INFORMS 2021 Program Book

INFORMS Anaheim 2021

MB27

Guangzhi Shang, Associate Professor, Florida State University, Tallahassee, FL, United States, Michael Galbreth, Li Wang The impact of forward logistics has been studied extensively in the recent retail operations literature. We look into the understudied return logistics. A prominent feature of this reverse process is that the service is completed by a co-production process between the customer and the firm. 3 - Slow and Steady, or Fast and Furious? An Empirical Study About Omnichannel Demand Sensitivity to Fulfillment Lead Time Fangyun Tan, Southern Methodist University, Dallas, TX, 75275, United States, Stanley Lim, Fei Gao We examine a large data set of an Italian omnichannel furniture retailer to study channel-specific effects of fulfillment lead time on demand. This omnichannel retailer sells the same products and has the same product fulfillment across three channels showroom, online and catalog. We find that the showroom channel makes consumers less sensitive to fulfillment lead time than both online and catalog channels. This finding contradicts the common practical and theoretical assumption about homogeneous lead time sensitivity across channels. We also find that niche products and experience goods accentuate the difference of lead time sensitivity between showroom and non-physical channels. Our study highlights the previously-ignored fulfillment time sensitivity aspect of the physical store’s value. In Person: Social Responsibility and Sustainability in Supply Chains: Incentives, Monitoring, and Strategies General Session Chair: Huan Cao, Greenbelt, MD, 20770-4193, United States 1 - Deadstock Fabric: The Role of Upcycling and Postponement Strategies Xiaoyang Long, University of Wisconsin-Madison, Wisconsin School Of Bus. Madison, WI, 53706-1324, United States, Luyi Gui Over-production has long been a pain point for the fashion industry, as it leads to the accumulation of deadstock, i.e., inventories that do not sell. Deadstock not only hurts the profitability of fashion brands but also leads to severe waste if not treated properly. Motivated by this problem, our work investigates how adopting postponement strategies affects fashion brands’ fabric acquisition practices and the subsequent implications for the amount of deadstock (including both fabric and finished goods) in the system. We also analyze the interaction between brands’ postponement and upcycling strategies, as well as the impact of such interactions on deadstock reduction. Finally, motivated by the growing policy attention to the deadstock problem, we turn to the governmental perspective and analyze potential policy interventions to promote deadstock reduction. 2 - Rider Behavior and Efficiency of Bike Sharing Systems Huan Cao, Robert H. Smith School of Business, College Park, MD, United States, Tunay Tunca, Weiming Zhu, Lin Jianfeng We empirically explore rider incentives and e ciency of dockless bike sharing systems. We develop a novel framework to model the customer decision process by explicitly accounting the customer arrival and the commuter-to-bike distance. Using transactional data from a major Chinese bike sharing company, we estimate the usage drivers for the system. Based on the estimation results, we then run counterfactual analysis to demonstrate how the number of deployed bikes a ect the performance of the system. We also study measures to improve e ciency, and compare the e ectiveness of dockless versus dock-based systems. MB27 CC Room 206B In Person: Network Optimization: Network Influence General Session Chair: Majid Akhgar, Oklahoma State University, Stillwater, OK, United States 1 - A Randomized Solution Approach for Finding Groups with Maximum Betweenness Centrality Tomas Lagos, PhD Student, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, United States Group betweenness centrality (GBC) indices are widely used to quantify the importance of nodes relatively to the structure of a network. For a given group of nodes, it is defined as the proportion of pairwise shortest paths in a network that each contains at least one node from the group. The problem of finding the group (of some given cardinality) that maximizes GBC is known to be NP-hard. In this talk we discuss a randomized solution approach for solving this problem with some theoretical performance guarantees. The performance of our approach is demonstrated in a numerical study with real-life and randomly generated test instances. MB25 CC Room 205B

MB23 CC Room 204C In Person: Empirical Work in Service Operations General Session Chair: Carlos Bonet, Columbia University, New York, NY, 10025, United States 1 - Strategic Choices and Routing Within Service Networks: Modeling and Estimation Using Machine Learning Kenneth Moon, University of Pennsylvania, Philadelphia, PA, 19104-6340, United States Service networks with open routing by self-interested customers have drawn attention in the theoretical literature. However, these networks, including shopping centers and amusement parks, remain challenging to explore empirically. Large-scale trajectory datasets offer tremendous opportunities to understand customer motivations and behaviors but are complex to analyze. We develop structural empirical methods to recover customer demand preferences and congestion sensitivities from diverse trajectory patterns using machine learning. Specifically, we employ adversarial neural networks to handle the high- dimensional space of trajectory types. Key innovations collapse the dynamics of customer trajectory choices into static trajectory market shares and derive theoretically efficient incentive-compatibility bounds on customers’ preferences. 2 - Private vs. Pooled Transportation: Customer Preference and Congestion Management Kashish Arora, Cornell University, Ithaca, NY, 77300, United States, Fanyin Zheng, Karan Girotra In this work, we build a structural model to study customers’ preferences on prices and service features when choosing between private taxis and a scheduled shuttle service. Using the estimated model, we evaluate the efficacy of congestion surcharge policies in reducing congestion on the road. We also compare the efficacy of these policies with policies that reduce inconveniences associated with the shuttle service. We find that a 20% decrease in the walking inconvenience can achieve 35% of the total number of customers substituting from cabs to shuttles achieved as compared to the congestion surcharges. Our findings suggest that, by changing operations levers such as pooled service features, cities can achieve a substantial amount of the benefit from reducing congestion, without sacrificing customer welfare, compared with congestion surcharge policies. 3 - Lotteries for Shared Experiences Carlos Bonet, Columbia University, New York, NY, 10025, United States, Nick Arnosti We consider a setting where tickets for an experience are allocated by lottery. Each agent belongs to a group, and a group is successful if and only if its members receive enough tickets for everyone to participate. A lottery is efficient if it maximizes the number of agents in successful groups, and fair if it gives every group the same chance of success.The most widespread mechanism, the Individual Lottery, gives large groups a significant advantage and may award groups more tickets than they need. We show that these issues can lead to arbitrarily unfair and inefficient outcomes. We propose two alternatives — the Group Lottery and the Weighted Individual Lottery — and show that they are approximately fair and approximately efficient. In Person: Innovative Incentives in Sustainable Operations and Supply Chain Management General Session Chair: Shouqiang Wang, The University of Texas at Dallas, Richardson, TX, 75080-3021, United States 1 - Avoiding Fields on Fire: Information Dissemination Policies for Environmentally Safe Crop-residue Management Mehdi Farahani, MIT, Cambridge, MA, 75082, United States, Milind Dawande, Ganesh Janakiraman, Shouqiang Wang Agricultural open burning, i.e., the practice of burning crop residue to prepare land for sowing a new crop, is a major contributor to climate change. An agricultural machine, called Happy Seeder, which can sow the new seed without removing the residue, has emerged as the most effective alternative. We study how the government can use effective information-disclosure policies to minimize open burning. A Happy Seeder is assigned to process a group of farms in an arbitrary order. Farmers decide whether to burn their farms or to wait for the Happy Seeder, given the information provided by the government about the Happy Seeder’s schedule. We propose the class of dilatory policies that provide no information until a pre-specified period and then reveal the entire schedule. We show that the use of an optimal dilatory policy can significantly reduce CO2 and black carbon emissions. 2 - Impact of Return Logistics on Future Repurchase: A Service Co-production Perspective MB24 CC Room 205A

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