IORA - Institute of Operations Research and Analytics

Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
2021-03-12 10:00 - 11:30 (Asia/Singapore)
Dr. Ying Chen is a financial statistician and data scientist. She develops statistical modelling and machine learning methods customized for nonstationary, high frequency and large dimensional complex data such as cryptocurrency, limit order book, and renewable energy. She also works on business intelligence, forecasting, text mining and sentiment analysis, and network analysis. Dr. Chen is Associate Professor in Department of Mathematics and Joint Appointee in Risk Management Institute (1 July 2019 to 30 June 2021), National University of Singapore. Dr. Chen is Associate Editor of 5 journals including Statistica Sinica (August 1, 2017 to July 31, 2023), Statistics and Its Interface, Computational Statistics, Digital Finance, and Journal of Operations Research and Decisions. She is ISI Elected Member since March 2016. She is regular member of the Advisory Board of Institute of Statistical Mathematics, Japan from 1 April 2018 to 31 March 2020.

Name of Speaker
A/P Chen Ying

Schedule
Friday 12 March 2021 , 10am

Link
https://nus-sg.zoom.us/j/87277632379?pwd=[REDACTED]

ID
872 7763 2379

Password
247866

Title
Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression

Abstract
Probabilistic forecasting of electricity load curves is of fundamental importance for effective scheduling and decision making in the increasingly volatile and competitive energy markets. We propose a novel approach to construct probabilistic predictors for curves (PPC), which leads to a natural and new definition of quantiles in the context of curve-to-curve linear regression. There are three types of PPC: a predict set, a predictive band and a predictive quantile, and all of them are defined at a pre-specified nominal probability level. In the simulation study, the PPC achieve promising coverage probabilities under a variety of data generating mechanisms. When applying to one day ahead forecasting for the French daily electricity load curves, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy, coverage rate and average length of the predictive bands. For example, PPC achieve up to 2.8-fold of the coverage rate with much smaller average length of the predictive bands. The predictive quantile curves provide insightful information which is highly relevant to hedging risks in electricity supply management. (Joint work with Xiuqin Xu, Yannig Goude and Qiwei Yao. Available at https://arxiv.org/abs/2009.01595)

The Dao of Robustness
2021-03-05 10:00 - 12:00
We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution. Unlike data-driven robust optimization approaches, the decision maker does not have to size the ambiguity set, but specifies an acceptable target, or loss of optimality compared to the baseline optimization model, as a trade off for the model’s ability to withstand greater uncertainty. We axiomatize the decision criterion associated with robustness optimization, termed as the fragility measure and present its representation theorem. We present practicable robustness optimization models including models with safegurarding constraints, adaptive and dynamic optimization models. Similar to robust optimization, we show that robustness optimization can also be done in a tractable way. We also provide numerical studies on adaptive problems and show that the solutions to the robustness optimization models are effective in alleviating the Optimizer’s Curse (Smith and Winkler 2006) and yielding superior out-of-sample performance compared the empirical optimization model and current data-driven robust optimization models. This is a joint work with Mingling Zhou and Zhuoyu Long.

Name of Speaker
Prof Melvyn Sim

Schedule
Friday 5 March 2021 , 10am

Link
https://nus-sg.zoom.us/j/84738208342?pwd=[REDACTED]

ID
847 3820 8342

Password
008617

Title
The Dao of Robustness

Abstract
We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution. Unlike data-driven robust optimization approaches, the decision maker does not have to size the ambiguity set, but specifies an acceptable target, or loss of optimality compared to the baseline optimization model, as a trade off for the model’s ability to withstand greater uncertainty. We axiomatize the decision criterion associated with robustness optimization, termed as the fragility measure and present its representation theorem. We present practicable robustness optimization models including models with safegurarding constraints, adaptive and dynamic optimization models. Similar to robust optimization, we show that robustness optimization can also be done in a tractable way. We also provide numerical studies on adaptive problems and show that the solutions to the robustness optimization models are effective in alleviating the Optimizer’s Curse (Smith and Winkler 2006) and yielding superior out-of-sample performance compared the empirical optimization model and current data-driven robust optimization models. This is a joint work with Mingling Zhou and Zhuoyu Long.

About the Speaker
Dr Melvyn Sim is a professor at the Department of Analytics and Operations, National University of Singapore. For the past twenty years, he has been working the the area of optimization and decision making under uncertainty.
Find out more about Prof Sim and Robust Optimization: https://youtu.be/eGXBd7KxjEY

IORA Seminar Series | 26 Feb | 10am
2021-02-26 10:00 - 11:00
Extensive research has shown that human decision makers in operations and supply chains are often influenced by cognitive biases and social preferences, and as a consequence fail to achieve the optimal performance prescribed by normative operations theories. In this talk, we will discuss how behavioral factors affect decisions and economic outcomes in two operations management settings. In the first setting, we analyze how cognitive biases affect inventory decisions when biases are endogenously given in competition. In the second setting, we study how fairness and competition interact in a supply chain of one supplier and two retailers. In both settings, we find that the presence of competition can reverse the impact that the behavioral factors have on decision and performance in individual settings.

Name of Speaker

A/P Wu Yaozhong

Schedule

Friday 26 Feb 2021 , 10am

Link
https://nus-sg.zoom.us/j/85139484987?pwd=[REDACTED]

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ID
851 3948 4987

Password
000323

Title

Understanding the role of behavioral factors in competitive operations and supply chains

Abstract

Extensive research has shown that human decision makers in operations and supply chains are often influenced by cognitive biases and social preferences, and as a consequence fail to achieve the optimal performance prescribed by normative operations theories. In this talk, we will discuss how behavioral factors affect decisions and economic outcomes in two operations management settings. In the first setting, we analyze how cognitive biases affect inventory decisions when biases are endogenously given in competition. In the second setting, we study how fairness and competition interact in a supply chain of one supplier and two retailers. In both settings, we find that the presence of competition can reverse the impact that the behavioral factors have on decision and performance in individual settings.

About the Speaker

Yaozhong Wu is an Associate Professor of Analytics and Operations at the NUS Business School. He received his Ph.D. in Technology and Operations Management from INSEAD. His main research interests are in the field of behavioral operations management. His papers have appeared in academic journals, such as Management Science, Operations Research, Manufacturing & Service Operations Management, Production and Operations Management, and Journal of Operations Management. A co-authored paper won the second place in the Wickham Skinner Awards for Best Paper Published in Production and Operations Management in 2014. Another co-authored paper won the Best Working Paper Award of the INFORMS Behavioral Operations Management Section in 2016. Currently, he serves as a senior editor for Production and Operations Management.

IORA Seminar Series | 15 Jan | 10am
2021-01-15 10:00 - 2021-01-16 11:00
Shape-constrained convex regression problem deals with fitting a convex function to the observed data, where additional constraints are imposed, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in $mathbb{R}^d$. We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables, $n(n-1)$ linear inequality constraints and $n$ possibly non-polyhedral inequality constraints, where $n$ is the number of data points. To efficiently solve the generally very large-scale convex QP, we design a proximal augmented Lagrangian method ({tt pALM}) whose subproblems are solved by the semismooth Newton method ({tt SSN}). To further accelerate the computation when $n$ is huge, we design a practical implementation of the constraint generation method such that each reduced problem is efficiently solved by our proposed {tt pALM}. Comprehensive numerical experiments, including those in the pricing of basket options and estimation of production functions in economics, demonstrate that our proposed {tt pALM} outperforms the state-of-the-art algorithms, and the proposed acceleration technique further shortens the computation time by a large margin. [This talk is based on joint work with Meixia Lin and Defeng Sun]

Name of Speaker
Prof Toh Kim Chuan

Schedule
Friday 15 January 2021 , 10am

Link
https://nus-sg.zoom.us/j/83515146165?pwd=[REDACTED]

ID
835 1514 6165

Password
700968

Title
An augmented Lagrangian method with constraint generations for shape-constrained convex regression problems

Abstract
Shape-constrained convex regression problem deals with fitting a convex function to the observed data, where additional constraints are imposed, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in $mathbb{R}^d$. We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables, $n(n-1)$ linear inequality constraints and $n$ possibly non-polyhedral inequality constraints, where $n$ is the number of data points. To efficiently solve the generally very large-scale convex QP, we design a proximal augmented Lagrangian method ({tt pALM}) whose subproblems are solved by the semismooth Newton method ({tt SSN}). To further accelerate the computation when $n$ is huge, we design a practical implementation of the constraint generation method such that each reduced problem is efficiently solved by our proposed {tt pALM}. Comprehensive numerical experiments, including those in the pricing of basket options and estimation of production functions in economics, demonstrate that our proposed {tt pALM} outperforms the state-of-the-art algorithms, and the proposed acceleration technique further shortens the computation time by a large margin. [This talk is based on joint work with Meixia Lin and Defeng Sun]

About the Speaker
Kim–Chuan Toh is a Professor at the Department of Mathematics, National University of Singapore (NUS). He obtained his BSc degree in Mathematics from NUS and PhD degree in Applied Mathematics from Cornell University. His current research focuses on designing efficient algorithms and software for convex programming and its applications, particularly large–scale optimization problems arising from data science/machine learning, and large–scale matrix optimization problems such as linear semidefinite programming (SDP) and convex quadratic semidefinite programming (QSDP). He is currently an Area Editor for Mathematical Programming Computation, an Associate Editor for the SIAM Journal on Optimization, Mathematical Programming Series B, and ACM Transactions on Mathematical Software. He received the Farkas Prize awarded by the INFORMS Optimization Society in 2017 and the triennial Beale–Orchard Hays Prize awarded by the Mathematical Optimization Society in 2018. He was elected as a Fellow of the Society for Industrial and Applied Mathematics in 2018.

INFORMS 2019
2019-10-20 - 2019-10-23
The 2019 INFORMS Annual Meeting is a unique opportunity to connect and network with the more than 6,000 INFORMS members, students, prospective employers and employees, and academic and industry experts who compose the INFORMS community.
It is with great pleasure to share the presentations and awards of IORA faculty, students at the INFORMS 2019 conference that took place in Seattle. Professor Teo Chung Piaw, Provost’s Chair and Director of the Institute of Operations Research and Analytics at NUS Business School, has been honoured by The Institute for Operations Research and Management Sciences (INFORMS) as a fellow, one of a mere dozen selected worldwide annually.

AWARDS

INFORMS FELLOWS: CLASS OF 2019
PROFESSOR TEO CHUNG PIAW
Professor Teo Chung Piaw, Provost’s Chair and Director of the Institute of Operations Research and Analytics at NUS Business School, has been honoured by The Institute for Operations Research and Management Sciences (INFORMS) as a fellow, one of a mere dozen selected worldwide annually.

GEORGE B.DANTZIG DISSERTATION AWARD COMPETITION
DR. GUODONG LYU
Dr. Guodong Lyu is the finalist with his PhD thesis “Online Resource Allocation: Theory and Applications”, under the supervision by Professor Chung-Piaw Teo (IORA, NUS).

2019 INFORMS-SECTION ON FINANCE BEST STUDENT PAPER COMPETITION
MR. SHUAIJIE QIAN
Mr. Shuaijie Qian was awarded the first place in “2019 Informs-Section on Finance Best Student Paper Competition”. This year the competition has attracted many talented students worldwide, including Columbia University and University of Pennsylvania.

PAPER PRESENTATION

PROFESSOR JOEL GOH

IORA Faculty Member, Prof Joel Goh presented a paper titled “Design of Incentive Programs for Optimal Medication Adherence“

PROFESSOR WENJIE TANG
IORA Faculty Member, Prof Wenjie Tang presented a paper titled “Invention Integrality And Gender Composition In Innovation Teams.“

DR AARON, JINJIA HUANG
IORA Research Fellow, Dr Aaron, Jinjia Huang presented a paper titled “Sparse and Efficient Rebalancing Operations: Concentrating the Flows in Dynamic Network“.”

HONG MING TAN
IORA PHD Student, Hong Ming Tan presented a paper titled “Information, Investment and Risk management.”

DR QINSHEN TANG
IORA Research Fellow, Dr Qinshen Tang presented a paper titled ”

Joint pricing and production: an analytics perspective
Repositioning for vehicle sharing: a risk mitigation perspective

DR SATYANATH BHAT
IORA Research Fellow, Dr Satyanath Bhat presented a paper titled “Doubled Dipping of Two-sided Platform Economy“

DR SHEN YICHI
IORA Research Fellow, Dr Shen Yichi presented a paper titled “Using Radial Basis Functions to Optimize Expensive Functions with Heterogenous Noise“

NUS National Observance Ceremony 2019
2019-08-09 09:30 - 13:30 | University Hall, National University of Singapore, 21 Lower Kent Ridge Rd, 119077, Singapore
IORA took part in NUS National Day Observance Ceremony held on Thursday, 8 August 2019 at University Hall, National University of Singapore. Dr Huang Jinjia, IORA Research Fellow, presented the SDPNAL+ software.
The software SDPNAL+ is designed for solving semidefinite programming (SDP), an important subfield of mathematical optimization and its applications are growing rapidly. Many practical problems in operations research and machine learning can be modeled or approximated as SDP problems. Traditional optimization methods can only solve small and medium scale (say, matrix dimension is less than 2000 and the number of constraints is less than 5000) SDP. Fortunately, large-scale SDP can be solved efficiently by SDPNAL+ now. Numerical experiments in the paper and other benchmark tests show that SDPNAL+ is a state-of-the-art solver for large-scale SDP and it is the only viable software to solve many large-scale SDPs at present. The largest SDP problem that is solved has matrix dimension 9261 and the number of constraints more than 12 million, which boosts the solvable scale to thousands of times. This software, developed by IORA faculty Prof Toh Kim Chuan, has won the 2018 Beale-Orchard-Hays Prize, the highest honor in the field of Computational Mathematical Optimization. In particular, the prize jury chair Dr. Michael Grant presented a concrete example shared by the nominator. It takes 122 hours for the traditional solver to solve a problem in a cluster with 56 cores CPU and 128 GPUs while SDPNAL+ solves it within 1.5 hours in a normal desktop PC. This new solver has many applications in practice. For instance, in a recent study, another IORA team of researchers have used this software to develop the backbone network structure to support bike rebalancing operations by volunteers
in a system like the Bike Angel program in New York, and demonstrated big reduction in the number of redundant moves by volunteers in such a system (i.e. with much less incentives), but with essentially the same level of performance (number of rides supported).
