Sharda, Bikram (2008-05). Robust manufacturing system design using petri nets and bayesian methods. Doctoral Dissertation. Thesis uri icon

abstract

  • Manufacturing system design decisions are costly and involve significant investment in terms of allocation of resources. These decisions are complex, due to uncertainties related to uncontrollable factors such as processing times and part demands. Designers often need to find a robust manufacturing system design that meets certain objectives under these uncertainties. Failure to find a robust design can lead to expensive consequences in terms of lost sales and high production costs. In order to find a robust design configuration, designers need accurate methods to model various uncertainties and efficient ways to search for feasible configurations. The dissertation work uses a multi-objective Genetic Algorithm (GA) and Petri net based modeling framework for a robust manufacturing system design. The Petri nets are coupled with Bayesian Model Averaging (BMA) to capture uncertainties associated with uncontrollable factors. BMA provides a unified framework to capture model, parameter and stochastic uncertainties associated with representation of various manufacturing activities. The BMA based approach overcomes limitations associated with uncertainty representation using classical methods presented in literature. Petri net based modeling is used to capture interactions among various subsystems, operation precedence and to identify bottleneck or conflicting situations. When coupled with Bayesian methods, Petri nets provide accurate assessment of manufacturing system dynamics and performance in presence of uncertainties. A multi-objective Genetic Algorithm (GA) is used to search manufacturing system designs, allowing designers to consider multiple objectives. The dissertation work provides algorithms for integrating Bayesian methods with Petri nets. Two manufacturing system design examples are presented to demonstrate the proposed approach. The results obtained using Bayesian methods are compared with classical methods and the effect of choosing different types of priors is evaluated. In summary, the dissertation provides a new, integrated Petri net based modeling framework coupled with BMA based approach for modeling and performance analysis of manufacturing system designs. The dissertation work allows designers to obtain accurate performance estimates of design configurations by considering model, parameter and stochastic uncertainties associated with representation of uncontrollable factors. Multi-objective GA coupled with Petri nets provide a flexible and time saving approach for searching and evaluating alternative manufacturing system designs.
  • Manufacturing system design decisions are costly and involve significant
    investment in terms of allocation of resources. These decisions are complex, due to
    uncertainties related to uncontrollable factors such as processing times and part
    demands. Designers often need to find a robust manufacturing system design that meets
    certain objectives under these uncertainties. Failure to find a robust design can lead to
    expensive consequences in terms of lost sales and high production costs. In order to find
    a robust design configuration, designers need accurate methods to model various
    uncertainties and efficient ways to search for feasible configurations.
    The dissertation work uses a multi-objective Genetic Algorithm (GA) and Petri net
    based modeling framework for a robust manufacturing system design. The Petri nets are
    coupled with Bayesian Model Averaging (BMA) to capture uncertainties associated with
    uncontrollable factors. BMA provides a unified framework to capture model, parameter
    and stochastic uncertainties associated with representation of various manufacturing
    activities. The BMA based approach overcomes limitations associated with uncertainty representation using classical methods presented in literature. Petri net based modeling is
    used to capture interactions among various subsystems, operation precedence and to
    identify bottleneck or conflicting situations. When coupled with Bayesian methods, Petri
    nets provide accurate assessment of manufacturing system dynamics and performance in
    presence of uncertainties. A multi-objective Genetic Algorithm (GA) is used to search
    manufacturing system designs, allowing designers to consider multiple objectives. The
    dissertation work provides algorithms for integrating Bayesian methods with Petri nets.
    Two manufacturing system design examples are presented to demonstrate the proposed
    approach. The results obtained using Bayesian methods are compared with classical
    methods and the effect of choosing different types of priors is evaluated.
    In summary, the dissertation provides a new, integrated Petri net based modeling
    framework coupled with BMA based approach for modeling and performance analysis
    of manufacturing system designs. The dissertation work allows designers to obtain
    accurate performance estimates of design configurations by considering model,
    parameter and stochastic uncertainties associated with representation of uncontrollable
    factors. Multi-objective GA coupled with Petri nets provide a flexible and time saving
    approach for searching and evaluating alternative manufacturing system designs.

publication date

  • May 2008