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The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Traditional design methods heavily rely on domain expert knowledge using documents to define assembly solutions which are later verified through simulations. However, these document-centric approaches cannot provide graphical notations for engineers to efficiently understand the entire assembly process. Moreover, it is difficult to analyze the performance of the designed assembly processes using simulations since the simulation models have to be developed based on the documents manually rather than be generated automatically from the design models. In this paper, a semantic-driven approach is proposed to support aircraft assembly process formalism and performance analysis. First, meta-models of aircraft assembly processes are developed based on SysML and discrete-event simulation models using a semantic modeling language named KARMA. Then an application ontology is defined for generating semantic models from KARMA architecture models to capture domain knowledge, system requirements and simulation model information of the aircraft assembly process. A model transformer is developed to transform the KARMA models to discrete-event simulation models based on the application ontology. Then the generated simulation models are executed to obtain the simulation results for verifying the designed assembly process. Finally, the obtained simulation results are used to support decision-making of selecting the optimal aircraft assembly process. A case study is conducted to verify the proposed method. [ABSTRACT FROM AUTHOR]

In the early stages of health attention, such as in the examination rooms, patients are generally treated using a First In First Treat (FIFT) logic. This treatment style is inefficient from an operational view as it leads to long waiting times for critical patients. Based on optimization and discrete-event simulation, this paper introduces a hybrid model to assign patients priorities and exam rooms. Patients arrive to the system following a probability function, and for every discrete interval, a mixed-integer linear optimization model is used to allocate patients, considering health priority, preparation times, and availability of time and resources. Instances of the problem are defined using information from a health system in Mexico. Additional to the linear model, a heuristic algorithm is proposed, composed of two phases and aims to provide good, feasible, and fast solutions without ensuring optimality. Computational experiments showed that the linear model is more appropriate for small instances since the heuristic is faster and the relative deviations with respect to the linear model are below five percent. Finally, the discrete-event simulation is run for two cases: (1) with a FIFT logic and (2) using the optimization model. The hybrid model reduces the global waiting times up to 90% and the number of high health priority patients who are not treated up to 100%. • A mixed-integer linear model and a heuristic algorithm are used to allocate patients. • Optimization-simulation model reduced waiting times in primary health centers. • Optimization-simulation model reduced the number of non-treated patients. • The hybrid model is applied in a real Mexico's health system. [ABSTRACT FROM AUTHOR]