Operations research combines the applications of optimization, probability and statistics to solve problems in different domains including business, energy and utilities, health services, financial services and logistics. In order to solve today’s complex system environment, operations research often works at the intersection of these disciplines, such as the use of optimization in the estimation of large scale statistical models, optimal collection of information, and stochastic optimization.
Systems engineers know how to develop and use mathematical and statistical models to help solve these decision problems. Like other engineers, they are problem formulators and solvers. Their work requires the formation of a mathematical model of a system and the analysis and prediction of the consequences of alternate modes of operating the system.
Distributionally Robust Discrete Optimization with Entropic Value-at-Risk
Z. Y. LongWe study the discrete optimization problem under the distributionally robust framework. We optimize the Entropic Value-at-Risk, which is a coherent risk measure and is also known as Bernstein approximation for the chance constraint. We propose...
Fast Algorithms for Big Data Analytics
A. M.-C. SoThe ubiquity of big datasets and the desire to extract information and knowledge from them have motivated the development of a wide array of data analytics tools in recent years. Many of these tools aim at identifying the most informative...
Financial Systemic Risk
N ChenFinancial institutions knit a complex network. They interconnect with each other directly through active borrowing-and-lending activities and holding significant amount of marketable securities against each other. In normal times, this network...
Managing Underperformance Risk in Project Portfolio Selection
Z.Y. LongWe consider a project selection problem where each project has an uncertain return with partially characterized probability distribution. The decision maker selects a feasible subset of projects so that the risk of the portfolio return not meeting...
New Scheduling Models with Applications to Berth Allocation
X. Cai and C.Y. Lee
The study focuses on modelling, analysis, and algorithms for a class of new scheduling problems where a big job must occupy a full machine, while a small job may share a machine with some other jobs at the same time. Applications...
Nonconvex Approaches to Rank-Constrained Semidefinite Programs
A. M.-C. SoMany intractable problems in engineering can be formulated as a semidefinite program (SDP) with a rank constraint. Currently, a standard approach to tackle these problems is semidefinite relaxation. The idea is to drop the rank constraint...
Nonconvex Optimization and Global Optimization
D. Li and C. K. Ng
The research goal is to develop equivalent transformations for generating a saddle point for nonconvex optimization problems. A saddle point condition is a sufficient condition for optimality. A saddle point can be generated in...
Nonlinear Integer Programming
D. Li and C. K. Ng
The research goal is to establish convergent duality theory and to develop efficient solution algorithms for large-scale nonlinear integer programming problems. The fundamental target underlying our theoretical development is to...
On Dynamic Decision Making to Meet Consumption Targets
Z. Y LongWe investigate a dynamic decision model that facilitates a target-oriented decision maker in regulating her risky consumption based on her desired target consumption level in every period in a finite planning horizon. We focus on dynamic operational...
Scheduling of Perishable Jobs under Uncertain Deadlines
X. Cai and X. ZhouWe study a new class of scheduling problems involving perishable jobs with post-completion deterioration, where each finished product will be picked up by a transporter that arrives with uncertainty. The processing time to complete...
Scheduling with Negotiable Third-Party Machines
X. Cai, C.Y. Lee and George VairakarakisSuppose a manufacturer has received a number of orders (jobs) from his customers, which should be completed by their respective due dates. Most of the facilities needed to process the jobs are available in the...
Strong approximations in multiclass queuing networks
X.F. GaoMulticlass queueing networks have been used to model manufacturing and communication systems. For those multiclass networks with a static priority service discipline, the diffusion approximation for the queue length of a higher priority group...
Theory and Applications of Chance Constrained Optimization
A. M.-C. SoIn the formulation of optimization models, the data defining the objective functions and/or constraints are often collected via estimation or sampling, and hence are only approximations of the nominal values. One approach to incorporate data...