Matías Urenda Moris

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

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 of-ten 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 plat-form. 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.

Documentation of discrete event simulation models for manufacturing system life cycle simulation

The concept of life cycle simulation appeals to most production engineers. There is a problem with simulation models of manufacturing systems; they may be highly complex and time consuming to develop. They embrace a considerable experience, which is gained through the development process of the simulation model. It is therefore not enough to develop an accurate simulation model. The model must be understood, updated, re-used and adhered to by others. One way to achieve these goals could be through use of a standardised documentation, which in turn explains the model and how it has been developed. This paper presents a method for the way in which simulation models can be documented by standardised notations. The documentation is adapted to different users of the simulation model.

Simplification and aggregation strategies applied for factory analysis in conceptual phase using simulation

Despite that simulation possesses an establish background and 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 conceptual phase of factory design. This paper presents how simplification and aggregation strategies are incorporated in a modeling, simulation and analysis tool, with the aim of supporting decision making in conceptual phase. Conceptual modeling is guided by a framework using an object library with generic drag and drop system components and system control objects. Data inputs are simplified by the use of Effective Process Time distributions and a novel aggregation method for product mix cycle time differences. The out coming specification is through a Web Service interface handle by modeling system architecture, automatically generating a simulation model and analysis. Case studies confirm a breakthrough in project time reduction without appreciable effects on the model¿s fidelity.

Lean, simulation and optimization: a win-win combination

Lean and simulation analysis are driven by the same objective, how to better design and improve processes making the companies more competitive. The adoption of lean has been widely spread in companies from public to private sectors and simulation is nowadays becoming more and more popular. Several authors have pointed out the benefits of combining simulation and lean, however, they are still rarely used together in practice. Optimization as an additional technique to this combination is even a more powerful approach especially when designing and improving complex processes with multiple conflicting objectives. This paper presents the mutual benefits that are gained when combining lean, simulation and optimization and how they overcome each others limitations. A framework including the three concepts, some of the barriers for its implementation and a real-world industrial example are also described.

System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is ...