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1. Introduction -- 2. Basic Concepts -- 3. Robust Problems -- 4. General Reformulation Results -- 5. General Solution Methods -- 6. Robust election Problems -- 7. Robust Shortest Path Problems -- 8. Robust Spanning Tree Problems -- 9. Other Combinatorial Problems -- 10. Other Models for Robust...
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Adjustable Robust Optimization (ARO) yields, in general, better worst-case solutions than static Robust Optimization (RO). However, ARO is computationally more difficult than RO. In this paper, we derive conditions under which the worst-case objective values of ARO and RO problems are equal. We...
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Decision-makers who usually face model/parameter risk may prefer to act prudently by identifying optimal contracts that … solved. Numerical experiments are run for various risk preference choices and it is found that for relatively large sample … the modeler puts on the tail risk when defining its objective function. These findings suggest that one should be very …
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In this paper we propose a methodology for constructing decision rules for integer and continuous decision variables in multiperiod robust linear optimization problems. This type of problems finds application in, for example, inventory management, lot sizing, and manpower management. We show...
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This paper addresses the problem of utility maximization under uncertain parameters. In contrast with the classical approach, where the parameters of the model evolve freely within a given range, we constrain them via a penalty function. We show that this robust optimization process can be...
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Part I: Sources of uncertainty, risk, and imprecision -- Sources of Uncertainty in Location Analysis -- Risk, hazard … -- Location and Strategies in Stackelberg Security Games with Risk Aversion -- Facility Location and Supply Chain Risk Analytics … models with uncertainty -- Hub Location Models Under Uncertainty -- On risk management of multistage multiscale FLP under …
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