Joel Goh

Assistant Professor of Business Administration

Joel Goh is an assistant professor of business administration in the Technology & Operations Management Unit, teaching the Technology & Operations Management course to first-year MBA students.

Professor Goh develops mathematical models to provide insights into medical decision making and recommendations for health policy in areas including drug safety, workplace stress, and cost-effectiveness of new medical technology. He has also made methodological contributions in the field of operations research, specifically in robust optimization and supply chain management. Professor Goh is the co-creator of ROME (Robust Optimization Made Easy), a freely distributed software package for modeling robust optimization problems. His research has been published in Management Science and Operations Research.

 

Joel Goh is an assistant professor of business administration in the Technology & Operations Management Unit, teaching the Technology & Operations Management course to first-year MBA students.

Professor Goh develops mathematical models to provide insights into medical decision making and recommendations for health policy in areas including drug safety, workplace stress, and cost-effectiveness of new medical technology. He has also made methodological contributions in the field of operations research, specifically in robust optimization and supply chain management. Professor Goh is the co-creator of ROME (Robust Optimization Made Easy), a freely distributed software package for modeling robust optimization problems. His research has been published in Management Science and Operations Research.

Professor Goh holds a Ph.D. in Operations, Information, and Technology from the Stanford University Graduate School of Business. He also earned M.S. and B.S. degrees from Stanford in Electrical Engineering.

Journal Articles

  1. Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns

    We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a newly-defined Partitioned Value-at-Risk (PVaR) risk measure by using half-space statistical information. Using simulated data, the PVaR approach always generates better risk-return tradeoffs in the optimal portfolios when compared to traditional Markowitz mean–variance approach. When using real financial data, our approach also outperforms the Markowitz approach in the risk-return tradeoff. Given that the PVaR measure is also a robust risk measure, our new approach can be very useful for optimal portfolio allocations when asset return distributions are asymmetrical.

    Keywords: robust optimization; portfolio management; value-at-risk; Mathematical Methods; Finance;

    Citation:

    Goh, Joel, Kian Guan Lim, Melvyn Sim, and Weina Zhang. "Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns."European Journal of Operational Research 221, no. 2 (September 2012): 397–406. View Details
  2. Total Cost Control in Project Management via Satisficing

    We consider projects with uncertain activity times and the possibility of expediting, or crashing, them. Activity times come from a partially specified distribution within a family of distributions. This family is described by one or more of the following details about the uncertainties: support, mean, and covariance. We allow correlation between past and future activity time performance across activities. Our objective considers total completion time penalty plus crashing and overhead costs. We develop a robust optimization model that uses a conditional value-at-risk satisficing measure. We develop linear and piecewise-linear decision rules for activity start time and crashing decisions. These rules are designed to perform robustly against all possible scenarios of activity time uncertainty, when implemented in either static or rolling horizon mode. We compare our procedures against the previously available Program Evaluation and Review Technique and Monte Carlo simulation procedures. Our computational studies show that, relative to previous approaches, our crashing policies provide both a higher level of performance, i.e., higher success rates and lower budget overruns, and substantial robustness to activity time distributions. The relative advantages and information requirements of the static and rolling horizon implementations are discussed.

    Keywords: project management; time and cost control; robust optimization; satisficing; linear decision rule; PERT; Management; Cost Management; Projects;

    Citation:

    Goh, Joel, and Nicholas G. Hall. "Total Cost Control in Project Management via Satisficing."Management Science 59, no. 6 (June 2013): 1354–1372. View Details
  3. Robust Optimization Made Easy with ROME

    We introduce ROME, an algebraic modeling toolbox for a class of robust optimization problems. ROME serves as an intermediate layer between the modeler and optimization solver engines, allowing modelers to express robust optimization problems in a mathematically meaningful way. In this paper, we discuss how ROME can be used to model (1) a service-constrained robust inventory management problem, (2) a project-crashing problem, and (3) a robust portfolio optimization problem. Through these modeling examples, we highlight the key features of ROME that allow it to expedite the modeling and subsequent numerical analysis of robust optimization problems. ROME is freely distributed for academic use at http://www.robustopt.com.

    Keywords: robust optimization; algebraic modeling toolbox; MATLAB; stochastic programming; decision rules; Inventory control; PERT; project management; portfolio optimization; Technology; Mathematical Methods; Operations;

    Citation:

    Goh, Joel, and Melvyn Sim. "Robust Optimization Made Easy with ROME."Operations Research 59, no. 4 (July–August 2011): 973–985. View Details
  4. Distributionally Robust Optimization and Its Tractable Approximations

    In this paper we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules. Our framework begins by first affinely extending the set of primitive uncertainties to generate new linear decision rules of larger dimensions and is therefore more flexible. Next, we develop new piecewise-linear decision rules that allow a more flexible reformulation of the original problem. The reformulated problem will generally contain terms with expectations on the positive parts of the recourse variables. Finally, we convert the uncertain linear program into a deterministic convex program by constructing distributionally robust bounds on these expectations. These bounds are constructed by first using different pieces of information on the distribution of the underlying uncertainties to develop separate bounds and next integrating them into a combined bound that is better than each of the individual bounds.

    Keywords: Technology; Mathematical Methods; Operations;

    Citation:

    Goh, Joel, and Melvyn Sim. "Distributionally Robust Optimization and Its Tractable Approximations."Operations Research 58, no. 4 (pt.1) (July–August 2010): 902–917. View Details

Working Papers

  1. The Relationship Between Workplace Practices and Mortality and Health Costs in the United States

    Keywords: health care policy; Working Conditions; psychosocial stressors; health outcomes; Health; Mathematical Methods;

    Citation:

    Goh, Joel, Jeffrey Pfeffer, and Stefanos A. Zenios. "The Relationship Between Workplace Practices and Mortality and Health Costs in the United States." Working Paper, August 2014. View Details
  2. Data Uncertainty in Markov Chains: Application to Cost-effectiveness Analyses of Medical Innovations

    Keywords: Markov Chain; uncertainty; Colorectal Cancer; Screening; Cost-effectiveness Analysis; Health; Mathematical Methods;

    Citation:

    Goh, Joel, Mohsen Bayati, Stefanos A. Zenios, Sundeep Singh, and David Moore. "Data Uncertainty in Markov Chains: Application to Cost-effectiveness Analyses of Medical Innovations." Working Paper, June 2014. View Details
  3. Active Postmarketing Drug Surveillance for Multiple Adverse Events

    Keywords: drug surveillance; sequential testing; queueing network; diffusion approximation; Health; Mathematical Methods;

    Citation:

    Goh, Joel, Margret V. Bjarnadottir, Mohsen Bayati, and Stefanos A. Zenios. "Active Postmarketing Drug Surveillance for Multiple Adverse Events." Working Paper, January 2013. (Finalist for 2012 Pierskalla Award, Health Applications Society, INFORMS.) View Details