MSOM Society Student Paper Competition: Abstracts of 2025 Winners

The 2025 prize winners are as follows:

First Prize

Optimizing the Path Toward Plastic-Free Oceans

Baizhi Song, London Business School

Second Prizes (in alphabetical order according to the author’s last name)

Rented Today, Bought Tomorrow: Buyout Pricing in the Circular Economy

Borja Apaolaza, The Wharton School, University of Pennsylvania

The Impact of Information-Granularity and Prioritization on Patients’ Care Modality Choices

Lin Zang, Stanford Graduate School of Business

Finalist

The NHS Blended Payment Scheme: Incentive Issues and Optimal Reform

Sara Mohammadi, UCL School of Management, University College London

Optimizing the Path Toward Plastic-Free Oceans

Baizhi Song

London Business School, bsong@london.edu

Coauthors: Dick den Hertog, University of Amsterdam; Jean Pauphilet, London Business School; Yannick Pham, The Ocean Cleanup; Bruno Sainte-Rose, The Ocean Cleanup

Advisor: Jean Pauphilet, London Business School

Increasing ocean plastic pollution is irreversibly harming ecosystems and human economic activities. We partner with a nonprofit organization and use optimization to help clean up oceans from plastic faster. Specifically, we optimize the route of their plastic collection system in the ocean tomaximize the quantity of plastic collected over time. We formulate the problem as a longest path problem in a well-structured graph. However, because collection directly impacts future plastic density, the corresponding edge lengths are nonlinear polynomials. After analyzing the structural properties of the edge lengths, we propose a search-and-bound method, which leverages a relaxation of the problem solvable via dynamic programming and clustering, to efficiently find high-quality solutions (within 6% optimal in practice) and develop a tailored branch-and-bound strategy to solve it to provable optimality. On one year of ocean data, our optimization-based routing approach increases the quantity of plastic collected by more than 60% compared with the current routing strategy, hence speeding up the progress toward plastic-free oceans.

Rented Today, Bought Tomorrow: Buyout Pricing in the Circular Economy

Borja Apaolaza

The Wharton School, University of Pennsylvania, apaolaza@wharton.upenn.edu

Coauthors: Gerard Cachon, The Wharton School, University of Pennsylvania; Santiago Gallino, The Wharton School, University of Pennsylvania; Antonio Moreno, Harvard Business School

Advisor: Santiago Gallino, The Wharton School, University of Pennsylvania

Online rental platforms that allow customers to purchase rented goods present a complex pricing challenge: setting buyout prices that balance immediate sales revenue with future rental income, accounting for item-specific factors such as condition, popularity, and customer preferences. In this paper, we develop, estimate, and validate a data-driven framework to inform buyout prices in this setting. Leveraging a Markov decision process, our framework assesses individual item value based on rental demand, product attrition, and customer purchase likelihood. We use real-world data from a leading fashion rental company to demonstrate that our methodology significantly improves profitability compared with existing practices and alternative benchmarks. We estimate that the proposed pricing policy increases earnings by 3.1% over the company’s current practice. Our analysis also shows that operating a rental-only business model leaves revenue opportunities untapped, underscoring the strategic value of buyout options in managing inventory and generating additional income.

The Impact of Information-Granularity and Prioritization on Patients’ Care Modality Choices

Lin Zang

Simon Business School, University of Rochester

Current affiliation: Stanford Graduate School of Business, lzang@stanford.edu

Coauthors: Yue Hu, Stanford Graduate School of Business; Ricky Roet-Green, Simon Business School, University of Rochester; Shujing Sun, Naveen Jindal School of Management, University of Texas at Dallas

Advisors: Ricky Roet-Green, Simon Business School, University of Rochester; Yue Hu, Stanford Graduate School of Business

The past few years have witnessed a significant expansion in telemedicine adoption by healthcare providers. On the one hand, telemedicine has the potential to increase patients’ access to medical appointments. On the other hand, because of the limitations of remote diagnostic and treatment methods, telemedicine may be insufficient for patients’ treatment needs and may necessitate subsequent in-person follow-up visits. To better understand this trade-off, we model the healthcare system as a queueing network providing two types of service: telemedicine and in-person consultations. We assume that an in-person visit guarantees successful treatment, whereas a telemedicine visit may fail to meet the patient’s treatment needs with a probability that is contingent on individual patient characteristics. We formulate patients’ strategic choices between these care modalities as a queueing game and characterize the game-theoretic equilibrium and the socially optimal patients’ choices. We further examine how improving patients’ understanding of their telemedicine suitability through predictive analytics at the online triage stage affects system performance. We find that increasing information granularity maximizes the stability region of the system but may not always be optimal in reducing the average waiting time. This limitation, however, can be overcome by simultaneously deploying a priority rule that induces the social optimum under specific conditions. Finally, leveraging real-world data from a large academic hospital in the United States, we perform a comprehensive case study that encompasses both the development of a prediction model for in-person follow-up needs and the implementation of effective information provision and prioritization strategies.

The NHS Blended Payment Scheme: Incentive Issues and Optimal Reform

Sara Mohammadi

UCL School of Management, University College London, sara.mohammadi.22@ucl.ac.uk

Coauthors and advisors: Kenan Arifoglu, UCL School of Management, University College London; Bilal Gokpinar, UCL School of Management, University College London

The National Health Service in England introduced the blended payment scheme for emergency care in 2019 as a mixed prospective payment model that combines fixed lump-sum payments under block contracts with variable activity-based payments under payment by results (PbR). Under this scheme, hospitals receive a fixed lump-sum payment at the start of the year, based on their planned activity or readiness, and a variable payment at year-end that adjusts for differences between planned and actual activity scaled by a variable rate but capped by break-glass provisions that limit extreme financial adjustment. We develop a game-theoretic model to analyze hospitals’ treatment cost and planned activity decisions under the blended payment, considering information asymmetry between hospitals and the regulator. We show that the blended payment scheme offers a middle ground and improves upon block contracts and PbR as validated by our empirical analysis. However, it fails to fully align hospital incentives for cost reduction and readiness. We identify three key limitations: (i) information asymmetry makes it difficult for the regulator to set the appropriate variable rate, (ii) break-glass provisions may weaken hospital incentives for readiness, and (iii) hospitals may face long-term budget deficits. To address these issues, we propose a modified blended payment that retains the fixed payment but reforms the variable payment by (i) setting the variable rate at 100%, (ii) adjusting payments based on deviations from average actual activity across all hospitals rather than each hospital’s own actual activity, and (iii) eliminating break-glass provisions. Our proposed payment model aligns hospital behavior with the social optimum and ensures a balanced long-term budget.