Abstract Paper aims To propose a framework to support online simulation studies considering facilitated modeling and concepts of modern industry context, such as agility and flexibility. Originality Since the frameworks in the literature deal with simulation projects focused on healthcare and face-to-face meetings, the present work innovates by offering an agile and flexible guide for simulation projects in production systems, which also supports online interventions. Research method Action Research method was used to develop the framework. After its development, the FaMoSim (Facilitated Modeling Simulation) framework was applied in a real case to evaluate its applicability. Main findings In the application of FaMoSim, we achieved the framework's objectives: carrying out a faster (up to 3 months) and more flexible online modeling process; creating a simple computer model that does not require a complex data collection structure nor a specialist team; generating a better understanding of the process and assisting the stakeholders in identifying improvements. Implications for theory and practice Considering some challenges that prevent the expansion of DES studies, the framework assists in expanding DES studies in environments where it is not widely used. The framework supports online interventions, making it an interesting tool that can be used mainly in times of social distancing.

Abstract Paper aims Propose a continuous decision support system, a Digital Twin, integrating two widely used techniques, Discrete Event Simulation and forecasting methods. Originality With the evolution of the industry, there is a growing need for increasingly agile and assertive decision support systems. Also, familiar tools and techniques tend to change over time to suit such a scenario, supporting new researches on their use in the modern industry. Research method The proposed method allows the use of simulation, with the aid of forecasting methods, for continuous decision making, composing the so-called Digital Twin. The method was applied in a real process to validate it. Main findings The Moving Average, Single Exponential Smoothing, and Double Exponential Smoothing forecasting methods were used to supply the simulation model in order to test scenarios and guide decision making. The developed system enabled a virtual copy with a certain degree of intelligence and that provides answers to make the constant decision-making process more efficient. Implications for theory and practice The proposed method can be used for several operational problems like headcount, production planning and covers different levels of decision. Therefore, it can be used both on the shop floor and at managerial levels.

Resumo Durante a condução do projeto de simulação, analistas e clientes adquirem um maior conhecimento do que está sendo simulado, porém grande parte deste conhecimento é perdido, devido à ausência de formas de se retê-lo. Em boa parte, isto ocorre devido à falta de discussão com relação aos tipos de conhecimento presentes nas etapas de um projeto de simulação. Nesse contexto, este trabalho tem como objetivo apresentar um estudo de caso, a fim de conhecer como são conduzidos os projetos e identificar os tipos de conhecimento gerados durante sua realização, em empresas de consultoria em simulação. Para isto, foram escolhidas três empresas que trabalham com simulação, de pequeno, médio e grande porte. Como resultado, identificou-se que em cada etapa do projeto de simulação estão presentes os quatro tipos de conhecimento estabelecidos na espiral de Nonaka e Takeuchi. Estes resultados auxiliam os analistas na documentação e gestão do conhecimento e contribuem para evitar erros.

Abstract During a simulation project, modelers and customers acquire a greater knowledge of what is being simulated. However, much of this knowledge is lost due to the lack of ways to retain it and due to the lack of discussion about the types of knowledge present in the stages of a simulation project. In this context, this paper aims to present a case study to know how the projects are conducted and to identify the types of knowledge generated during their execution. The case study was performed in simulation consulting firms. Three companies specialized in the simulation were selected: a small one, a medium one, and a large one. It was identified that the four types of knowledge, established by the Nonaka and Takeuchi are present in each step of the simulation project. These results assist modelers in documenting and managing the knowledge and help them to avoid errors.

Abstract This study presents the use of a serious game developed to teach Lean philosophy. The structure of this game was built from theoretical elements and predefined learning events. Learning outcomes and student motivation were considered in the evaluation of the efficacy of the game. This serious game was applied to four groups of students with different profiles. The evaluation results were compared among these groups of students. It can be concluded that the serious game developed showed positive results in learning and motivation demonstrated by the students, regardless of the group analyzed. The main contributions to the literature presented in this article were the serious game (named 3L) that was developed and the efficacy evaluation method, considering the learning and motivation demonstrated in different profiles of students.

O presente artigo apresenta um estudo bibliométrico sobre o tema gestão do conhecimento em projetos de simulação. Neste trabalho foi realizada uma análise envolvendo a base de dados ISI - Web of Science, identificando os principais autores, países e fontes de publicação do tema. Foi utilizado o "mapa de citações" para apresentar ao leitor como estão relacionadas as citações sobre o tema pesquisado. Foram utilizados também dois softwares de apoio a bibliometria: Stikis(r) e Ucinet(r), para identificar as principais palavras-chave do tema, visto que as bases de dados não oferecem este recurso. O procedimento da pesquisa se fundamentou em coletar os dados na base ISI - Web of Science e conduzir as análises, extraindo dessa maneira os resultados. O trabalho buscou identificar como se encontra este campo de pesquisa no meio acadêmico, e oferecer a potenciais pesquisadores da área, uma base consistente de informações para estudos sobre este tema. Ao final do trabalho, pode-se obter um conjunto de informações sobre o tema gestão do conhecimento em projetos de simulação. Apesar da importância do tema pesquisado, verificaram-se poucos trabalhos que exploram estes assuntos em conjunto, indicando ser uma área ainda a ser explorada por novas publicações.

This article presents a bibliometric study on the knowledge management in simulation projects. In this article was performed an analysis involving the ISI database - Web of Science, identifying the main authors, countries and research topic sources. We used the "citation map" to introduce to the reader how the citations are related on the subject searched. And also used two software support bibliometrics: Stikis(r) and Ucinet(r) to identify the main research topic keywords, since the databases do not offer this feature. The research procedure was based on collecting the data in ISI - Web of Science and conducting analysis, extracting the results. The article tried to identify how is this field of research in academia, and offer the potential researchers in the field, a consistent information base for studies on this matter. At the end of the article, a set of information on knowledge management in simulation projects is available. Despite the importance of the research theme, there have been few studies that explore these issues together, indicating that an area has yet to be explored by new publications.

A Programação por Metas, ou Goal Programming (GP), é uma importante abordagem analítica que tem sido aplicada com sucesso na solução de importantes problemas de decisão. Propõe-se um modelo de Programação por Metas Binária-Mista, ou Mixed-Binary Goal Programming (MBGP), para otimizar o planejamento agregado da produção e da distribuição de uma empresa do setor sucroenergético, que incorpora a cogeração de energia. Deste modo, o modelo aborda os processos de produção de açúcar, de álcool, de melaço e derivados e da cogeração de energia. Contempla, também, as etapas agrícolas, industriais e de distribuição, possibilitando que as decisões sejam tomadas em um horizonte de planejamento semanal, incluindo a safra e entressafra. A aplicação deste modelo em uma usina de açúcar e álcool brasileira auxiliou na determinação de políticas ótimas de produção e distribuição, gerando resultados financeiros e operacionais interessantes para a empresa, além de gerar relatórios de exportação de energia aderentes à realidade da usina estudada.

The Goal Programming (GP) is an important analytical approach that has been successfully applied in relevant real-world decision making problems. This paper proposes a Mixed-Binary Goal Programming (MBGP) model to optimize production and distribution planning of a energy cogeneration sugar and ethanol milling company. Therefore, the MBGP model integrates the production process of sugar, alcohol, molasses, and energy cogeneration, and it enables decision-making in the agricultural, industrial, and distribution phases on a weekly-basis planning horizon including the harvest season and the periods between harvests. The model was applied in a Brazilian sugar and ethanol milling company, and it contributed to the formulation of production and distribution optimum policies yielding good financial and operational results in addition to raising energy export in conformity with the reality of the plant.

Optimization methods combined with computer-based simulation have been utilized in a wide range of manufacturing applications. However, in terms of current technology, these methods exhibit low performance levels which are only able to manipulate a single decision variable at a time. Thus, the objective of this article is to evaluate a proposed optimization method for discrete-event simulation models based on genetic algorithms which exhibits more efficiency in relation to computational time when compared to software packages on the market. It should be emphasized that the variable's response quality will not be altered; that is, the proposed method will maintain the solutions' effectiveness. Thus, the study draws a comparison between the proposed method and that of a simulation instrument already available on the market and has been examined in academic literature. Conclusions are presented, confirming the proposed optimization method's efficiency.

As the number of simulation experiments increases, the necessity for validation and verification of these models demands special attention on the part of the simulation practitioners. By analyzing the current scientific literature, it is observed that the operational validation description presented in many papers does not agree on the importance designated to this process and about its applied techniques, subjective or objective. With the expectation of orienting professionals, researchers and students in simulation, this article aims to elaborate a practical guide through the compilation of statistical techniques in the operational validation of discrete simulation models. Finally, the guide's applicability was evaluated by using two study objects, which represent two manufacturing cells, one from the automobile industry and the other from a Brazilian tech company. For each application, the guide identified distinct steps, due to the different aspects that characterize the analyzed distributions