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Publications supported by MOE Tier 3 Grant

From Targeting to Transfer: Design of Allocation Rules in Cash Transfer Programs

Huan Zheng, Guodong Lyu, Jiannan Ke, Chung-Piaw Teo
Problem Definition: Cash transfer programs (CTPs) have spread in the last decade to help fight extreme poverty in different parts of the world. A key issue here is to ensure that the cash is distributed to the targeted beneficiaries in an appropriate manner to meet the goals of the…
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)
Manufacturing & Service Operations Management (M&SOM)
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From Targeting to Transfer: Design of Allocation Rules in Cash Transfer Programs

Huan Zheng, Guodong Lyu, Jiannan Ke, Chung-Piaw Teo
Problem Definition: Cash transfer programs (CTPs) have spread in the last decade to help fight extreme poverty in different parts of the world. A key issue here is to ensure that the cash is distributed to the targeted beneficiaries in an appropriate manner to meet the goals of the…
Manufacturing & Service Operations Management (M&SOM)
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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)
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An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization

Heng Yang, Ling Liang, Lucas Carlone, and Kim-Chuan Toh
We consider solving high-order and tight semidefinite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that often admit degenerate rank-one optimal solutions. Instead of solving the SDP alone, we propose a new algorithmic framework that blends local search using the nonconvex POP into global descent using the convex…
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)
Mathematical Programming
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An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization

Heng Yang, Ling Liang, Lucas Carlone, and Kim-Chuan Toh
We consider solving high-order and tight semidefinite programming (SDP) relaxations of nonconvex polynomial optimization problems (POPs) that often admit degenerate rank-one optimal solutions. Instead of solving the SDP alone, we propose a new algorithmic framework that blends local search using the nonconvex POP into global descent using the convex…
Mathematical Programming
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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)
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QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems

Ling Liang, Xudong Li, Defeng Sun, Kim-Chuan Toh
In this paper, we aim to solve high dimensional convex quadratic programming (QP) problems with a large number of quadratic terms, linear equality and inequality constraints. In order to solve the targeted {\bf QP} problems to a desired accuracy efficiently, we develop a two-phase {\bf P}roximal {\bf A}ugmented {\bf…
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)
ACM Transaction on Mathematical Software, 48 (2022), Article 33.
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QPPAL: A two-phase proximal augmented Lagrangian method for high dimensional convex quadratic programming problems

Ling Liang, Xudong Li, Defeng Sun, Kim-Chuan Toh
In this paper, we aim to solve high dimensional convex quadratic programming (QP) problems with a large number of quadratic terms, linear equality and inequality constraints. In order to solve the targeted {\bf QP} problems to a desired accuracy efficiently, we develop a two-phase {\bf P}roximal {\bf A}ugmented {\bf…
ACM Transaction on Mathematical Software, 48 (2022), Article 33.
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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)
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Robust Satisficing

Daniel Zhuoyu Long, Melvyn Sim, Minglong Zhou
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 deviates from the empirical distribution. Unlike robust optimization approaches, the decision maker does not have to size…
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)
Operations Research
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Robust Satisficing

Daniel Zhuoyu Long, Melvyn Sim, Minglong Zhou
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 deviates from the empirical distribution. Unlike robust optimization approaches, the decision maker does not have to size…
Operations Research
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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)
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Convex Optimization for Bundle Size Pricing Problem

Xiaobo Li, Hailong Sun, Chung-Piaw Teo
We study the bundle size pricing (BSP) problem where a monopolist sells bundles of products to customers, and the price of each bundle depends only on the size (number of items) of the bundle. Although this pricing mechanism is attractive in practice, finding optimal bundle prices is difficult since…
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)
Management Science
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Convex Optimization for Bundle Size Pricing Problem

Xiaobo Li, Hailong Sun, Chung-Piaw Teo
We study the bundle size pricing (BSP) problem where a monopolist sells bundles of products to customers, and the price of each bundle depends only on the size (number of items) of the bundle. Although this pricing mechanism is attractive in practice, finding optimal bundle prices is difficult since…
Management Science
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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)
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Last Mile Innovation: The Case of the Locker Alliance Network

Guodong Lyu, Chung-Piaw Teo
Problem Definition: The Singapore government has recently proposed the concept of “Locker Alliance” (LA), an interoperable network of public lockers in residential areas and hot spots in community, to improve the efficiency of last mile parcel delivery operations. This is to complement the existing infrastructure, comprising mainly of proprietary…
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)
MSOM
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Last Mile Innovation: The Case of the Locker Alliance Network

Guodong Lyu, Chung-Piaw Teo
Problem Definition: The Singapore government has recently proposed the concept of “Locker Alliance” (LA), an interoperable network of public lockers in residential areas and hot spots in community, to improve the efficiency of last mile parcel delivery operations. This is to complement the existing infrastructure, comprising mainly of proprietary…
MSOM
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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)
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A Revised Approach for Risk-Averse Multi-Armed Bandits under CVaR Criterion

Najakorn Khajonchotpanya, Yilin Xue, Napat Rujeerapaiboon
We study multi-armed bandit problems that use conditional value-at-risk as an underlying risk measure. In particular, we propose a new upper confidence bound algorithm and compare it with the state-of-the-art alternatives with respect to various definitions of regret from the risk-averse online learning literature. For each comparison, we demonstrate…
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)
Operations Research Letters
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A Revised Approach for Risk-Averse Multi-Armed Bandits under CVaR Criterion

Najakorn Khajonchotpanya, Yilin Xue, Napat Rujeerapaiboon
We study multi-armed bandit problems that use conditional value-at-risk as an underlying risk measure. In particular, we propose a new upper confidence bound algorithm and compare it with the state-of-the-art alternatives with respect to various definitions of regret from the risk-averse online learning literature. For each comparison, we demonstrate…
Operations Research Letters
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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)
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On degenerate doubly nonnegative projection problems

Ying Cui, Ling Liang, Defeng Sun, Kim-Chuan Toh
The doubly nonnegative (DNN) cone, being the set of all positive semidefinite matrices whose elements are nonnegative, is a popular approximation of the computationally intractable completely positive cone. The major difficulty for implementing a Newton-type method to compute the projection of a given large scale matrix onto the DNN…
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)
Mathematics of Operations Research
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On degenerate doubly nonnegative projection problems

Ying Cui, Ling Liang, Defeng Sun, Kim-Chuan Toh
The doubly nonnegative (DNN) cone, being the set of all positive semidefinite matrices whose elements are nonnegative, is a popular approximation of the computationally intractable completely positive cone. The major difficulty for implementing a Newton-type method to compute the projection of a given large scale matrix onto the DNN…
Mathematics of Operations Research
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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)
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An Inexact Augmented Lagrangian Method for Second-order Cone Programming with Applications

Ling Liang, Defeng Sun, Kim-Chuan Toh
In this paper, we adopt the augmented Lagrangian method (ALM) to solve convex quadratic second-order cone programming problems (SOCPs). Fruitful results on the efficiency of the ALM have been established in the literature. Recently, it has been shown in [Cui, Sun, and Toh, {\em Math. Program.}, 178 (2019), pp….
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)
SIAM J. Optimization
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An Inexact Augmented Lagrangian Method for Second-order Cone Programming with Applications

Ling Liang, Defeng Sun, Kim-Chuan Toh
In this paper, we adopt the augmented Lagrangian method (ALM) to solve convex quadratic second-order cone programming problems (SOCPs). Fruitful results on the efficiency of the ALM have been established in the literature. Recently, it has been shown in [Cui, Sun, and Toh, {\em Math. Program.}, 178 (2019), pp….
SIAM J. Optimization
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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)
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An augmented Lagrangian method with constraint generations for shape-constrained convex regression problems

Meixia Lin, Defeng Sun, Kim-Chuan Toh
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 paper provides a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in ℝd. We…
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)
Mathematical Programming Computation
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An augmented Lagrangian method with constraint generations for shape-constrained convex regression problems

Meixia Lin, Defeng Sun, Kim-Chuan Toh
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 paper provides a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in ℝd. We…
Mathematical Programming Computation
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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)
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A Representative Consumer Model in Data-Driven Multi-product Pricing Optimization

Zhenzhen Yan, Karthik Natarajan, Chung Piaw Teo, Cong Cheng
We develop a data-driven approach for the multi-product pricing problem, using the theory of a represen- tative consumer in discrete choice. We establish a set of mathematical relationships between product prices and demand for each product in the system, including that of the outside option. We provide identification conditions…
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)
Management Science
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A Representative Consumer Model in Data-Driven Multi-product Pricing Optimization

Zhenzhen Yan, Karthik Natarajan, Chung Piaw Teo, Cong Cheng
We develop a data-driven approach for the multi-product pricing problem, using the theory of a represen- tative consumer in discrete choice. We establish a set of mathematical relationships between product prices and demand for each product in the system, including that of the outside option. We provide identification conditions…
Management Science
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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)
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Robot Scheduling for Mobile-Rack Warehouses: Human–Robot Coordinated Order Picking Systems

Zheng Wang, Jiuh-Biing Sheu, Chung-Piaw Teo, Guiqin Xue
Intelligent part-to-picker systems are spreading across a broad range of industries as preferred solutions for agile order fulfillment, wherein mobile racks are carried by robots and moved to stations where human pickers can pick items from them. Such systems raise the challenge of designing good work schedules for human…
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)
Production and Operations Management
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Robot Scheduling for Mobile-Rack Warehouses: Human–Robot Coordinated Order Picking Systems

Zheng Wang, Jiuh-Biing Sheu, Chung-Piaw Teo, Guiqin Xue
Intelligent part-to-picker systems are spreading across a broad range of industries as preferred solutions for agile order fulfillment, wherein mobile racks are carried by robots and moved to stations where human pickers can pick items from them. Such systems raise the challenge of designing good work schedules for human…
Production and Operations Management
Link to Publication
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)
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