Robust manufacturing system design using petri nets and bayesian methods

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

An Interval Based Approach To Model Input Uncertainty In Discrete-event Simulation

The objective of this research is to increase the robustness of discrete-event simulation (DES) when input uncertainties associated models and parameters are present. Input uncertainties in simulation have different sources, including lack of data, conflicting information and beliefs, lack of introspection, measurement errors, and lack of information about dependency. A reliable solution is obtained from a simulation mechanism that accounts for these uncertainty components in simulation. An interval-based simulation (IBS) mechanism based on imprecise probabilities is proposed, where the statistical distribution parameters in simulation are intervals instead of precise real numbers. This approach incorporates variability and uncertainty in systems. In this research, a standard procedure to estimate interval parameters of probability distributions is developed based on the measurement of simulation robustness. New mechanisms based on the inverse transform to generate interval random variates are proposed. A generic approach to specify the required replication length to achieve a desired level of robustness is derived. Furthermore, three simulation clock advancement approaches in the interval-based simulation are investigated. A library of Java-based IBS toolkits that simulates queueing systems is developed to demonstrate the new proposed reliable simulation. New interval statistics for interval data analysis are proposed to support decision making. To assess the performance of the IBS, we developed an interval-based metamodel for automated material handling systems, which generates interval performance measures that are more reliable and computationally more efficient than traditional DES simulation results

A Bayesian Approach to The Assessment of Fuel Composition Variability Effects on Grate-bed Biomass Combustion

Combustion systems are the most energy-intensive facilities in the world. They are responsible for releasing the majority of the greenhouse gases (GHG) and NOx into the earth’s atmosphere. Biomass is the only renewable energy source consisting of fixed carbon elements which can be substituted for fossil fuels in combustion systems. The main distinction between biomass and fossil fuel combustion is fewer pollutant emissions of biomass combustion, as well as, biomass combustion’s lower price and simpler storage facility. So far, direct combustion of the solid biomass is the most popular method, both thermally and economically, among all various bioenergy systems, which is due to the price of biofuels process cost. Grate firing technology is of interest to burn solid biomass because it has less sensitivity to feed composition and size, which shows the excellent potential of this technology. However, owing to the intrinsic composition variability of biomass, there are still uncontrolled deflections associated with biomass combustors operations. This study is an effort to quantify the overall impact of fuel compositions variability on moving bed biomass combustion, which will facilitate the understanding of biomass combustion. Randomly selected biomass pellets were individually investigated via a Thermogravimetric Analysis (TGA) to specify the fuel compositions; moisture, volatile, char, and ash. This data, together with the predefined fuel composition provided by fuel supplier are utilized to train a model using a Bayesian approach to populate our measured data. Simultaneously, a 1D transient numerical model of moving bed biomass combustion is deliberately developed corresponding to the research goals. The model iteratively runs with distributed fuel composition made by the Bayesian data generator and simulates the combustor under uncertain conditions. The comprehensive thermo-economic and environmental analysis of the biomass boiler operated with the three most common biomass types was conducted. Specifically, this includes biomass pellets, wood waste, and municipal solid waste and through this research showed that biomass pellets are the most efficient in terms of thermal operation and financial revenue. An experiment-based approach to the composition uncertainty impact of biomass pellets and bamboo chips on moving bed combustors were also practiced. While a notable heat flux deviation from mean operation conditions was observed for both, the pelletizing helped pellets to limit the level of uncertainty to a satisfying degree. Higher char content can limit the combustion uncertainty to a strong extent, while the moisture content was found to be the main contributor to the level of uncertainty. As well, NOx emission arising from biomass combustion fluctuated up to 17% due to composition variability. Finally, combustor operations under more reliable input data via the Bayesian data generator showed a remarkable system deviation from that of predefined input conditions. Overlooking the fuel compositions variability caused an overestimation of heat generation of up to 8.5%. Moreover, a notable amount of unburned biomass particles was sent to an ash bin, which is not in line with biomass harvesting sustainability. To avoid this in the future, the system must be regulated to correspond to the fuel compositions offered by the Bayesian model