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. Multi-Echelon Inventory Management Under Short-Term Take-or-Pay Contracts

    Joel Goh and Evan L. Porteus

    We extend the Clark–Scarf serial multi-echelon inventory model to include procuring production inputs under short-term take-or-pay contracts at one or more stages. In each period, each such stage has the option to order/process at two different cost rates; the cheaper rate applies to units up to the contract quantity selected in the previous period. We prove that in each period and at each such stage, there are three base-stock levels that characterize an optimal policy, two for the inventory policy and one for the contract quantity selection policy. The optimal cost function is additively separable in its state variables, leading to conquering the curse of dimensionality and the opportunity to manage the supply chain using independently acting managers. We develop conditions under which myopic policies are optimal and illustrate the results using numerical examples. We establish and use a generic one-period result, which generalizes an important such result in the literature. Extensions to cover variants of take-or-pay contracts are included. Limitations are discussed.

    Keywords: Inventory management; multi-echelon inventory theory; Karush Lemma; Clark-Scarf model; convex ordering cost; Advance Commitments; Supply Chain;

    Citation:

    Goh, Joel, and Evan L. Porteus. "Multi-Echelon Inventory Management Under Short-Term Take-or-Pay Contracts." Production and Operations Management (forthcoming). (Finalist for 2014 POMS College of Supply Chain Management Student Paper Award.) View Details
  2. Understanding Online Hotel Reviews Through Automated Text Analysis

    Shawn Mankad, Hyunjeong "Spring" Han, Joel Goh and Srinagesh Gavirneni

    Customer reviews submitted at Internet travel portals are an important yet underexplored new resource in obtaining feedback on customer experience for the hospitality industry. These data are often voluminous and unstructured, presenting analytical challenges for traditional tools that were designed for well-structured, quantitative data. We adapt methods from natural language processing and machine learning to illustrate how the hotel industry can leverage this new data source by performing automated evaluation of the quality of writing, sentiment estimation, and topic extraction. By analyzing 5,830 reviews from 57 hotels in Moscow, Russia, we find that (i) negative reviews tend to focus on a small number of topics, whereas positive reviews tend to touch on a greater number of topics; (ii) negative sentiment inherent in a review has a larger downward impact than corresponding positive sentiment; and (iii) negative reviews contain a larger variation in sentiment on average than positive reviews. These insights can be instrumental in helping hotels achieve their strategic, financial, and operational objectives.

    Keywords: hotel reviews; natural language processing; service operations; Information Technology; Service Operations; Accommodations Industry; Moscow;

    Citation:

    Mankad, Shawn, Hyunjeong "Spring" Han, Joel Goh, and Srinagesh Gavirneni. "Understanding Online Hotel Reviews Through Automated Text Analysis." Service Science 8, no. 2 (June 2016): 124–138. View Details
  3. The Relationship Between Workplace Stressors and Mortality and Health Costs in the United States

    Joel Goh, Jeffrey Pfeffer and Stefanos A. Zenios

    Even though epidemiological evidence links specific workplace stressors to health outcomes, the aggregate contribution of these factors to overall mortality and health spending in the United States is not known. In this paper, we build a model to estimate the excess mortality and incremental health expenditures associated with exposure to the following 10 workplace stressors: unemployment, lack of health insurance, exposure to shift work, long work hours, job insecurity, work–family conflict, low job control, high job demands, low social support at work, and low organizational justice. Our model uses input parameters obtained from publicly accessible data sources. We estimated health spending from the Medical Expenditure Panel Survey and joint probabilities of workplace exposures from the General Social Survey, and we conducted a meta-analysis of the epidemiological literature to estimate the relative risks of poor health outcomes associated with exposure to these stressors. The model was designed to overcome limitations with using inputs from multiple data sources. Specifically, the model separately derives optimistic and conservative estimates of the effect of multiple workplace exposures on health and uses optimization to calculate upper and lower bounds around each estimate, which accounts for the correlation between exposures. We find that more than 120,000 deaths per year and approximately 5%–8% of annual healthcare costs are associated with and may be attributable to how U.S. companies manage their work forces. Our results suggest that more attention should be paid to management practices as important contributors to health outcomes and costs in the United States.

    Keywords: occupational health; health costs; mortality; applied optimization; Health;

    Citation:

    Goh, Joel, Jeffrey Pfeffer, and Stefanos A. Zenios. "The Relationship Between Workplace Stressors and Mortality and Health Costs in the United States." Management Science 62, no. 2 (February 2016): 608–628. View Details
  4. Exposure to Harmful Workplace Practices Could Account for Inequality in Life Spans Across Different Demographic Groups

    Joel Goh, Jeffrey Pfeffer and Stefanos A. Zenios

    The existence of important socioeconomic disparities in health and mortality is a well-established fact. Many pathways have been adduced to explain inequality in life spans. In this article we examine one factor that has been somewhat neglected: people with different levels of education get sorted into jobs with different degrees of exposure to workplace attributes that contribute to poor health. We used General Social Survey data to estimate differential exposures to workplace conditions, results from a meta-analysis that estimated the effect of workplace conditions on mortality, and a model that permitted us to estimate the overall effects of workplace practices on health. We conclude that 10%–38% of the difference in life expectancy across demographic groups can be explained by the different job conditions their members experience.

    Keywords: occupational health; inequality; life expectancy; socioeconomic issues; Health;

    Citation:

    Goh, Joel, Jeffrey Pfeffer, and Stefanos A. Zenios. "Exposure to Harmful Workplace Practices Could Account for Inequality in Life Spans Across Different Demographic Groups." Health Affairs 34, no. 10 (October 2015): 1761–1768. View Details
  5. Workplace Stressors & Health Outcomes: Health Policy for the Workplace

    Joel Goh, Jeffrey Pfeffer and Stefanos A. Zenios

    Extensive research focuses on the causes of workplace-induced stress. However, policy efforts to tackle the ever-increasing health costs and poor health outcomes in the United States have largely ignored the health effects of psychosocial workplace stressors such as high job demands, economic insecurity, and long work hours. Using meta-analysis, we summarize 228 studies assessing the effects of ten workplace stressors on four health outcomes. We find that job insecurity increases the odds of reporting poor health by about 50%, high job demands raise the odds of having a physician-diagnosed illness by 35%, and long work hours increase mortality by almost 20%. Therefore, policies designed to reduce health costs and improve health outcomes should account for the health effects of the workplace environment.

    Keywords: occupational health; mortality; stress; Meta-analysis; Health;

    Citation:

    Goh, Joel, Jeffrey Pfeffer, and Stefanos A. Zenios. "Workplace Stressors & Health Outcomes: Health Policy for the Workplace." Behavioral Science & Policy 1, no. 1 (2015): 43–52. View Details
  6. Active Postmarketing Drug Surveillance for Multiple Adverse Events

    Joel Goh, Margrét V. Bjarnadóttir, Mohsen Bayati and Stefanos A. Zenios

    Postmarketing drug surveillance is the process of monitoring the adverse events of pharmaceutical or medical devices after they are approved by the appropriate regulatory authorities. Historically, such surveillance was based on voluntary reports by medical practitioners, but with the widespread adoption of electronic medical records and comprehensive patient databases, surveillance systems that utilize such data are of considerable interest. Unfortunately, existing methods for analyzing the data in such systems ignore the open-ended exploratory nature of such systems that requires the assessment of multiple possible adverse events. In this article, we propose a method, SEQMEDS, that assesses the effect of a single drug on multiple adverse events by analyzing data that accumulate sequentially and explicitly captures interdependencies among the multiple events. The method continuously monitors a vector-valued test-statistic derived from the cumulative number of adverse events. It flags a potential adverse event once the test-statistic crosses a stopping boundary. We employ asymptotic analysis that assumes a large number of observations in a given window of time to show how to compute the stopping boundary by solving a convex optimization problem that achieves a desired Type I error and minimizes the expected time to detection under a pre-specified alternative hypothesis. We apply our method to a model in which the interdependency among the multiple adverse events is captured by a Cox proportional hazards model with time-dependent covariates and demonstrate that it provides an approximation of a fully sequential test for the maximum hazard ratio of the drug over multiple adverse events. A numerical study verifies that our method delivers Type I /II errors that are below pre-specified levels and is robust to distributional assumptions and parameter values.

    Keywords: drug surveillance; health care; stochastic models; queueing; diffusion approximation; Brownian motion; Health;

    Citation:

    Goh, Joel, Margrét V. Bjarnadóttir, Mohsen Bayati, and Stefanos A. Zenios. "Active Postmarketing Drug Surveillance for Multiple Adverse Events." Operations Research 63, no. 6 (November–December 2015): 1528–1546. (Finalist for 2012 Pierskalla Award, INFORMS Health Applications Society.) View Details
  7. Total Cost Control in Project Management via Satisficing

    Joel Goh and Nicholas G. Hall

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

    Joel Goh, Kian Guan Lim, Melvyn Sim and Weina Zhang

    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 1, 2012): 397–406. View Details
  9. Robust Optimization Made Easy with ROME

    Joel Goh and Melvyn Sim

    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
  10. Distributionally Robust Optimization and Its Tractable Approximations

    Joel Goh and Melvyn Sim

    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. Choosing an Assortment Rotation Strategy to Boost Sales

    Kris J. Ferreira and Joel Goh

    Assortment rotation strategies vary widely across different retailers; Gap introduces new products a few times a season, whereas Zara introduces new products every couple of weeks. The flash sales industry takes the frequent assortment rotation strategy to the extreme and introduces new products on a daily basis or even multiple times a day. These companies aim to create a feeling of urgency and scarcity of products by offering great deals but for limited time and with limited inventory. Our work aims to quantify the benefit of the frequent assortment rotation strategy, particularly to identify the impact of frequent assortment rotations on sales. We model this environment as a finite-horizon stochastic dynamic program to better understand the consumer's purchase decisions. We are currently analyzing and comparing our model to the setting where all products are offered for the entire selling season, and we aim to show under what conditions it would be advantageous—or disadvantageous—for the retailer to employ the frequent assortment rotation strategy.

    Keywords: Strategy; Consumer Behavior; Operations; Sales; Retail Industry;

    Citation:

    Ferreira, Kris J., and Joel Goh. "Choosing an Assortment Rotation Strategy to Boost Sales." Working Paper, August 2015. View Details