Research Methodologies: 


Research Interests: 

Research Papers


Problem Definition: Local food distributors play a vital role in connecting small and medium farms (SMFs) to customers, but they face increasing closures due to inconsistent supply and rising operational costs. These challenges disrupt local food systems, jeopardizing product availability and pricing stability. Collaborating with Tamarack Holdings (TH), a Michigan-based agri-food distributor, this study examines purchasing behaviors of businesses and leverage the behavioral insights to develop an incentive scheme to improve product availability and optimize resource allocation under constraints. 

Methodology/Results: Based on transaction data from TH, we identify two key dimensions of a customer order: the number of SKUs selected from the distributor’s assortment and the quantity of each chosen SKU. To model these dimensions, we apply a Multiple Discrete-Continuous Extreme Value (MDCEV) choice framework and estimate parameters of the underlying structural choice model. We then develop a clustering-based approach, based on insights from our empirical demand estimation, to identify priority products—those with high margins and more likely to be purchased frequently and in larger quantities – for targeted interventions. For these priority products, we design additional incentives to enhance their availability and formulate the problem as a constrained bi-level optimization problem. 

Managerial Implications: Our findings highlight the importance of integrating insights from demand estimation with the distributor’s resource allocation decision problem. We provide a systematic demand-driven framework to calculate product-level incentive amounts for farmers to improve product availability. Specifically, our model calibration results show that for the set of priority products, the average improvement in product availability is 43.1%. These results have broad implications for local food systems that extend beyond the context of our collaboration with TH. 



Problem Definition: One of the seventeen United Nations Sustainable Development Goals aims for inclusive and equitable quality education, with lifelong benefits, for all. Our work in this paper focuses on the operations of non-profit organizations (NPOs) that broaden access to high-quality education for underprivileged students. Specifically, we analyze the resource-allocation strategy of an NPO that adopts a two-stage structure in allocating resources to its beneficiaries, e.g., free pre-secondary education (first stage) for all underprivileged students in a target population, followed by sponsorships for post-secondary education (second stage) at leading institutions to those students who demonstrate commendable performance in the first stage. The lifetime outcomes of the beneficiaries depend on their own effort and the quality of the resources that the NPO provides.
Methodology/Results: We establish the strategic role of an NPO's resource-allocation strategy on the effort beneficiaries invest and their lifetime outcomes -- in particular, despite the supportive nature of the NPO's resources and despite possessing enough quantity of these resources to support all beneficiaries, we show why the NPO benefits from deliberately throttling access to the resources.
Managerial Implications: Our findings have important implications for the design of such support policies for NPOs. For a fixed endowment of resources, we demonstrate the effect of competition among the beneficiaries on their effort and lifetime outcomes. Likewise, for a fixed population of beneficiaries, we show the value of creating a strategic scarcity of resources to incentivize beneficiaries to exert more effort. Finally, when faced with multiple beneficiary subgroups, we identify when the NPO benefits from pooling the beneficiary subgroups vs. earmarking dedicated resources for each subgroup.
Abstract: We study the design of crowdsourcing contests in settings where the outputs of the contestants are quantifiable - e.g., a data-science challenge. This setting is in contrast to those where the output is only qualitative and cannot be objectively quantified - e.g. when the contest's goal is to design a logo. The literature on crowdsourcing contests focuses largely on ordinal contests, where the designer ranks the contestants' outputs, and awards are based on relative ranks. Such contests are ideally suited for the latter setting, where output is qualitative. For our setting (quantitative output), it is possible to design cardinal contests, where awards could be based on the actual outputs and not on their ranking alone - thus, the family of cardinal contests includes the family of ordinal contests. We study the problem of designing an optimal cardinal contest. We use mechanism design theory to derive an optimal cardinal mechanism and provide a convenient implementation - a decreasing reward-meter mechanism - of the optimal contest. We establish the practicality of our mechanism by showing that it is "Obviously Strategy-Proof", a recently introduced formal notion of simplicity in the literature. We also numerically examine the benefit of the optimal cardinal contest over the most popular ordinal contest - namely, the Winner-Takes-All  (WTA) contest. Our numerical analysis suggests that, for the contest designer, the average improvement provided by the optimal cardinal mechanism over the WTA contest is about 23%. For a given number of contestants, the benefit of the optimal cardinal mechanism is especially appreciable for projects where the ratio of the designer's utility to agents' cost-of-effort falls within a wide practical range. For projects where this ratio is very high, the expected profit of the best WTA mechanism is reasonably close to that of the optimal cardinal mechanism. 

  • The Impact of Co-location in Emissions Regulation Clusters on Traditional and Vendor-Managed Supply Chain Inventory Decisions,  with Nazli Turken and Avinash Geda. Published at Annals of Operations Research. [DOI] 
Abstract: Most research examining the response of supply chains to environmental regulations assumes that all parties are subject to the same regulations. However, it is uncommon for buyers and vendors to be located in the same place and subject to the same environmental regulations. This paper investigates the inventory and production decisions of a buyer and a vendor in a supply chain with multiple buyers and a single vendor that produces multiple products under various environmental regulations. Our analysis demonstrates that the multiple buyers and multiple products scenario can be simplified to the single buyer and single product case under certain conditions. We find that a cap-and-trade regulation applied to the buyer may increase or decrease the economic order quantity in traditional supply chains, but it consistently reduces the economic order quantity in vendor-managed inventories (VMI). Our results indicate that while the cap-and-trade regulation reduces the carbon emissions of the buyer in both VMI and traditional supply chains, it may increase the carbon emissions of the vendor. Additionally, we discover that carbon cap regulation, although less commonly used than cap-and-trade regulation, may further decrease the carbon emissions of both the buyer and vendor beyond the effects of a green buyer or green vendor. Moreover, we provide recommendations for actions that the buyer or vendor can take to offset the impact of changes in parameters on economic order or production quantities.