The Experts below are selected from a list of 73926 Experts worldwide ranked by ideXlab platform

Onder Belgin - One of the best experts on this subject based on the ideXlab platform.

  • a meta model based simulation Optimization using hybrid simulation analytical modeling to increase the productivity in automotive industry
    Mathematics and Computers in Simulation, 2016
    Co-Authors: Berna Dengiz, Onder Belgin
    Abstract:

    Simulation modeling is one of the most useful techniques to analyze and evaluate the dynamic behavior of the complex manufacturing systems. Combining the mathematical power of an analytical method and the modeling capability of simulation with Optimization approach called hybrid simulation-analytical modeling has been presented rarely. In this study a production control model is developed for a paint shop department in an automotive company in Turkey. As a real case study, the optimum operating setting of a paint shop production line of automotive company is determined using hybrid simulation Optimization approach. In the Optimization Stage of the study Design of Experiment (DoE) is used to identify critical variables of the system by fitting a polynomial to the experimental data in a multiple linear regression analysis. The meta-model is validated and shown that it provides good approximations to simulation results. Findings from hybrid simulation-analytical Optimization approach give invaluable knowledge to the company for the re-designing and control of current manufacturing system to increase its productivity.

  • Simulation Optimization of a multi-Stage multi-product paint shop line with Response Surface Methodology
    SIMULATION, 2014
    Co-Authors: Berna Dengiz, Onder Belgin
    Abstract:

    Recently, Response Surface Methodology (RSM) has attracted a growing interest, along with other simulation Optimization (SO) techniques, for non-parametric modeling and robust Optimization of systems. In the Optimization Stage of this study, the authors use RSM to find optimum working conditions of a system. The authors also use discrete event simulation modeling, Optimization Stage integration, design of experiment (DOE) and sensitivity analysis (a) to investigate the behavior of a real paint shop production line via construction of response surface plots and (b) to reveal the influence of input variables, as well as to determine interaction effects between them. The proposed approach presents an approximation model management structure for the computation-intensive Optimization problem of an automotive factory with reduced variance, computational cost and amount of effort.

  • Winter Simulation Conference - Paintshop production line Optimization using response surface methodology
    2007 Winter Simulation Conference, 2007
    Co-Authors: Berna Dengiz, Onder Belgin
    Abstract:

    This paper deals with the problem of determining the optimum number of workstations to be used in parallel and workers at some stations using simulation Optimization approach in a paint shop line of an automotive factory in Ankara, Turkey. In the Optimization Stage of the study response surface methodology (RSM) is used to find the optimum levels of considered factors. Simulation model and Optimization Stage integration is used both to analyse the performance of the current paint shop line and deter mine the optimum working conditions, respectively, with reduced cost, time and effort.

Berna Dengiz - One of the best experts on this subject based on the ideXlab platform.

  • a meta model based simulation Optimization using hybrid simulation analytical modeling to increase the productivity in automotive industry
    Mathematics and Computers in Simulation, 2016
    Co-Authors: Berna Dengiz, Onder Belgin
    Abstract:

    Simulation modeling is one of the most useful techniques to analyze and evaluate the dynamic behavior of the complex manufacturing systems. Combining the mathematical power of an analytical method and the modeling capability of simulation with Optimization approach called hybrid simulation-analytical modeling has been presented rarely. In this study a production control model is developed for a paint shop department in an automotive company in Turkey. As a real case study, the optimum operating setting of a paint shop production line of automotive company is determined using hybrid simulation Optimization approach. In the Optimization Stage of the study Design of Experiment (DoE) is used to identify critical variables of the system by fitting a polynomial to the experimental data in a multiple linear regression analysis. The meta-model is validated and shown that it provides good approximations to simulation results. Findings from hybrid simulation-analytical Optimization approach give invaluable knowledge to the company for the re-designing and control of current manufacturing system to increase its productivity.

  • Simulation Optimization of a multi-Stage multi-product paint shop line with Response Surface Methodology
    SIMULATION, 2014
    Co-Authors: Berna Dengiz, Onder Belgin
    Abstract:

    Recently, Response Surface Methodology (RSM) has attracted a growing interest, along with other simulation Optimization (SO) techniques, for non-parametric modeling and robust Optimization of systems. In the Optimization Stage of this study, the authors use RSM to find optimum working conditions of a system. The authors also use discrete event simulation modeling, Optimization Stage integration, design of experiment (DOE) and sensitivity analysis (a) to investigate the behavior of a real paint shop production line via construction of response surface plots and (b) to reveal the influence of input variables, as well as to determine interaction effects between them. The proposed approach presents an approximation model management structure for the computation-intensive Optimization problem of an automotive factory with reduced variance, computational cost and amount of effort.

  • Winter Simulation Conference - Redesign of PCB production line with simulation and Taguchi design
    Proceedings of the 2009 Winter Simulation Conference (WSC), 2009
    Co-Authors: Berna Dengiz
    Abstract:

    This paper presents the problem of determining the optimum condition of printed circuit board (PCB) manufacturing process in an electronic company in Ankara, Turkey. In the Optimization Stage of the study Taguchi method is integrated with simulation model considering minimum total cost under stochastic breakdowns. Using this methodology we investigate the system performance of the current PCB line and determine the optimum working conditions with reduced cost, time and effort.

  • Winter Simulation Conference - Paintshop production line Optimization using response surface methodology
    2007 Winter Simulation Conference, 2007
    Co-Authors: Berna Dengiz, Onder Belgin
    Abstract:

    This paper deals with the problem of determining the optimum number of workstations to be used in parallel and workers at some stations using simulation Optimization approach in a paint shop line of an automotive factory in Ankara, Turkey. In the Optimization Stage of the study response surface methodology (RSM) is used to find the optimum levels of considered factors. Simulation model and Optimization Stage integration is used both to analyse the performance of the current paint shop line and deter mine the optimum working conditions, respectively, with reduced cost, time and effort.

Krzysztof Grabczewski - One of the best experts on this subject based on the ideXlab platform.

  • a new methodology of extraction Optimization and application of crisp and fuzzy logical rules
    IEEE Transactions on Neural Networks, 2001
    Co-Authors: Wøodzisøaw Duch, Rafaø Adamczak, Krzysztof Grabczewski
    Abstract:

    A new methodology of extraction, Optimization, and application of sets of logical rules is described. Neural networks are used for initial rule extraction, local or global minimization procedures for Optimization, and Gaussian uncertainties of measurements are assumed during application of logical rules. Algorithms for extraction of logical rules from data with real-valued features require determination of linguistic variables or membership functions. Contest-dependent membership functions for crisp and fuzzy linguistic variables are introduced and methods of their determination described. Several neural and machine learning methods of logical rule extraction generating initial rules are described, based on constrained multilayer perceptron, networks with localized transfer functions or on separability criteria for determination of linguistic variables. A tradeoff between accurary/simplicity is explored at the rule extraction Stage and between rejection/error level at the Optimization Stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trapezoidal" membership functions and allowing to optimize the linguistic variables using gradient procedures. Numerous applications of this methodology to benchmark and real-life problems are reported and very simple crisp logical rules for many datasets provided.

  • Neural methods of knowledge extraction
    Control and Cybernetics, 2000
    Co-Authors: Wøodzisøaw Duch, Rafaø Adamczak, Krzysztof Grabczewski, Norbert Jankowski
    Abstract:

    Contrary to the common opinion neural networks may be used for knowledge extraction. Recently a new methodology of logical rule extraction, Optimization and application of rule-based systems has been described. C-MLP2LN algorithm, based on constrained multilayer perceptron network, is described here in details and the dynamics of a transition from neural to logical system illustrated. The algorithm han- dles real-valued features, determining appropriate linguistic variables or membership functions as a part of the rule extraction process. Initial rules are optimized exploring the tradeoff between accuracy/simplicity at the rule extraction Stage and between reliability of rules and rejection rate at the Optimization Stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trape- zoidal" membership functions and allowing to optimize the linguistic vari- ables using gradient procedures. Comments are made on application of neural networks to knowledge discovery in benchmark and in real life

Eric Wong - One of the best experts on this subject based on the ideXlab platform.

  • Toward market-driven agents for electronic auction
    IEEE Transactions on Systems Man and Cybernetics - Part A: Systems and Humans, 2001
    Co-Authors: Kwang Mong Sim, Eric Wong
    Abstract:

    While there are several existing agent-based systems addressing the crucial and difficult issues of automated negotiation and auction, this research has designed and engineered a society of trading agents with two distinguishing features: 1) a market-driven negotiation strategy and 2) a deal optimizing auction protocol. Unlike some of the existing systems where users manually select predefined trading strategies, in the market-driven approach, trading agents automatically select the appropriate strategies by examining the changing market situations. Results from a series of experiments suggest that the market-driven approach generally achieved more favorable outcomes as compared to the fixed strategy approach. Furthermore, it provides a more intuitive simulation of trading because trading agents are able to respond to different market situations with appropriate strategies. By augmenting the auction protocol with a deal Optimization Stage, trading agents can be programmed to optimize transaction deals by delaying the finalization of deals in search of better deals. Experimental results showed that by having a deal Optimization Stage, the auction protocol produced generally optimistic outcomes.

N D K Asante - One of the best experts on this subject based on the ideXlab platform.

  • diagnosis and Optimization approach for heat exchanger network retrofit
    Aiche Journal, 1999
    Co-Authors: N D K Asante
    Abstract:

    A new modeling approach for HEN retrofit design is discussed. The design task consists of a search for topology changes, called the diagnostic Stage, followed by an evaluation Stage and a cost Optimization Stage. Promising modifications are selected from the diagnosis Stage and assessed in terms of the impacts on implementation cost, operability, and safety. The options deemed impractical are removed and the remaining ones are optimized together with the existing HEN to give the final HEN retrofit design. The new approach combines mathematical Optimization techniques with a better understanding of the retrofit problem, based on thermodynamic analysis and practical engineering, to produce a systematic procedure capable of efficiently solving industrial-size retrofit problems. The network pinch concept provides new insights to the HEN retrofit problem and plays an important role in selecting promising modifications, forming the foundation of the new method. This concept, when applied to mathematical formulation, significantly simplified the mathematical models while maintaining good quality of solutions. This approach allows the design tasks to be automated with user interactions.