Session Overview |
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SA 8:30-10:00 | SA04 - SIG Service1: Fairness in online resource allocation Location: Forum 6 Session Chair: Jing Dong Session Chair: Rouba Ibrahim |
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Fair assortment planning 1Massachusetts Institute of Technology, United States of America; 2Institut Teknologi Bandung, Indonesia We introduce and study a fair assortment planning problem, where any two products with similar merits are offered similar visibility. We propose a framework to find near-optimal solutions to this problem, using the Ellipsoid method and a separation oracle to its dual. We then develop two approximate separation oracles, which result in a polynomial-time 1/2-approx. algorithm and an FPTAS for our problem. We conclude with a case study on a movie dataset, showing the efficacy of our algorithms. Fair dynamic rationing 1Yale School of Management; 2University of Chicago, Booth School of Business; 3Stanford Graduate School of Business Social planners often aim to equitably and efficiently ration a social good among agents whose (possibly correlated) demands realize sequentially. We design a simple adaptive policy that simultaneously achieves the best-possible guarantees on the expected minimum fill rate and the minimum expected fill rate, where each agent's fill rate is determined by an irrevocable, one-time allocation. We complement our results with a numerical study motivated by the rationing of COVID-19 medical supplies. |
SA 8:30-10:00 | SA03 - SIG Sustainable1: Green Mobility: Government Regulations and Data Analytics Location: Forum 7 Session Chair: Can Zhang Session Chair: Yangfang Helen Zhou |
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Curbing emissions: environmental regulations and product offerings across markets 1University of Science and Technology of China; 2University of Texas at Dallas The Trump administration’s 2018 announcement to freeze the EPA standard threatened to widen its gap from the CARB standard and cause a split market where automakers offer differentiated car models in CARB and non-CARB states. Inspired by this crisis, we adopt a game-theoretic model where two regulators set efficiency standards in their respective markets. We show that horizontal negotiations and vertical negotiations can both unify a split market and reduce emissions. Planning bike lanes with data: ridership, congestion, and path selection 1University of Toronto; 2University of California - Los Angeles, United States of America Bike lane expansion promotes cycling and reduces car traffic, but narrows vehicle lanes and amplifies congestion. We study the bike lane planning problem considering the conflicting effects. In an extensive case study in Chicago, we present a consistent estimator for travel-time function and optimize new bike lane locations while enforcing traffic equilibrium. We estimate 25 miles of new bike lanes increase cycling ridership by 76%, with at most an 8% increase in driving time between each OD pair. |
SA 8:30-10:00 | SA02 - SIG Healthcare1: Primary Care Location: Forum 8 Session Chair: Jonas Jonasson Session Chair: Pengyi Shi |
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Continuity of care increases clinical productivity in primary care 1University of Cambridge, United Kingdom; 2INSEAD, Singapore; 3University of Cambridge, United Kingdom Relational Continuity (RC) in primary care confers many reported benefits, yet it has been in sharp decline. Using multiple econometric techniques on a large consultation-level dataset comprising of 5M patients registered with 300 primary care practices across the UK over the course of 10 years, we find that RC has a significant productivity benefit, with operational and strategic implications for primary care practices and third-party payers. The power of data: Assessing primary care performance using routinely collected Emergency Department data 1London Business School; 2Erasmus University Rotterdam; 3Guy's and St Thomas' NHS Foundation Trust and Kings College London The rising demand for Emergency Department (ED) care is partially driven by the failure to provide timely and high-quality primary care, resulting in patients being forced to use EDs. It is therefore important to identify primary care practices (PCPs) whose patients place a lower burden on ED departments so that best practices can be identified and disseminated, and practices whose patients place a higher burden in order to provide support. This work develops and validates one such methodology. |
SA 8:30-10:00 | SA01 - SIG SCM1: Inventory Innovations Location: Forum 12 Session Chair: Rachel Chen Session Chair: Luyi Gui |
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Learning from the aggregated optimum: decision rules for managing ameliorating food inventory 1Technical University of Munich, Germany; 2INESC TEC, Faculty of Engineering of University of Porto, Portugal The management of ameliorating food inventories with age-differentiated products entails a trade-off between immediate revenues and further maturation. We derive interpretable decision rules for purchasing, fulfilment, and issuance decisions under purchase price and decay uncertainty. We learn the rules from the optimal policy for an aggregated problem. A linear program facilitates scaling back. For a port wine industry case, our derived management strategies yield a substantial profit increase. Fixing inventory inaccuracies at scale 1MIT; 2CMU We observe that detecting inventory inaccuracies can be viewed as a problem of identifying anomalies in a (low-rank) Poisson matrix. We propose a conceptually simple approach whose cost approaches that of an optimal algorithm at a min-max optimal rate. Using data from a consumer goods retailer, we show that our approach provides up to a 10× cost reduction over incumbent approaches to anomaly detection. |
SA 8:30-10:00 | SA05- SIG iFORM1: Crowdfunding Location: Forum 13 Session Chair: Gerry Tsoukalas Session Chair: Yuqian Xu |
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Product development in crowdfunding: Theoretical and empirical analysis 1UCL School of Management, University College London; 2Rotterdam School of Management, Erasmus University Rotterdam; 3Anderson School of Management, University of California Los Angeles Entrepreneurs often use crowdfunding to solicit feedback from customers to improve their products, and may therefore prefer to launch crowdfunding campaigns for a basic version of their products. Yet, customers may not be persuaded by a campaign if a product appears too basic. Analyzing a game-theoretical model and testing its predictions empirically, we study how a product’s level of enhancement at campaign launch influences product improvements during campaign and campaign success. |
Coffee break | S 10:00-10:30: Coffee break Sunday morning |
SB 10:30-12:00 | SB04 - SIG Service2: Machine learning in action Location: Forum 6 Session Chair: Jing Dong Session Chair: Rouba Ibrahim |
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Cold start to improve market thickness on online advertising platforms: data-driven algorithms and field experiments 1University of Illinois at Urbana Champaign, United States of America; 2Washington University in St. Louis; 3Arizona State University; 4New York University Shanghai To solve the cold start problem on advertising platforms, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness on the platform, and propose a bandit algorithm to solve the problem. We also demonstrate the effectiveness of our algorithm via a novel two-sided randomized field experiment, and show our algorithm increases the cold start success rate by 62% and boosts the platform’s overall market thickness by 3.1%. Synthetically Controlled Bandits 1Operations Research Center, Massachusetts Institute of Technology, United States of America; 2Graduate School of Business, Columbia University, United States of America; 3Sloan School of Management, Massachusetts Institute of Technology, United States of America; 4Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, United States of America We present a dynamic experimental design for settings where the experimental units are coarse (e.g. to mitigate interference). `Region-split' experiments on online platforms are one such setting. Our design, dubbed Synthetically Controlled Thompson Sampling (SCTS), minimizes the cost (i.e. regret) associated with experimentation at no meaningful loss to inferential ability. We provide theoretical guarantees and experiments highlighting the merits of SCTS relative to fixed and switchback designs. |
SB 10:30-12:00 | SB03 - SIG Sustainable2: Combating Food Waste Location: Forum 7 Session Chair: Can Zhang Session Chair: Yangfang Helen Zhou |
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Estimating Stockout Costs and Optimal Stockout Rates to improve the Management of Ugly Produce Inventory 1Michigan State University, United States of America; 2Arizona State University, United States of America; 3Seoul National University, South Korea Efficiently managing inventories requires an accurate estimation of stockout costs. This estimation is complicated by challenges in determining how to compensate consumers monetarily so that they will maintain the same level of utility had stockouts not occurred. This paper presents an analysis of these compensation costs, as applied to the design of optimal stockout rates by an online retailer selling to consumers aesthetically substandard fruits and vegetables rejected by mainstream grocers. On the Management of Premade Foods 1The HKUST Business School, Hong Kong S.A.R. (China); 2Stanford University We examine a grocery retailer's management of premade food. The retailer's objective is to maximize the direct profit plus (weighted) customer welfare generated the food product. The retailer chooses: the shelf life, FIFO vs. LIFO issuance, and whether or not to time-stamp items. Our first main result is that LIFO issuance is universally optimal. Second, the retailer time-stamps items if the disposal cost for unsold items is low or the retailer puts sufficient weight on customer welfare. |
SB 10:30-12:00 | SB02 - SIG Healthcare2: Volatility and workload of providers Location: Forum 8 Session Chair: Jonas Jonasson Session Chair: Pengyi Shi |
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"I Quit": Schedule volatility as a driver of voluntary employee turnover The Wharton School, University of Pennsylvania, United States of America We examine how employer-driven volatility in workers' schedules impacts their decision to voluntarily leave their job. Using time-stamped work log data of home health nurses, we construct and study an operational measure of schedule volatility. Using an instrumental variables approach, we find that higher levels of schedule volatility substantially increase workers' likelihood of quitting. Using policy simulations, we illustrate how schedule volatility, and employee turnover, could be mitigated. Does What Happens in the ED Stay in the ED? The Effects of Emergency Department Physician Workload on Post-ED Care Use 1Alberta School of Business, University of Alberta; 2Wisconsin School of Business, University of Wisconsin-Madison; 3School of Medicine and Public Health, University of Wisconsin-Madison Using a data set assembled from detailed ED visit-level data and exhaustive billing data in an integrated health system, we show that there is an increasing concave relationship between ED physician workload and post-ED care use. Further, we identify ED physician test ordering behavior as a mechanism of these effects. Together, these findings suggest that when ED physician workload increases, resource utilization increases in the ED and several other channels of care in the healthcare system. |
SB 10:30-12:00 | SB01 - SIG SCM2: Data-Driven Models Location: Forum 12 Session Chair: Rachel Chen Session Chair: Luyi Gui |
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A data-driven model of a firm's operations with application to cash flow forecasting Cornell University, United States of America A firm’s cash flow from operations is a function of the contemporaneous and lagged values of its operational variables---sales, operating cost, inventory, payables, etc. Estimating this function is important for forecasting and managing cash flows. However, cash flow forecasting is a challenging problem. In this paper, we propose a generalizable and data-driven model of a firm's operations to disentangle this endogeneity and estimate causal impacts among variables. How big should your data really be? Data-driven newsvendor and the transient of learning Columbia University, United States of America We study the data-driven newsvendor problem in which the decision-maker must trade-off underage and overage costs and only observes historical demand. Our metric of interest is the worst-case relative expected regret, compared to an oracle knowing the demand distribution. We provide an exact analysis of Sample Average Approximation across all data sizes. We also derive a minimax optimal algorithm and its performance. Our work reveals that tens of samples are sufficient to perform efficiently. |
SB 10:30-12:00 | SB05 - SIG iFORM2: Implications of Blockchain technology for operations Location: Forum 13 Session Chair: Gerry Tsoukalas Session Chair: Yuqian Xu |
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Supply chain transparency and blockchain design 1Samuel Curtis Johnson Graduate School of Management, Cornell University; 2School of Business, Nanjing University We consider two ways that blockchain can enhance supply chain transparency: (1) making the manufacturer’s sourcing cost transparent to the buyers (vertical cost transparency), and (2) making the ordering status of buyers transparent to each other (horizontal order transparency). We develop prescriptions to supply chain practitioners with regard to when blockchain should be adopted, who should be the initiator, and how to design the blockchain’s access control layer for the logistics data. Accounts receivable tokenization in multitier supply networks 1School of Management and Engineering, Nanjing University; 2Whitman School of Management, Syracuse University Accounts receivable can be turned into digital assets that program ownership and the flow of cash into transferable tokens that can either be sold on at a discount via factoring or be passed on to the upstream of the supply chain as a payment instrument. This paper investigates how accounts receivable tokenization impacts the multitier supply chain's decisions and profits under different configurations and contractual forms. Measuring utility and speculation in blockchain tokens University of Michigan, Ross School of Business Problem Definition: A large segment of cryptoassets consists of tokens that serve as a payment or governance mechanism for a digital platform, usually a peer-to-peer marketplace of various services. An ongoing debate about the viability of the token market is centered on whether tokens are used purely for speculation, or have actual utility on their underlying platforms. The objective of this study is to create and validate a set of granular measures of token utility and speculation. |
Lunch Break | S 12:00-13:00: Sunday Lunch |
SC 13:00-14:30 | SC04 - SIG Service3: Managing queues in service systems Location: Forum 6 Session Chair: Jing Dong Session Chair: Rouba Ibrahim |
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The psychology of virtual queue: when waiting feels less like waiting 1Vanderbilt University; 2California State University, Stanislaus, United States of America; 3Vanderbilt University We use a text mining approach to extract waiting complaints from over 0.72 million online customer reviews of restaurants and conduct difference-in-differences regressions to estimate the impact of Virtual Queue (VQ). We find that VQ reduces customers' pre-process waiting complaints and does not lead to in-process waiting complaints increase. VQ also enhances customers' overall satisfaction. Service providers who face high substitutability or offer low-value service are benefited most from VQ. Fair scheduling of heterogeneous customer populations University of Southern California, United States of America When managing service systems, it is common to use priority rules based on some operational criteria. We consider the societal implications of such individual-focused priority policies, when individuals are members of broader population groups. We find that optimal service policies can lead to significant inequity across population groups. We propose policies that generate equitable outcomes across populations with little, or at times, even no additional system cost. |
SC 13:00-14:30 | SC03 - SIG Sustainable3: Socially Responsible Operations Location: Forum 7 Session Chair: Can Zhang Session Chair: Yangfang Helen Zhou |
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Unmasking human trafficking risk in commercial sex supply chains with machine learning 1Wharton Business School, University of Pennsylvania; 2Uncharted Software, TellFinder Alliance The covert nature of sex trafficking provides a barrier to generating large-scale, data-driven insights to inform law enforcement, policy and social work. We leverage massive deep web data (collected from leading commercial sex websites) with a novel machine learning framework to study how and where sex worker recruitment occurs. We provide a geographical network view of commercial sex supply chains, highlighting deceptive recruitment-to-sales pathways that signal high trafficking risk. The effect of social impact language on employee recruitment 1University of Pittsburgh, Pittsburgh, PA; 2Carnegie Mellon University, Pittsburgh, PA Firms use social impact claims to attract workers, but the credibility of these claims is understudied. We suggest that when social impact is presented as corporate purpose, firm capacity is a key source of credibility. Using an online job board, we use topic modeling to confirm that (i) firms present social impact as purpose, (ii) purpose claims attract job seekers, and (iii) the latter effect is moderated by firm size. We experimentally confirm that perceptions of capacity drive our results. |
SC 13:00-14:30 | SC02 - SIG Healthcare3: The operational aftermath of the pandemic Location: Forum 8 Session Chair: Jonas Jonasson Session Chair: Pengyi Shi |
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Dynamic capacity management for deferred surgeries 1Southern Methodist University; 2Zuse Institute Berlin; 3University of Texas at Austin; 4Benefits Science Technologies The COVID-19 pandemic has necessitated widespread deferrals of elective surgeries, which non only increase the cost from deterioration of patients' conditions but also decrease the revenue. We develop a robust optimization framework for the dynamic surgical capacity management problem. Our Implementation for more than 15,000 hernia patients in the United States demonstrates sizable improvements over alternative methods by up to 10% with multiple practical insights on optimal expansion strategy. Quantifying the benefits of targeting for pandemic response 1Stanford University, USA; 2INSEAD, France We propose and implement a rigorous framework and algorithms to quantify the merits of targeted confinement interventions for pandemic response, and demonstrate them in a case study of COVID-19 in Île-de-France. We find that optimized interventions that differentiate based on both population groups and activities are interpretable, and achieve significantly better health and economic outcomes, while also reducing confinement time for each group, compared to less targeted interventions. |
SC 13:00-14:30 | SC01 - SIG SCM3: Revenue Management Location: Forum 12 Session Chair: Rachel Chen Session Chair: Luyi Gui |
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Network revenue management with nonparametric demand learning: \sqrt{T}-regret and polynomial dimension dependency 1McGill University, Canada; 2University of Florida This paper studies the classic price-based network revenue management (NRM) problem with demand learning. The retailer dynamically decides prices of n products over a finite selling season (of length T) subject to m resource constraints, with the purpose of maximizing the cumulative revenue. In this paper, we focus on nonparametric demand model with some mild technical assumptions which are satisfied by most of the commonly used demand functions. Optimal algorithm for solving composition of convex function with random functions and its applications in network revenue management 1University of Illinois at Urbana-Champaign, US; 2ETH Zurich, Switzerland Various operations management problems can be formulated as stochastic optimization under random truncation. Leveraging a convex reformulation, we propose a mirror gradient method that achieves global convergence for the nonconvex objective with optimal complexity. The proposed method only operates in the original space using estimators of the nonconvex objective and consistently outperforms several state-of-the-art control policies in passenger and air-cargo network revenue management. Joint assortment optimization and personalization Cornell University, United States of America We consider a joint customization and assortment optimization problem. A firm faces customers of different types, each making a choice according to a different MNL model. The firm picks an assortment of products to carry subject to a constraint. Then, a customer of a certain type arrives into the system and the firm customizes the assortment that it carries by, possibly, dropping products from the assortment. We study the value of customization, the complexity of the problem and design novel algorithms. |
SC 13:00-14:30 | SC05 - SIG iFORM3: Supply Chain Financing Location: Forum 13 Session Chair: Gerry Tsoukalas Session Chair: Yuqian Xu |
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Financing a sustainable supply chain 1Ivey Business School, Western University, Canada; 2School of Business, Sun Yat-sun University; 3CUHK Business School, The Chinese University of Hong Kong; 4McCombs School of Business, The University of Texas at Austin We study the role of financing in establishing supply chain (SC) sustainability. We consider an SC where a buyer sources from a financially constrained supplier, who borrows from either a bank or the buyer. We show that both bank financing and buyer financing cannot simultaneously manage SC sustainability and profitability. We thus propose an alternative, in which the supplier borrows from a bank but the buyer offers a reward if the supplier passes the audit, and demonstrates its effectiveness. Retailer-initiated inventory-based financing 1Peking University; 2IESE Business School, Spain We study the contract design and the effectiveness of the retailer initiated inventory-based financing (IBF) scheme. Using a game-theoretical model, we derive the small retailer’s optimal inventory ordering and pledging decisions during the stockpiling phase and characterize the optimal interest rate for the large retailer. We also empirically study the small retailer’s borrowing pattern and estimate the impact of the interest rate on the small retailers’ planning horizon and the loan amount. |
Coffee break | S 14:30-15:00: Coffee break Sunday afternoon |
SD 15:00-16:30 | SD04 - SIG Service4: Platform operations Location: Forum 6 Session Chair: Jing Dong Session Chair: Rouba Ibrahim |
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Structuring online communities 1Kellogg School of Management, Northwestern University; 2Wharton School of Business, University of Pennsylvania Users in online communities can ask questions and other users can answer these questions. Generally, question answerers get rewards while the askers gain knowledge if their questions get answered. We model the community as a stochastic game and find how users decide to participate in such communities. We theoretically validate the empirically observed network structure in such communities. Further, we find that the number of users in the community is non-monotonic in the participation cost. On-demand transportation: Drivers wages versus platform profit 1Columbia University, USA; 2Dartmouth College, USA Motivated by the debate around drivers' welfare in on-demand transportation, we propose a framework to evaluate current practices and possible alternatives. The platform allocates time slots to drivers, who are strategic agents maximizing their utility, which depends on their temporal preference (when to drive), slots they are allocated, and time they spend on-road. We use our framework to evaluate existing policies and propose improvements with respect to platform profit and drivers' wages. |
SD 15:00-16:30 | SD03 - SIG Sustainable4: Emerging Topics: Agricultural Operations and Ocean Waste Recycling Location: Forum 7 Session Chair: Can Zhang Session Chair: Yangfang Helen Zhou |
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Innovative business models in ocean-bound plastic recycling 1Rotman School of Management, University of Toronto, Canada; 2CUHK Business School, The Chinese University of Hong Kong (CUHK), Shatin, N.T., Hong Kong 30 million tons of plastic reach the ocean each year, most from developing countries. We study novel business models to address this problem. Firms profitably recycle plastic to reduce ocean pollution while positively impacting local communities. They sell (a) plastic offsets and (b) segregated plastic. We analyze a supply chain model of (a), (b) or both. Adopting both attains larger environmental and social impacts and profitability. We use empirical data to unveil additional insights. Improving cash-constrained smallholder farmers' revenue: The role of government loans 1Massachusetts Institute of Technology; 2University of Southern California A critical challenge faced by smallholder farmers is that the need for immediate cash often forces them to sell their crops at sub-optimal times. This paper develops a game-theoretic model to examine how cash constraints influence farmers' selling decisions across the harvest and lean seasons, as well as to analyze the efficacy of government loan programs in improving farmers' revenue. Finally, we use field data of Bengal gram farmers in India to empirically validate and quantify our insights. |
SD 15:00-16:30 | SD02 - SIG Healthcare4: Patient flow in healthcare systems Location: Forum 8 Session Chair: Jonas Jonasson Session Chair: Pengyi Shi |
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Design of patient visit itineraries in tandem systems 1Boston College, United States of America; 2Shanghai Jiao Tong University, China; 3Sun Yat-sen University, China In many healthcare settings, patients receive a series of services during a single visit, e.g., infusion care and orthopedic visit. A key commonality is the tandem structure where each stage involves a non-trivial random service time. We develop the first analytic model to provide each patient an individualized visit itinerary in a tandem health service system. A case study populated by data from a large infusion center shows that our approach makes a remarkable 27% cost reduction over practice. What causes delays in admission to rehabilitation care? A structural estimation approach 1Decision, Risk, and Operations, Columbia Business School; 2Department of Mechanical and Industrial Engineering, University of Toronto, Canada Delays in admission to rehabilitation care can be both capacity-driven and/or due to processing delays. Standard data however only includes a single measure of delay, and the bed allocation decisions in practice do not follow a systematic policy. We propose a hidden Markov model to estimate the processing times and the status-quo bed allocation policy. We validate our structural model and conduct counterfactual experiments to evaluate various operational interventions aimed at reducing delays. |
SD 15:00-16:30 | SD01 - SIG SCM4: E-commerce Analytics Location: Forum 12 Session Chair: Rachel Chen Session Chair: Luyi Gui |
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Online advertisement allocation under customer choices and algorithmic fairness 1National University of Singapore; 2Shanghai Jiao Tong University; 3New York University Shanghai; 4The Chinese University of Hong Kong In this paper, we explore dynamic ad allocation with limited slots upon each customer arrival for e-commerce platforms when customers follow a choice model to click the ads. Motivated by the recent advocacy for the algorithmic fairness, we adjust the value from advertising by a general fairness metric evaluated with the click-throughs of different ads and customer types. We propose a two-stage stochastic program and design a debt-weighted offer-set algorithm to solve the online problem. Designing Sparse Graphs for Stochastic Matching with an Application to Middle-Mile Transportation Management 1National University of Singapore; 2University of Chicago; 3Zhejiang Cainiao Supply Chain Management Co., Ltd Motivated by the middle-mile delivery operations of an e-retailer, we consider the problem of designing a sparse graph that supports a large matching after random node deletion. We study three families of sparse graph designs (namely, Clusters, Rings, and Erdos Renyi graphs) and show that their performances are close to the complete graph. We test our theory using real data and conclude that adding a little flexibility to the routing network can significantly reduce transportation costs. Simple and order-optimal correlated rounding schemes for multi-item e-commerce order fulfillment Columbia University, United States of America We provide the first improvements to the celebrated correlated rounding procedure of Jasin and Sinha (2015), which has become a fundamental problem in multi-item e-commerce order fulfillment. We derive rounding schemes with guarantees of $1+\ln(n)$ and $d$, where $d$ is the maximum number of fulfillment centers containing an item. The first of these improves their guarantee of ~n/4 by an entire order of magnitude in terms of the dependence on $n$. We also show our guarantees to be tight. |
SD 15:00-16:30 | SD05 - SIG iFORM4: Capacity/Inventory Management under Financial Risks Location: Forum 13 Session Chair: Gerry Tsoukalas Session Chair: Yuqian Xu |
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Capacity expansion in service platforms: financing vs. employment 1Imperial College London, United Kingdom; 2London Business School, United Kingdom Service platforms connecting consumers to independent service providers are now ubiquitous in industries such as ride-hailing, food delivery, and accommodation. We study how a platform may expand capacity through either financing new providers' assets (e.g., cars), or investing in assets directly and employing the providers. We use a game-theoretic model to show when the platform prefers to expand by either scheme compared to conventional bank financing, and how this affects provider profits. Reshoring under tariff uncertainty and competition 1Washington University in St. Louis; 2Washington University in St. Louis; 3University of Miami Recent development in the U.S. tariff policies has forced companies to rethink their global operational strategies, particularly whether to add a domestic production location that is immune to tariffs. This paper formulates a three-stage model to analyze the global firm's reshoring capacity, output quantity, and production decisions. We examine how reshoring capacity investment is affected by domestic competition and by tariff uncertainty at both the raw-material and finished-goods level. |
SE 17:00-18:30 | SE04 - SIG Service5: Evidence-based approach in operations management Location: Forum 6 Session Chair: Jing Dong Session Chair: Rouba Ibrahim |
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Identifying the bottleneck unit: Impact of congestion spillover in hospital inpatient unit network 1SNU Business School, Korea, Republic of (South Korea); 2Columbia Business School, USA; 3Keck Medicine of USC, USA We use 5-year data from a hospital with 16 inpatient units to empirically examine whether and how much congestion propagates through the network of inpatient units. We find that the magnitude of the congestion spillover is substantial in our study hospital. We then use counterfactual analyses to empirically identify the bottleneck unit---the unit that has the biggest impact on system performance when an intervention is applied to increase its capacity. Capping mobile data access creates value for bottom-of-the-pyramid consumers – experimental evidence from a Mumbai settlement London Business School, United Kingdom Via an app we developed, we identify a barrier to digital information access by the poor – data shortages. In a Mumbai slum, we randomly assigned respondents to a data plan with daily replenishment cycles – or a standard plan. Our data reveal that absent caps, respondents binge on YouTube and social media, resulting in subsequent data shortages. The capped plan increases late-plan access of WhatsApp invites to health camps, increases attendance at these camps, and reduces social media checking. |
SE 17:00-18:30 | SE03 - SIG Sustainable5: Energy Operations: Efficient Electricity Market and Integration of Energy Storage Location: Forum 7 Session Chair: Can Zhang Session Chair: Yangfang Helen Zhou |
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Renewable, flexible, and storage capacities: Friends or Foes? Indiana University, United States of America We study the investment relations among the renewable, flexible, and storage capacities. We optimize the joint operations of these three types of resources. We then optimize the investment mix of these resources and examine the investment relations among them. We find that whether storage complements or substitutes other resources depends on how storage reduces operating cost and whether the potential cost reduction is constrained by charging or discharging. Aggregating distributed energy resources: efficiency and market power 1The University of Chicago Booth School of Business, United States of America; 2King Fahd University of Petroleum and Minerals The rapid expansion of distributed energy resources (DERs) is one of the most significant changes to electricity systems. We study in this paper two models to aggregate DERs. In the first model, a profit-seeking aggregator procures electricity from DERs, and sells them in the wholesale market. In the second model, a uniform two-part pricing policy is applied to DER owners, while the aggregator becomes fully regulated but is guaranteed positive profit. Both models are shown to be fully efficient. |
SE 17:00-18:30 | SE02 - SIG Healthcare5: Medical and operational decision making Location: Forum 8 Session Chair: Jonas Jonasson Session Chair: Pengyi Shi |
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Individualized dynamic patient monitoring under alarm fatigue 1University of British Columbia, Canada; 2University of Alberta, Canada Hospitals are rife with alarms, many of which are false. This leads to alarm fatigue, in which clinicians become desensitized and may inadvertently ignore real threats. We develop a partially observable Markov decision process model for recommending dynamic, patient-specific alarms. We find that compared to current approaches of setting patients’ alarms, our dynamic patient-centred model significantly reduces the risk of patient harm. Split liver transplantation: An analytical decision support model 1Carnegie Mellon University, United States of America; 2The University of California, San Francisco, United States of America Split liver transplantation (SLT) can save two lives using one liver. To facilitate increased SLT usage, we formulate a multi-queue fluid model, incorporating size matching specifics, dynamic health conditions, transplant type, and fairness. We find the optimal organ allocation policy, and evaluate its performance versus other common allocations. |
SE 17:00-18:30 | SE01 - SIG SCM5: Empirical Supply Chain Management Location: Forum 12 Session Chair: Rachel Chen Session Chair: Luyi Gui |
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Using Internet-of-Things Point-of-Consumption Data for smart Replenishment 1Kühne Logistics University, Germany; 2Technion – Israel Institute of Technology, Israel Newly emerging smart replenishment systems at the point-of-consumption track product usage via smart, connected devices and use this data to automate order processes. Based on a large industry dataset from the professional coffee industry, we develop models for demand forecasting, inventory control, and replenishment under inventory inaccuracies. Project networks and reallocation externalities 1Schulich School of Business, York University, Canada; 2Sauder School of Business, University of British Columbia, Canada; 3Desautels Faculty of Management, McGill University Project networks involve several participants; clients, contractors, and subcontractors; each working on multiple projects concurrently. By tracking a network of 2.6 million public projects over a five-year span, we show that when a project suffers a localized disruption, other projects in the network get delayed because participants reallocate resources to the disrupted project. This creates a domino-effect externality that ripples through the network, causing delays across unrelated projects. Predictive 3D printing with IoT 1Duke University, United States of America; 2Pennsylvania State University, United States of America We consider the problem of a 3D printer supplying a critical part installed in multiple machines embedded with sensors and interconnected via IoT. We show that it is optimal to print-to-stock predictively in advance of demand, triggered by a system-lifetime-status dependent threshold. We further quantify the impact of IoT on system cost and inventory by separately assessing the impact of advance demand information from embedded sensors and that of IoT's real-time information fusion. |
SIG Dinner | S 19:30-22:00: SIG Dinner |