Factory flow design and analysis using internet-enabled simulation-based optimization and automatic model generation

Abstract

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

An interactive decision support system using simulation-based optimization and data mining

“…Through scalability, cloud computing offers the possibility of using the web-based version of the authors' simulation software by any users. Ng et al (2011) also present an internet-based DSS where a simulation software can evaluate solutions both in a distributed and parallel manner. This increases the precision and speed of the DSS.…”

“…The model is an extended version of the one presented in Ng et al (2011). Although the model is a hypothetical one, it is realistically designed based on our experiences in working with real-world complex models and decision making situations in industry.…”

“…S 3 is a selection among three options: (1) two machines with longer setup times; (2) three machines with longer setup times; (3) two machines with shorter setup times. When compared to the model presented in Ng et al (2011), the current model is more complex as there are two decisions that have to be made for the operations at the second stage, between Store2 and Buffer3, namely, (1) number of machines (N 2 ) and (2) if these machines are flexible machining centers that can process multiple part types or they are dedicated machines that are capable of processing only one specific part type. Since this model involves three product variants (i.e.…”

On the scalability of meta-models in simulation-based optimization of production systems

“…In the experiments considered in this paper, the models developed using FACTS Analyzer 2.0 (Ng et al 2011) are used to solve the above described three-objective optimization problem. Note that the only objective function that requires simulation is f 1 (x) = max{T H(x)}.…”

Production simulation education using rapid modeling and optimization: Successful studies

“…The key features that FACTS Analyzer 2.0 added were more complex product variant and setups handling as well as additional modeling objects, like Selections, for rapidly comparing different scenarios by switching the connections to different submodels. Started already in the first version, simulation-based optimization (SBO), particularly multiobjective optimization (MOO), has been an integral component of FACTS Analyzer to facilitate managers/engineers to run their own optimization runs in order to seek the optimal combinations of model variables so that more confident decisions can be made by analyzing the Pareto-optimal solutions, as demonstrated in Ng et al (2011), Pehrsson and Ng (2011) and Pehrsson et al (2013) with real-world applications.…”