Jian Hu

Jian Hu
University of Michigan-Dearborn | UM-Dearborn · Department of Industrial and Manufacturing Systems Engineering

About

9
Publications
1,500
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191
Citations

Publications

Publications (9)
Article
Full-text available
We develop the concept of utility robustness to address the problem of ambiguity and inconsis-tency in utility assessments. A robust decision-making framework is built on a utility set which characterizes a decision maker's risk attitude described by boundary and auxiliary conditions. This framework is studied using the Sample Average Approximation...
Article
We propose an optimization model using a novel robust preference relationship—reference-based almost stochastic dominance (RSD). The concept of RSD addresses the two problems in utility-based decision making: (i) ambiguity and inaccuracy in characterizing the decision maker’s individual risk attitude, and (ii) overconservativeness of stochastic dom...
Article
Stochastic dominance is a preference relation of uncertain prospect defined over a class of utility functions. While this utility class represents basic properties of risk aversion, it includes some extreme utility functions rarely characterizing a rational decision maker's preference. In this paper we introduce reference-based almost stochastic do...
Article
The problem of comparing random vectors arises in many applications. We propose three new concepts of stochastically weighted dominance for comparing random vectors X and Y. The main idea is to use a random vector V to scalarize X and Y as (VX)-X-T and (VY)-Y-T, and subsequently use available concepts from stochastic dominance and stochastic optimi...
Article
Traffic incidents constitute a major cause for non-recurrent roadway congestion. This paper proposes a stochastic-programming model for clearing asymmetrical incidents in a network as quickly as possible. A response to an incident is asymmetric when the workloads among response vehicles are uneven due to real-time scheduling considerations. This is...
Article
Full-text available
This article presents and studies models for multi-criteria budget allocation problems under uncertainty. The proposed models incorporate uncertainties in decision maker's weights using a robust weighted sum approach. The risk averseness of the decision maker in satisfying random risk-related constraints is ensured by using stochastic dominance. A...
Article
Full-text available
We introduce and study a family of models for multi-expert multi-objective/criteria decision making. These models use a concept of weight robustness to generate a risk averse decision. In par-ticular, the multi-expert multi-criteria robust weighted sum approach (McRow) introduced in this paper identifies a (robust) Pareto optimum decision that mini...
Article
Full-text available
In this paper we study optimization problems with second-order stochastic dominance constraints. This class of problems allows for the modeling of optimization problems where a risk-averse decision maker wants to ensure that the solution produced by the model dominates certain benchmarks. Here we deal with the case of multi-variate stochastic domin...
Article
Full-text available
This paper presents and studies two new models for multi-criterion optimization under uncer-tainty. The first model uses a robust weighted objective function. The second model uses the concept of stochastic dominance constraints to impose benchmark requirements to incorporate risk averseness of the decision maker. These models are developed to dete...

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