framework to efficiently compute goal priorities for multiple objective decision-making. We validate the framework by presenting a simulation of decision-making.
