Despite simulation offers tremendous promise for designing and analyzing complex production systems, manufacturing industry has been less successful in using it as a decision support tool, especially in the early conceptual phase of factory flow design. If simulation is used today for system design, it is more often used in later phases when important design decisions have already been made and costs are locked. With an aim to advocate the use of simulation in early phases of factory design and analysis, this paper introduces FACTS Analyzer, a toolset developed based on the concept of integrating model abstraction, automatic model generation and simulation-based optimization under an innovative Internet-based platform. Specifically, it addresses a novel model aggregation and generation method, which when combined together with other system components, like optimization engines, can synthetically enable simulation to become much easier to use and speed up the time-consuming model building, experimentation and optimization processes, in order to support optimal decision making. INTRODUCTIONReal-world systems in manufacturing, supply chains and public services are too complex to be modeled by analytical techniques. Therefore, discrete event simulation (DES) are very useful for performing modeling and analysis on these systems. However, DES models are by nature evaluative -instead of suggesting any optimal solutions, a DES model evaluates a given set of design variables and generates the required performance measures. For a decision maker, the process of finding a sufficiently good design setting could be too time-consuming and in many cases impossible if the search space is huge. Simulation-based optimization (SBO) is a relatively new technique applied to seek the "optimal" setting for a complex system based on one or multiple performance measures generated from simulation by using various searching methodologies. SBO is a technology that offers huge potentials to solve real-world problems and have been successfully applied in many different domains (April et al. 2004). Nevertheless, until now, virtually all of today's commercial SBO packages still possess several major limitations: (1) they work in a deterministic mode, without taking into account the stochastic outputs from DES; (2) they do not explicitly address multi-objective problems, and (3) similar to most of the DES packages, the majority of SBO tools available is traditional software that need to be installed and run locally on the users' computers. With the vision that Internet and Web technologies could enable the explosive growth in research and commercial opportunities (Fu et al. 2000;Boesel et al. 2001;Miller et al. 2001), many efforts have been paid on Web-based simulation (WBS) since the 1990s. However, as summarized recently by Byrne 2176 978-1-4577-2109-0/11/$26.00 ©2011 IEEE

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Manufacturing companies of today are under pressure to run their production most efficiently in order to sustain their competitiveness. Manufacturing systems usually have bottlenecks that impede their performance, and finding the causes of these constraints, or even identifying their locations, is not a straightforward task. SCORE (Simulation-based COnstraint REmoval) is a promising method for detecting and ranking bottlenecks of production systems, that utilizes simulation-based multi-objective optimization (SMO). However, formulating a real-world, large-scale industrial bottleneck analysis problem into a SMO problem using the SCORE-method manually include tedious and error-prone tasks that may prohibit manufacturing companies to benefit from it. This paper presents how the greater part of the manual tasks can be automated by introducing a new, generic way of defining improvements of production systems and illustrates how the simplified application of SCORE can assist manufacturing companies in identifying their production constraints.