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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/85935 |
| Date | 10 October 2008 |
| Creators | Sharda, Bikram |
| Contributors | Banerjee, Amarnath |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, born digital |
Page generated in 0.0025 seconds