Towards assisted input and output data analysis in manufacturing simulation: The EDASim approach

Bogon, Tjorben; Timm, Ingo J.; Jessen, Ulrich; Schmitz, Markus; Wenzel, Sigrid; Lattner, Andreas D.; Paraskevopoulos, Dimitrios; Spieckermann, Sven

doi:10.1109/wsc.2012.6465114

Cited by 11 publications

(12 citation statements)

References 26 publications

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“…In previous research, we were contributing to developing assistance functionalities for simulation systems as well as working on the application of knowledge-based and machine learning algorithms to simulation (Bogon et al 2012) However, we recognized that the missing methodological foundation of computer simulation in information systems research could evolve to a showstopper. Thus, in future research, we are aiming at developing solutions for meeting the previously introduced challenges in order to increase quality of simulation-based research.…”

Section: Resultsmentioning

confidence: 99%

Untitled

2019

GMiL

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Timm and Lorig 2 EVOLUTION OF COMPUTER SIMULATION Almost 70 years ago, the foundation for computer simulation has been laid. Back then the invention of ENIAC (a "general purpose electronic computing machine" (Goldstine and Goldstine 1946) developed for calculating fire tables during World War II) enabled scientists to automatically execute mathematical computations for solving numerical problems (Ulam 1990). Since then, hardware for executing simulation experiments has been enhanced and the application areas expanded. Nowadays, computer simulation is applied in various disciplines and has become an essential technique in scientific research. By observing and adjusting simulation models, hypotheses concerning the behavior of artificial systems can be evaluated and the impacts of modifying real systems can be estimated ex ante (Banks 1998). However, findings made by analyzing artificial systems are often used for drawing conclusions in regard to the real world. Especially in the context of investment decisions, simulation is frequently used for purposes of planning alternative approaches. Common areas of application are, for example, factory planning including materialflow simulation (e.g. Kühn 2006) or traffic simulation when building new road networks or redesigning traffic junctions (e.g. Lattner et al. 2011). Therefore, the necessity of general conditions and guidelines for assuring a certain level of quality is obvious. Reliable results can only be generated if the preparation and execution of simulation experiments are conducted under well-defined or even standardized conditions. Only in this case, results may be used as a profound basis for decision-making or as an origin for further research. Since the beginnings of simulation research, numerous articles have been published discussing the classification and application of the technology. In this regard, various approaches describing conditions for conducting simulation studies have been proposed as well. Furthermore, the definition of the term computer simulation itself is varying in literature. Humphreys compared partially opposing definitions and derived the following definition: "A computer simulation is any computer-implemented method for exploring the properties of mathematical models where analytic methods are unavailable." (Humphreys 1990) In contrast, Banks developed another commonly used definition of the term computer simulation: "A simulation is the imitation of the operation of a real-world process or system over time." (Banks 1996) The definitions mentioned above emphasize the ambiguity in the understanding of the technology. Computer simulation can either be regarded as a method (e.g. for the generation of knowledge (Ören 1987)) or a tool (e.g. for generating artificial scenarios by imitating real systems (Reynolds 2008)). Yet, when performing simulation experiments, most of the authors do not seem to be aware of this fundamental methodological uncertainty. Accordingly, a proper application of computer simulation is difficult.

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Section: Resultsmentioning

confidence: 99%

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2019

GMiL

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“…Although the resulting OCBA algorithm is heuristic, it has been successfully applied to heuristic algorithms, which have been successfully applied to many practical problems [26,27]. The OCBA solution can also be used as an approximate solution to the optimization problem defined by the exponential rate of the large deviation probability that maximizes the wrong choice [28,29]. OCBA was also studied in the Bayesian framework [30], but the formula for parameter-optimization problems did not consider order assignment.…”

Section: Simulation Optimization Ranking and Selectionmentioning

confidence: 99%

DA-OCBA: Distributed Asynchronous Optimal Computing Budget Allocation Algorithm of Simulation Optimization Using Cloud Computing

et al. 2019

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The ranking and selection of simulation optimization is a very powerful tool in systems engineering and operations research. Due to the influence of randomness, the algorithms for ranking and selection need high and uncertain amounts of computing power. Recent advances in cloud computing provide an economical and flexible platform to execute these algorithms. Among all ranking and selection algorithms, the optimal computing budget allocation (OCBA) algorithm is one of the most efficient. However, because of the lack of sufficient samples that can be executed in parallel at each stage, some features of the cloud-computing platform, such as parallelism, scalability, flexibility, and symmetry, cannot be fully utilized. To solve these problems, this paper proposes a distributed asynchronous OCBA (DA-OCBA) algorithm. Under the framework of parallel asynchronous simulation, this algorithm takes advantage of every idle docker container to run better designs in advance that are selected by an asymptotic allocation rule. The experiment demonstrated that the efficiency of simulation optimization for DA-OCBA was clearly higher than that for the traditional OCBA on the cloud platform with symmetric architecture. As the number of containers grew, the speedup of DA-OCBA was linearly increasing for simulation optimization.

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“…The RuleGrowth/TRuleGrowth/CMRules algorithms have been applied successfully in e-learning [6,7], manufacturing simulation [31], quality control [27], web page clickstream analysis [30] and anti-pattern detection in service based systems [31]. In the next paragraphs, we present results from the application of TRuleGrowth to webpage prefetching.…”

Section: Experiments To Assess Prediction Accuracymentioning

confidence: 99%

Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

Fournier-Viger

Tseng

et al. 2015

IEEE Trans. Knowl. Data Eng.

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Sequential rule mining is an important data mining problem with multiple applications. An important limitation of algorithms for mining sequential rules common to multiple sequences is that rules are very specific and therefore many similar rules may represent the same situation. This can cause three major problems: (1) similar rules can be rated quite differently, (2) rules may not be found because they are individually considered uninteresting, and (3) rules that are too specific are less likely to be used for making predictions. To address these issues, we explore the idea of mining "partially-ordered sequential rules" (POSR), a more general form of sequential rules such that items in the antecedent and the consequent of each rule are unordered. To mine POSR, we propose the RuleGrowth algorithm, which is efficient and easily extendable. In particular, we present an extension (TRuleGrowth) that accepts a sliding-window constraint to find rules occurring within a maximum amount of time. A performance study with four real-life datasets show that RuleGrowth and TRuleGrowth have excellent performance and scalability compared to baseline algorithms and that the number of rules discovered can be several orders of magnitude smaller when the sliding-window constraint is applied. Furthermore, we also report results from a real application showing that POSR can provide a much higher prediction accuracy than regular sequential rules for sequence prediction.

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Towards assisted input and output data analysis in manufacturing simulation: The EDASim approach

Cited by 11 publications

References 26 publications

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DA-OCBA: Distributed Asynchronous Optimal Computing Budget Allocation Algorithm of Simulation Optimization Using Cloud Computing

Mining Partially-Ordered Sequential Rules Common to Multiple Sequences

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