Towards Self-Adaptive Discrete Event Simulation (SADES)

Systems that benefit from the ongoing use of simulation, often require considerable input by the modeller(s) to update and maintain the models. This paper proposes automating the evolution of the modelling process for discrete event simulation (DES) and therefore limiting the majority of the human modeller’s input to the development of the model. This mode of practice could be named Self-Adaptive Discrete Event Simulation (SADES). The research is driven from ideas emerging from simulation model reuse, automations in the modelling process, real time simulation, dynamic data driven application systems, autonomic computing and self-adaptive software systems. This paper explores some of the areas that could inform the development of SADES and proposes a modified version of the MAPE-K feedback control loop as a potential process. The expected outcome from developing SADES would be a simulation environment that is self-managing and more responsive to the analytical needs of real systems

From COTS Simulation Software to an Open-source Platform: A Use Case in the Medical Device Industry

AbstractThe implementation of Discrete Event Simulation (DES) – based decision support tools in complex manufacturing environments could prove of invaluable help to industrial practitioners involved in cross-functional decision processes at multiple hierarchical levels. The increasing number of decision variables, their stochastic nature and the non-linearity of their mutual relationships theoretically make simulation a preferred modelling approach for a great variety of manufacturing systems as strict simplifying assumptions are not necessarily required and the models’ detail level can be tuned according to the analysis purposes. However, recourse to Commercial Off-The-Shelf (COTS) simulation packages to develop and implement simulation-based solutions in real manufacturing environments usually presents significant cost-of-ownership (COO). Along with license costs, modelling flexibility and sustainability represent fundamental issues raised by industrial engineers that adopt COTS simulation packages. In order to promote the use of DES in production related decision making processes and reduce the associated COO for manufacturing companies, an open-source simulation platform, ManPy, has been developed. ManPy consists of a library of DES objects implemented in SimPy. ManPy's scope is to provide modellers with generic, highly customizable open-source simulation objects that can be connected to form a model in the same fashion of COTS simulation packages. ManPy's on-going development is based on guidelines provided by the analysis of real industrial use cases. Specific pilot models developed in SimPy are used to identify new objects and relevant features to be incorporated in ManPy in order to make it a highly flexible simulation tool. In this article, a use case based on a labour intensive serial production line operating in a medical device manufacturing plant is described. Insights for the transition from a COTS simulation model to a specific SimPy model and finally to generic ManPy objects are presented

Tehdassimuloinnin lähtötietojen tunnistaminen ja kerääminen

Simulointi on erinomainen työkalu tuotantojärjestelmien analysointiin ja tehokkuuden parantamiseen. Nykyaikaisessa yritysmaailmassa on kova kysyntä kyvylle luoda ja toimittaa tehokkaita ja käyttökelpoisia simulaatiomalleja. Kuitenkin simulointimallien nopea kehittäminen ja käyttöönotto on estynyt tehottoman tiedonkeruun, pitkän mallidokumentaation ja huonon suunnittelun takia. Tehdassimulointiprojektit ja simulointimallit ovatkin vahvasti riippuvaisia korkeasta lähtötietojen laadusta, ja simulointiprojekteista iso osa kuluu tietojenkeräämiseen. Tutkimuksen tavoitteena on selkeyttää simulointiprojektiin liittyvää tiedonkeräämistä ja näin ollen tehostaa simulointiprojektien suorittamista. Tutkimuksessa selvitettiin, mistä tunnistetaan tarvittavat lähtötiedot tehdassimulointiprojektin alussa, ja miten lähtötietoja kerätään tehdassimulointiprojektin alussa. Työssä myös tutkittiin, kuinka lähtötietojen kerääminen ja muut keskeiset tekijät, kuten ongelman määrittely ja mallin monimutkaisuus sekä simulointiprojektin johtaminen vaikuttavat tehdassimuloinnin onnistumiseen. Tutkimuksessa tutkimus suoritettiin kirjallisuuskatsauksena. Kirjallisuuskatsaus suoritettiin etsimällä aiheeseen liittyvää kirjallisuutta Andor-palvelusta sekä Google Scholarista, kirjallisuudessa keskityttiin vertaisarvioituihin ja luotettaviin tieteellisiin lähteisiin, kuten kirjoihin ja artikkeleihin, sekä konferenssijulkaisuihin. Kirjallisuutta rajattiin keskittymällä simulointiprojekteihin sekä jatkuvaan simulointiin. Kirjallisuuskatsauksen tuloksena selvisi, että tärkeimmät vaiheet lähtötietojen määrittämisen kannalta simulointiprojektissa ovat ongelman ja tavoitteiden määrittäminen, käsitteellisen mallin muodostaminen sekä oikean systeemin ymmärtäminen. Oikean systeemin ymmärtäminen tukee käsitteellisen mallin muodostamista, josta simulointimallin tietovaatimukset ovat tunnistettavissa. Lähtötietojen keräämisen nopean ja onnistuneen kannalta kriittistä on, että kerääminen automatisoidaan standardimenetelmällä, jos se on kannattavaa. Kaikki lähtötiedot pitää sijaita samassa paikassa, sekä kaikki tiedot ja vaiheet pitäisi dokumentoida tarkasti. Työssä käsiteltyjä simuloinnin onnistumisen kannalta tärkeitä tekijöitä olivat ongelman ja tavoitteiden määrittely, käsitteellinen malli, mallin monimutkaisuus, lähtötiedot, validointi ja vahvistaminen sekä simulointiprojektin johtaminen. Edeltävät tekijät valittiin, koska ne määrittävät simulointiprojektin etenemistä, simulointimallin sisältöä sekä lisäksi ne esiintyivät tutkitussa kirjallisuudessa useasti. Simulointiprojektin johtaminen ohjaa koko simulointimallin kehittämistä, ja simulointiprojektin osapuolten tyytyväisyys on siitä riippuvainen. Loput tekijät määrittävät simulointimallin sisällön tarkemmin sekä vaikuttavat sen käyttökelpoisuuteen ja tulosten uskottavuuteen. Tämän tutkimuksen tueksi pitäisi tutkia useampia simulointiprojektiin vaikuttavia tekijöitä sekä tutkia tarkemmin simulointiprojektin johtamisen vaikutusta simuloinnin onnistumiseen. Myös lähtötietojen keräämisen automatisoimista standardimenetelmällä olisi tärkeä tutkia, jotta lähtötietojen kerääminen olisi tehokkaampaa ja luotettavampaa sekä automaatiota voisi implementoida pienemmällä kynnyksellä lähtötietojen keräämiseen

Discrete-Event Simulation Data Transformation: A Model-Driven Data Integration Approach

Achieving a smooth production system is a complex process that requires the use of commercial discrete event simulation (DES) tools to provide a high flexibility production process, for instance the use of simulation modelling to model a production system. These tools require high levels of cooperation to work together because they are not designed to be integrated and hardly share their data. This research aims to integrate DES tools applied by different manufacturing systems in order to enable them to share their data. This thesis presents data integration from a simulation model point of view because it views data integration between different DES tools models as key steps towards system integration. A new approach has been developed which is called a Model-Driven Data Integration Approach (MDDI), so named because the integration involves the combination of data from different DES tools model sources. The effectiveness of this data integration approach has been demonstrated in a case study undertaken for DES design of a phone production line in the manufacturing industry. However, the application of the MDDI is not limited to this case study: it can also be used for other system and applications. The MDDI approach was tested and evaluated on the basis of this case study. These test cases simulated how the data integration based on different DES tools’ models react to the process of data sharing as they occur in the manufacturing production line. The result is that the MDDI approach best maintains data consistency and integrity and can be adopted by different industries

An Integrated Framework for Automated Data Collection and Processing for Discrete Event Simulation Models

Discrete Events Simulation (DES) is a powerful tool of modeling and analysis used in different disciplines. DES models require data in order to determine the different parameters that drive the simulations. The literature about DES input data management indicates that the preparation of necessary input data is often a highly manual process, which causes inefficiencies, significant time consumption and a negative user experience. The focus of this research investigation is addressing the manual data collection and processing (MDCAP) problem prevalent in DES projects. This research investigation presents an integrated framework to solve the MDCAP problem by classifying the data needed for DES projects into three generic classes. Such classification permits automating and streamlining the preparation of the data, allowing DES modelers to collect, update, visualize, fit, validate, tally and test data in real-time, by performing intuitive actions. In addition to the proposed theoretical framework, this project introduces an innovative user interface that was programmed based on the ideas of the proposed framework. The interface is called DESI, which stands for Discrete Event Simulation Inputs. The proposed integrated framework to automate DES input data preparation was evaluated against benchmark measures presented in the literature in order to show its positive impact in DES input data management. This research investigation demonstrates that the proposed framework, instantiated by the DESI interface, addresses current gaps in the field, reduces the time devoted to input data management within DES projects and advances the state-of-the-art in DES input data management automation

Aplicação de uma abordagem de aquisição e armazenamento do conhecimento em projetos de simulação a eventos discretos.

As fases iniciais de um projeto de simulação definem o rumo da pesquisa bem como influenciam os resultados da investigação. Dito isso, torna-se importante a colaboração e compreensão entre pesquisador de simulação e especialista do sistema sob estudo. O objetivo desta dissertação é melhorar a gestão de projetos de simulação a eventos discretos, principalmente nas fases de concepção e implementação, através a aplicação de métodos da gestão do conhecimento (GC). Apresenta-se um projeto de pesquisa-ação na qual desenvolveu-se um estudo de simulação e aplicaram-se métodos de GC. A aquisição do conhecimento na fase de concepção e, em seguinte, o armazenamento do conhecimento na fase de implementação, foram os pontos centrais da pesquisa. Para melhor adquirir o conhecimento, utilizou-se a Soft Systems Methodology, qual é metodologia para estruturar problemas complexos. Para melhorar a coleta e organização de dados, apresentou-se uma abordagem passo-a-passo. Ou seja, todo o conhecimento relevante pode ser capturado e armazenado por meio de uma série de passos, assim facilitando a validação dos modelos conceitual e computacional, além de deixar um caminho de informação para futuras projetos de simulação