INFORMS 2021 Program Book

INFORMS Anaheim 2021

SB37

4 - Data-driven Adaptive Robust Optimization for Resource Sharing During a Pandemic Pooyan Kazemian, Case Western Reserve University, Cleveland, OH, 02114-2509, United States, Esmaeil Keyvanshokooh, Mohammad Fattahy, Maryam Zokaeinikoo, Mark P. Van Oyen, Kenneth Freedberg Amid local outbreaks of COVID-19, many US hospitals canceled elective procedures to preserve ventilator capacity for COVID-19 patients. The virus spreads at varying rates, causing demand for care to peak at different times across different regions. Hence, sharing scarce portable resources can help alleviate local capacity shortfalls. We develop a data-driven adaptive robust simulation optimization method for allocating and relocating ventilators among different regions of multiple states to satisfy demand with fewer total ventilators. We conduct a case study of sharing ventilators among regions in Ohio and Michigan during the pandemic’s first peak in 2020. We demonstrate that ventilator demand could be satisfied using 22%-65% fewer ventilators with ventilator sharing than no sharing (status quo), thereby allowing hospitals to preserve more elective procedures. SB37 CC Room 210C In Person: Incentives for Collaborative Innovation General Session Chair: Sina Moghadas Khorasani, University of California-San Diego, San Diego, CA, 92130-2843, United States Co-Chair: Sanjiv Erat, University of California-San Diego, La Jolla, CA, 92093, United States Lakshmi Nittala, 1 - Optimal Feedback in Contests Sina Moghadas Khorasani, University of California San Diego, La Jolla, CA, 92130-2843, United States, Jeffrey Ely, George Georgiadis, Luis Rayo We derive an optimal dynamic contest for environments where the principal monitors effort through a coarse, binary performance measure and chooses prize- allocation and termination rules together with a real-time feedback policy. The optimal contest takes a stark cyclical form: contestants are kept fully apprised of their own successes, and at the end of each fixed-length cycle, if at least one agent has succeeded, the contest ends and the prize is shared equally among all successful agents regardless of when they succeeded; otherwise, the designer informs all contestants that nobody has yet succeeded and the contest resets. 2 - Best or Right? Positioning and Authentication in Online Matching Platforms Sreekumar R. Bhaskaran, Southern Methodist University, Cox School of Bus. Dallas, TX, 75275-0333, United States, Amit Basu, Rajiv Mukherjee A firm seeking a business partner, or an individual searching for a life partner, can use an online matching platform not only to efficiently search for available candidates, but also to address two related challenges. First, a match-seeker may not know what candidates would be compatible with them. And second, particularly in the online setting, candidates may misrepresent their credentials. In this paper, we model and analyze whether an online matching platform’s decisions should enhance search with a positioning capability that helps match- seekers determine the subjective compatibility of potential matches (horizontal differentiation), and also whether it should offer an authentication service that enables match-seekers to reliably signal their objective quality (vertical differentiation). 3 - Learning and Doing in Contests Lakshminarayana Nittala, University of Dayton, Anderson Center Dayton, OH, 45469, United States, Sanjiv Erat Innovation contests have been studied primarily as a mechanism to obtain extreme valued solutions. In the current work we propose that the conceptualization of innovation contests needs to be expanded to also consider the long term benefits from the knowledge/capabilities generated by the contestants’ efforts. We offer a novel model that explicitly includes the generation and utilization of knowledge by participants in an innovation contest and discuss implications for the design of contests.

SB38 CC Room 210D In Person: Mitigating Climate Risk in the Energy Sector — Emerging Business Models and Regulatory Interventions General Session Chair: Joonho Bae, University of Michigan Ross School of Business, Ann Arbor, MI, 48104, United States 1 - Uncertainty in Carbon Tax Policy and its Effect on Investment in Renewable Electricity Generating Capacity Thomas Palley, Indiana University, Bloomington, IN, United States, Asa Palley, Owen Wu We develop a model to study the effects of carbon policy uncertainty on utility investments in renewable generation to replace carbon-intensive generating capacity. We also consider uncertainty for a policymaker in setting a price on carbon given imprecise information about the true cost of a marginal unit of emissions. Numerically, we explore our analytical findings for a representative utility in the United States. Broadly, we find that utilities invest less in renewables when uncertainty is greater, preferring to wait until future periods when the uncertainty is resolved. 2 - Investment Decisions for a Microgrid Price Dependent and Independent Demand Cases Fariba Farajbakhsh Mamaghani, Tulane University, New Orleans, LA, United States, Metin Cakanyildirim Traditional electric grids can be improved in terms of competition, reliability and availability of transmission capacity by building microgrids. A microgrid is a group of local generators and consumers that primarily transact with each other, buy excess demand from the grid and sell excess supply thereto. It comes in a variety of sizes and costs depending on its size. Its gains and losses from transactions with the grid are related to demand and price dependency as well as its size. Finding the optimal capacity by considering demand randomness and dependency factors is a challenge. In this paper, we provide a profit maximization formulation for a microgrid and reveal the effect of demand and price dependency on the optimal capacity and the investment decisions. 3 - Performance-Based Contracts for Energy Efficiency Projects Ali Shantia, Toulouse Business School, Jourdain, Toulouse, 31000, France, Sam Aflaki, Roman Kapuscinski, Liang Ding Energy Service Companies use performance-based contracts for Energy Efficiency projects. The performance of these contracts, however, is unverifiable by the clients. The achieved efficiency also encourages the client to consume more energy (the rebound effect). We show that the mentioned effects, along with the client’s risk aversion, diminish the performance of such contracts; therefore, they never achieve the first-best (FB) outcomes. We define and characterize a group of piece-wise linear contracts that perform reasonably well when FB outcome is difficult to achieve. 4 - Cost-saving Synergy: Demystifying Energy Stacking with Battery Energy Storage Systems Joonho Bae, University of Michigan Ross School of Business, Ann Arbor, MI, United States, John M. Silberholz, Roman Kapuscinski Despite the great potential of a battery energy storage system (BESS) to an electrical grid, most stand-alone use of BESS is not economical due to its high upfront cost and batteries’ limited lifespan. Energy stacking, a strategy providing more than two applications simultaneously with a single BESS, has been of great interest to improve profitability. However, some key questions remain unanswered in the literature. Using the two typical battery applications, we show that there always exists cost-saving synergy, which explains why stacking may be beneficial. This paper is the first to use analytical modeling to systematically characterize the stacking synergy and establish general lessons.

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