INFORMS 2021 Program Book

INFORMS Anaheim 2021

SB09

2 - Panelist Masoud Kamalahmadi, University of Miami, Miami, FL, 33145, United States 3 - Panelist Esmaeil Keyvanshokooh, University of Michigan, Ann Arbor, Ann Arbor, MI, 48108-1020, United States 4 - Panelist Vikrant Vaze, Dartmouth College, Hanover, NH, 03755-3560, United States SB06 CC Room 303A In Person: Diversity/PSOR/MIF Diversity, Equity and Inclusion in OR/MS/Analytics. Innovations in Research and Practice I General Session Chair: Michael P Johnson, University of Massachusetts Boston, University of Massachusetts Boston, Boston, MA, 02125-3393, United States 1 - We’re Here: Interviews with LGBTQ+ Members of the INFORMS Community Tyler Perini, Georgia Institute of Technology, Atlanta, GA, 30318, United States While it can be tempting to rely solely on quantitative metrics, it is also critical to humanize individuals when it comes to minority issues. This requires stories to be told, heard, and documented. The objective for this project is to use semi- structured interviews to survey, document, and report the individual stories that color and humanize data for LGBTQ+ issues. Choosing to be “out” in academia is a highly personal and nuanced decision, and it is one that is unique to the LGBTQ+ community. Where do ambitious students or early career faculty find an LGBTQ+ mentor in our field? What mentorship advice can be condensed and shared publicly? The aim of this work is to tackle these and other challenges with a document that is meant to be valuable for Queer and non-Queer audiences, alike. This is a work in progress sponsored by the INFORMS DEI Ambassador Program. SB07 CC Room 201B In Person: Renewable Energy General Session Chair: Alexandra M. Newman, Colorado School of Mines, Colorado School of Mines, Golden, CO, 80401-1887, United States 1 - Estimating the Value of Concentrating Solar Power under New Costs Paradigm Kehinde Abiodun, Colorado School of Mines, Golden, CO, 80401, United States There is a gap in knowledge regarding the value of Concentrating Solar Power (CSP). Extant studies on the value of CSP are mostly outdated. This paper takes a price-taker approach to calculate the value of CSP based on recent cost information. The estimated value is based not only on the value from energy services and storage, but also on the provision of ancillary services, including spinning reserves and firm capacity. This paper uses price data from the CAISO market, zone SP15 in California, and National Renewable Energy Lab (NREL’s) System Advisor Model (SAM). 2 - Experience Curves and the Relatedness of Technologies: Offshore and Onshore Wind Energy Christian Hernandez-Negron, University of Massachusetts Amherst, Amherst, MA, United States, Erin Baker, Anna Goldstein We look at the impact of modeling offshore wind as (1) a fully new technology, (2) a direct offshoot of onshore wind, and (3) a hybrid. We chart the cumulative installed capacity of offshore wind on a global scale against the LCOE starting in 2010, and we find that assumptions about its relatedness to onshore wind are equally important as assumptions about future growth scenarios. We contrast these experience curve models with expert elicitations, which appear to underestimate recent trends in cost reduction for offshore wind. The results are consistent with the idea that experts view offshore wind as a direct offshoot of onshore wind. This research highlights a previously neglected factor in experience curve analysis, which may be especially important for technologies, such as offshore wind energy, that are expected to contribute significantly to climate change mitigation.

SB08 CC Room 303C In Person: Algorithmic Advances in Location Science for Spatial Demands General Session Chair: Peiqi Wang, Northeastern University, Princeton, NJ, 08540, United States 1 - A Spatial Algorithm to Identify All Non-dominated Solutions in Coverage and Access Optimization Alan Murray, Professor, University of California at Santa Barbara, CA, United States, Jiwoon Baik Selecting a good location for an activity or service is fundamentally important. Many different approaches across a range of disciplines have been proposed, developed, and explored to address such strategic decision-making. This paper introduces a bi-objective strategic location problem to address maximal coverage and access. A mathematical model formulation is presented, and an optimal solution algorithm is developed. Application findings are reported for several case studies. 2 - Predicting Ambulance Call Demand by Space and Time: A Machine Learning Approach In this study, spatially distributed hourly call volume predictions are generated using a multi-layer perceptron (MLP) artificial neural network model following feature selection using an ensemble-based decision tree model. K-Means clustering is applied to produce heterogeneous spatial clusters based on call location and associated call volume densities. The predictive performance of the MLP model is benchmarked against both a selection of traditional forecasting techniques. Results show that MLP models outperform time-series and industry forecasting methods, particularly at finer levels of spatial granularity where the R. Justin Martin, Assistant Teaching Professor, Wake Forest University, Winston Salem, NC, United States, Cem Saydam Peiqi Wang, Northeastern University, Boston, MA, United States This paper focuses on a special case of location problems where the goal is to downsize the existing facilities. Recent trends towards e-commerce and the impact of the COVID-19 pandemic is forcing many companies to make downsizing decisions to endure under these largely unforeseen market conditions. Hence the survival of many companies depends on making downsizing decisions efficiently and correctly. Computational geometry and optimization approaches have been successfully used in many logistics problems including location problems. We introduce several optimization models for different variants of the downsizing problem, develop geometric optimization algorithms to solve them and conduct a theoretical analysis to measure the impact of downsizing. SB09 CC Room 303D In Person: Learning and Decision-Making on Networks General Session Chair: Yueyang Zhong, The University of Chicago Booth School of Business, Chicago, IL, 60637-1610, United States 1 - Fast Rates for the Regret of Offline Reinforcement Learning Yichun Hu, Cornell University, New York, NY, United States, Nathan Kallus, Masatoshi Uehara We study the regret of RL from offline data generated by a fixed behavior policy in an infinite-horizon discounted MDP. While existing analyses of common approaches suggest an O(1/ √ n) convergence for regret, empirical behavior exhibits much faster convergence. In this paper, we provide fast rates analysis for the regret convergence. First, we show that given any estimate for the optimal quality function Q*, the regret of the policy it defines converges at a rate given by the exponentiation of the Q*-estimate’s pointwise convergence rate. The level of exponentiation depends on the level of noise in the decision-making problem. Second, we provide new analyses of FQI and Bellman residual minimization to establish the correct pointwise convergence guarantees. As specific cases, our results imply O(1/n) rates in linear cases and exp(− (n)) rates in tabular cases. need for more accurate call volumes forecasts is more essential. 3 - Geometric Optimization Approaches for Downsizing Logistics Problems

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