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Real-life decisions are usually made in the state of uncertainty (randomness, fuzziness, roughness, etc.). How dowe 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. It includes numerous modeling ideas, hybrid intelligent algorithms, and various applications in transportation problem, inventory system, facility location&allocation, capital budgeting, topological optimization, vehicle routing problem, redundancy optimization, and scheduling. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.
List of contents
From the contents:
- Fundamentals: Mathematical Programming; Genetic Algorithms; Neural Networks
- Stochastic Programming: Random Variables; Stochastic Expected Value Models; Stochastic Chance-Constrained Programming; Stochastic Dependent-Chance Programming
- Fuzzy Programming: Fuzzy Variables; Fuzzy Expected Value Models; Fuzzy Chance-Constrained Programming; Fuzzy Dependent-Chance Programming; Fuzzy Programming with Fuzzy Decisions
- Rough Programming: Rough Variables; Rough Programming
- Fuzzy Random Programming: Fuzzy Random Variables; Fuzzy Random Expected Value Models; Fuzzy Random Chance-Constrained Programming; Fuzzy Random Dependent-Chance Programming
- Random Fuzzy Programming: Random Fuzzy Variables; Random Fuzzy Expected Value Models; Random Fuzzy Chance-Constrained Programming; Random Fuzzy Dependent-Chance Programming
- Generel Principle: Multifold Uncertainty; Uncertain Programming.
