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A Stochastic Production Planning Model Under Uncertain Demand, Meenakshi Prajapati
A Stochastic Production Planning Model Under Uncertain Demand, Meenakshi Prajapati
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Production planning plays a vital role in the management of manufacturingfacilities. The problem is to determine the production loading plan consisting of the quantity of production and the workforce level - to fulfill a future demand. Although the deterministic version of the problem has been widely studied in the literature, the stochastic production planning problem has not. The application of production planning models could be limited if the stochastic nature of the problem, for example, uncertainty in future demand, is not addressed. This study addresses such a stochastic production planning problem under uncertain demand and its application in an enclosure …
Evolutionary Methodology For Optimization Of Image Transforms Subject To Quantization Noise, Michael Ray Peterson
Evolutionary Methodology For Optimization Of Image Transforms Subject To Quantization Noise, Michael Ray Peterson
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Lossy image compression algorithms sacrifice perfect imagereconstruction in favor of decreased storage requirements. Modelossy compression schemes, such as JPEG2000, rely upon the discrete wavelet transform (DWT) to achieve high levels of compression while minimizing the loss of information for image reconstruction. Some compression applications require higher levels of compression than those achieved through application of the DWT and entropy coding. In such lossy systems, quantization provides high compression rates at the cost of increased distortion. Unfortunately, as the amount of quantization increases, the performance of the DWT for accurate image reconstruction deteriorates. Previous research demonstrates that a genetic algorithm can …
New Heuristic And Metaheuristic Approaches Applied To The Multiple-Choice Multidimensional Knapsack Problem, Chaitr Hiremath
New Heuristic And Metaheuristic Approaches Applied To The Multiple-Choice Multidimensional Knapsack Problem, Chaitr Hiremath
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The knapsack problem has been used to model various decision making processes. Industrial applications find the need for satisfying additional constraints and these necessities lead to the variants and extensions of knapsack problems which are complex to solve. Heuristic algorithms have been developed by many researchers to solve the variants of knapsack problems. Empirical analysis has been done to compare the performance of these heuristics. Little research has been done to find out why certain algorithms perform well on certain test problems while not so well on other test problems. There has been little work done to gain knowledge of …
System Design Of Undersea Vehicles With Multiple Sources Of Uncertainty, Todd W. Benanzer
System Design Of Undersea Vehicles With Multiple Sources Of Uncertainty, Todd W. Benanzer
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The work performed investigates the system design and optimization of an undersea vehicle. The system design is driven by the available components, the missions the vehicle is required to perform, and the performance the system configuration yields. The system design consists of three design modules: path planning, component selection and sizing, and structural analysis. The path planning module uses a novel application of the Particle Swarm Optimization algorithm named Path Planning by Additive Freedom. Additionally, the unknown aspects of the mission space through which the path propagates are dealt with using an uncertainty quantification method known as Evidence Theory. Component …