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Recent Projects
About the Project Another area of application for these methodologies is to address the challenge of deploying resources in a real time manner, often without complete information of how the environment will evolve in the near future. This kind of real time optimization is often encountered in service platforms…
About the Project Another area of application for these methodologies is to address the challenge of deploying resources in a real time manner, often without complete information of how the environment will evolve in the near future. This kind of real time optimization is often encountered in service platforms…
About the Project The problems that arise in the real world are difficult due to the inherent non linearities in the model. Academic groups from different schools have tackled these problems by linearizing and discretizing the model and data uncertainty. This allow them to solve the optimization problem as…
About the Project The problems that arise in the real world are difficult due to the inherent non linearities in the model. Academic groups from different schools have tackled these problems by linearizing and discretizing the model and data uncertainty. This allow them to solve the optimization problem as…
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Media
Recent Publications
Zhi Chen , Peng Xiong
We introduce a Python package called RSOME for modeling a wide spectrum of robust and distributionally robust optimization problems. RSOME serves as an open-source framework for modeling various optimization problems subject to distributional ambiguity in a highly readable and mathematically intuitive manner. It is versatile and fits well in…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Zhi Chen , Peng Xiong
We introduce a Python package called RSOME for modeling a wide spectrum of robust and distributionally robust optimization problems. RSOME serves as an open-source framework for modeling various optimization problems subject to distributional ambiguity in a highly readable and mathematically intuitive manner. It is versatile and fits well in…
INFORMS Journal on Computing
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Ju Liu, Liu Changchun, Chung Piaw Teo
We develop a general framework for selecting a small pool of candidate solutions to maximize the chances that one will be optimal for a combinatorial optimization problem, under a linear and additive random payoff function. We formulate this problem using a two-stage distributionally robust model, with a mixed 0–1…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Ju Liu, Liu Changchun, Chung Piaw Teo
We develop a general framework for selecting a small pool of candidate solutions to maximize the chances that one will be optimal for a combinatorial optimization problem, under a linear and additive random payoff function. We formulate this problem using a two-stage distributionally robust model, with a mixed 0–1…
Production and Operations Management
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Li Chen, Melvyn Sim
We propose robust optimization models and their tractable approximations that cater for ambiguity-averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst-case expected exponential utility where the underlying uncertainty is generated from a set of stochastically independent…
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
Li Chen, Melvyn Sim
We propose robust optimization models and their tractable approximations that cater for ambiguity-averse decision makers whose underlying risk preferences are consistent with constant absolute risk aversion (CARA). Specifically, we focus on maximizing the worst-case expected exponential utility where the underlying uncertainty is generated from a set of stochastically independent…
Operations Research
This research is supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 grant call (Award ref: MOE-2019-T3-1-010)
