Manufacturing productivity improvement: A study of human boredom, job rotation and scheduling
The focus of this thesis is the development of methodologies for the analysis o f 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 o f the important side effects o f 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 o f 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 o f 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 o f the job rotation model. The proposed algorithm integrates several ingredients including a simulated annealing module, three types o f memory, an evolutionary operator, and a blockage removal feature in a generic framework. The main characteristic o f the proposed model is the use o f 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 o f 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