The focus of this thesis is the development of methodologies for the analysis of two important issues in a manufacturing environment: human boredom and machine utilization. In particular, this study proposes a mathematical programming model for job rotation to cope with worker's boredom in a manufacturing cell. One of the important side effects of task rotation is the impact of rotation interval on workers' skill learning and forgetting which may cause productivity losses. For this reason, the proposed model simultaneously incorporates worker's skill and boredom variations. The developed formulation is a mixed integer nonlinear mathematical programming model. A linear version of the model is also presented by assuming that both worker's skill and boredom change linearly over time. Furthermore, given the complexity and uncertainty involving human emotions, a novel approach based on the state-of-the-art Bayesian Networks is also presented to measure and to predict human boredom at work. First, the static Bayesian Network is introduced to capture the static aspect of boredom modeling. The static boredom model is subsequently extended based on dynamic Bayesian Networks to account for temporal aspect of boredom modeling. The dynamic boredom model allows integrating boredom evidences spatially and temporally, thus providing a more robust and accurate boredom inference. The proposed boredom model is validated using case based scenarios. Moreover, a metaheuristic approach based on several well-known search algorithms is also presented to solve the nonlinear version of the job rotation model. The proposed algorithm integrates several ingredients including a simulated annealing module, three types of memory, an evolutionary operator, and a blockage removal feature in a generic framework. The main characteristic of the proposed model is the use of both positive and negative memory to intensify the search around good solutions as well as an evolution-based diversification approach. This generic metaheuristic can be tailored to solve a variety of hard optimization problems.
Another important issue in a manufacturing environment is machine utilization which is largely affected by scheduling decisions. Though various scheduling problems have been investigated for several decades, the lack of efficient solution methods is still hindering the machine utilization and hence the manufacturing productivity. For this reason, the two most common scheduling problems, job shop scheduling and flow shop scheduling problems are investigated in this thesis. The proposed new metaheuristic is used to solve the scheduling problems.
This study presents a methodology to explicitly deal with human's boredom and skill variations at work. It also introduces a probabilistic model to quantitatively measure and predict human boredom. Finally, it contributes to the development of computational intelligence by introducing a generic framework of a new metaheuristic to solve a class of combinatorial optimization problems.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/29755 |
| Date | January 2009 |
| Creators | Azizi, Nader |
| Publisher | University of Ottawa (Canada) |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 224 p. |
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