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Theory and Practice of Uncertain Programming [electronic resource] /by Baoding Liu.

by Liu, Baoding [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Fuzziness and Soft Computing: 239Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.Description: online resource.ISBN: 9783540894841.Subject(s): Engineering | Software engineering | Mathematical optimization | Operations research | Distribution (Probability theory) | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Software Engineering | Optimization | Operations Research, Mathematical Programming | Probability Theory and Stochastic ProcessesDDC classification: 519 Online resources: Click here to access online
Contents:
Mathematical Programming -- Genetic Algorithms -- Neural Networks -- Stochastic Programming -- Fuzzy Programming -- Hybrid Programming -- Uncertain Programming -- System Reliability Design -- Project Scheduling Problem -- Vehicle Routing Problem -- Facility Location Problem -- Machine Scheduling Problem.
In: Springer eBooksSummary: Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.
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Mathematical Programming -- Genetic Algorithms -- Neural Networks -- Stochastic Programming -- Fuzzy Programming -- Hybrid Programming -- Uncertain Programming -- System Reliability Design -- Project Scheduling Problem -- Vehicle Routing Problem -- Facility Location Problem -- Machine Scheduling Problem.

Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

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