Yue Zhang 张越 - Research

I always look forward to collaborations with industry partners. My contact information can be found on the home page.

I am particularly interested in studying advanced technologies and their impact on operations and supply chain management. Below is a selection of my research work:

Zhang, X., Y. Xiao, Y. Zhang. 2025. Add a "Green" Option? Two-sided Pricing Strategies for Ride-Hailing Platforms with EV Penetration Targets. Under major revision.

In an effort to reduce urban carbon emissions, ride-hailing platforms such as Uber and Lyft have introduced "Green" ride options that allow riders to select environmentally friendly vehicles. This paper investigates the effectiveness of such options in a platform that matches electric vehicle (EV) and fuel vehicle (FV) drivers with eco-conscious and eco-unconscious riders. We build a model to examine the platform's optimal two-sided pricing strategies for scenarios with and without a "Green" option, accounting for an EV penetration target that the platform has to abide by. Without the "Green" option, the platform charges the same price to all riders but may offer differentiated wages for EV and FV drivers. Our analysis shows that the platform voluntarily offers higher wages to EV drivers, resulting in higher EV penetration. With the "Green" option, the platform is able to offer two-sided differentiated pricing across both the rider and driver types. However, this can trigger a de-pooling effect, resulting in a mismatch between rider demand and driver supply. Our finding reveals that, surprisingly, introducing a "Green" option does not necessarily guarantee a higher EV penetration rate, and can even lead to a "greenwashing" effect. That is, although seemingly "Green", it may backfire with worse environmental performance. Only when eco-conscious riders exhibit a moderate level of environmental concern can the "Green" option simultaneously enhance the platform's profitability and its environmental performance. Finally, regulatory bodies should proceed with caution, because setting an overly high EV penetration target may significantly harm the platform's profitability and in turn discourage the offering of the "Green" option.

Zhang, Y., B. Tomlin. 2025. Production-Equipment Rentals: Technical Service Design and Pricing for Customers of Varying Expertise. Under 2nd round review at Manufacturing & Service Operations Management.

Businesses with short-term production needs can rent equipment (e.g., 3D printer) from a manufacturer or distributor (hereafter, OEM), or use a manufacturing-services firm (e.g., print farm). Those who rent may vary in their expertise and therefore in their cost of attaining quality. Those who use a print farm benefit from the expertise of print-farm technicians. An OEM that offers rentals has an incentive to provide technical service to improve customer expertise so as to make renting attractive. We analytically study the rental-and-technical-service-design problem for an OEM, accounting for the heterogeneity in customer expertise and the choice of utility-maximizing customers to rent or to use a print farm. We consider both a setting where the OEM provides the same menu of offerings to all customers (population-based) and a setting where it customizes the offerings to each customer (user-based). We also study an extension where the print farm actively competes and one where the OEM owns the print farm. We characterize the optimal offering for the OEM, the resulting profit, the value of customization (difference in profits under user- and population-based), and the impact of expertise distribution on these terms. Our problem is related to vertical quality differentiation but leads to several contrasting insights, including that offering a single quality level can be optimal, that the deadweight loss can be lower for a more heterogeneous customer population, and that upward (instead of the classic downward) distortion occurs. We provide guidance on when technical service should be bundled with the rental and when it should be offered as an optional upgrade. Our findings highlight when rental markets with higher heterogeneity are more or less attractive. Conventional wisdom suggests that higher customer heterogeneity should increase the value of customization. We prove this wisdom is not always correct.

Song, J.-S. , Y. Zhang. 2025. Predictive 3D Printing of Spare Parts with IoT. Management Science. 71(3) 1925-1943

-This paper was selected for presentation at the MSOM Supply Chain Management SIG, 2022

-SSRN Top Ten download list, 2021

Industry 4.0 integrates digital and physical technologies to transform work management, where two core enablers are the Internet of Things (IoT) and 3D printing (3DP). IoT monitors complex systems in real-time, while 3DP enables agile manufacturing that can respond to real-time information. However, the details of how these two can be integrated are not yet clear. To gain insights, we consider a scenario where a 3D printer supplies a critical part to multiple machines that are embedded with sensors and connected through IoT. While the public perception indicates that this integration would enable on-demand printing, our research suggests this is not necessarily the case. Instead, the true benefit is the ability to print predictively. In particular, it is typically more effective for the 3D printer to predictively print-to-stock, based on a threshold that depends on the system’s status. We also identify a printing mode called predictive print-on-demand that allows for minimal inventory, and find the speed of 3DP to be the primary factor that influences its optimality. Furthermore, we assess the value of IoT in cost reductions by separately analyzing the impact of advance information from embedded sensors and the real-time information fusion through IoT. We find that IoT provides significant value in general. However, the conventional wisdom that IoT’s value scales up for larger systems is suitable only when the expansion is paired with appropriate 3DP capacity. Our framework can help inform investment decisions regarding IoT/embedded sensors and support the development of scheduling tools for predictive 3D printing.

Zhang, Y., W. McCall, J.-S. Song. 2024. Case - Digitizing Spare Parts Supply Chain via 3D Printing - An Operational Cost Analysis. Articles in Advance. INFORMS Transactions on Education.

- An earlier version of this case study was selected as Finalist of 2022 INFORMS Case Competition.

Zhang, Y., W. McCall, J.-S. Song. 2024. Case Article - Digitizing Spare Parts Supply Chain via 3D Printing - An Operational Cost Analysis. Articles in Advance. INFORMS Transactions on Education.

The case presents a sourcing problem and a manufacturing problem faced by an original equipment manufacturer, seeking recommendations for sourcing a diverse range of parts for high voltage equipment, as well as making decisions on the manufacturing strategy for a component used in a water monitoring system. The case provides an opportunity to explore the qualitative and quantitative aspects of 3D printing versus traditional manufacturing, specifically in terms of operational cost. Furthermore, this case facilitates discussions on the potential impact of 3D printing on supply chains. It is suitable for use in graduate and undergraduate courses, as it introduces key concepts such as manufacturing and inventory policies, queueing theory, and lifecycle analysis. Ultimately, the case is designed to promote a deeper understanding of the challenges and opportunities that manufacturers face in today’s rapidly evolving technological landscape.

Zhang, Y., B. Westerweel, R. Basten, J.-S. Song. 2022. Distributed 3D Printing of Spare Parts via IP Licensing. Manufacturing & Service Operations Management. 24(5) 2685-2702.

-SSRN Top Ten download list, 2020

Additive manufacturing, also known as 3D printing, has the potential to shift supply chains from global networks that rely on centralized production with traditional manufacturing technologies to mainly digital networks with distributed, local 3D printing. Particularly well positioned to drive this transition are original equipment manufacturers (OEMs) who design and produce capital goods. We consider an OEM supplying a single part to multiple buyers over an infinite horizon. We study how the OEM can digitize the spare parts supply chain by leveraging 3D printing via intellectual property (IP) licensing. We first set up a benchmark model of the traditional physical supply chain with centralized production by the OEM. We then propose the OEM to act as an IP licensor by selling spare parts designs, rather than physical parts. With the license agreement, a buyer can print spare parts locally through a third-party printing service provider, enjoying a much shorter lead time and lower setup cost. Given a license, each buyer chooses whether to switch to the IP licensing channel or stay in the traditional channel. The OEM selects the license terms to maximize his total profit across both channels. We characterize the OEM's optimal license and the resulting supply chain configuration. We show that 3D printing's competency in price plays a dominant role in decentralization. Through a numerical experiment with realistic parameter settings, we demonstrate that decentralized supply chain occurs in a surprisingly large number of cases. The proposed new business model can also significantly increase the OEM's profit. Our results indicate that IP licensing by OEMs can become a major enabler in the transition to digital supply networks with distributed 3D printing, benefiting all parties involved.

Song, J.-S., Y. Zhang. 2020. Stock or Print? Impact of 3D Printing on Spare Parts Logistics. Management Science. 66(9) 3860-3878.

-This paper won the First Prize in 2019 Columbia Business Initiative/CSAMSE Best Paper Award Competition

-This research was featured on Fuqua Insights (link), 3D Printing Industry (link) and Additive Manufacturing (link).

We present a general framework to study the design of spare parts logistics in the presence of 3D printing technology. We consider multiple parts facing stochastic demands, and adopt procure/manufacture-to-stock versus print-on-demand to highlight the main difference of production modes featured in traditional manufacturing and 3D printing. A multi-class priority queue with deterministic service times is employed to capture the intrinsic heterogeneity among spare parts and reflect the operational details of 3D printing. To minimize long-run average system cost, our model determines which parts to stock and which to print. We find that the optimal 3D printer's utilization increases as the additional unit cost of printing declines and the printing speed improves. The rate of increase, however, decays, demonstrating the well-known diminishing returns effect. We also find the optimal utilization to increase in part variety and decrease in part criticality, suggesting the value of 3D technology in tolerating large part variety and the value of inventory for critical parts. By examining the percentage cost savings enabled by 3D printing, we find that, while the reduction in printing cost continuously adds to the value of 3D printing in a linear fashion, the impact of the improvement of printing speed exhibits S-shaped growth. We also derive various structural properties of the problem and devise an efficient algorithm to obtain near-optimal solutions. Finally, our numerical study shows that the 3D printer is in general lightly used under realistic parameter settings but results in significant cost savings, suggesting complementarity between stock and print in cost minimization.

Chen, L., J.-S. Song, Y. Zhang. 2017. Serial Inventory Systems with Markov-Modulated Demand: Derivative Bounds, Asymptotic Analysis, and Insights. Operations Research. 65(5) 1231-1249.

-An earlier version of this paper won the Third Prize in 2016 Columbia Business Initiative/CSAMSE Best Paper Award Competition

In this paper, we consider the inventory control problem for serial supply chains with continuous, Markov-modulated demand (MMD). Our goal is to simplify the computational complexity by resorting to certain approximation techniques, and, in doing so, to gain a deeper understanding of the problem. To this end, we analyze the problem in several new ways. We first perform a derivative analysis of the problem's optimality equations, and develop general, analytical solution bounds for the optimal policy. Based on the bound results, we derive a simple procedure for computing near-optimal heuristic solutions for the problem. These simple solutions reveal a closer relationship with the primitive model parameters. Second, we perform asymptotic analysis with long replenishment lead time and establish an MMD central limit theorem. We further show that the relative errors between our heuristics and the optimal solutions converge to zero as the lead time becomes sufficiently long, with the rate of convergence being the square root of the lead time. Our numerical results reveal that our heuristic solutions can achieve near-optimal performance even under relatively short lead times. Third, we show that, by leveraging the Laplace transformation, the optimal policy becomes computationally tractable under the gamma distribution family. This enables us to numerically compare various heuristic solutions with the optimal solution and to demonstrate that our heuristic outperforms existing heuristics in most cases. Finally, we observe that the internal fill rate and demand variability propagation in an optimally controlled supply chain under MMD exhibit behaviors different from those under stationary demand.

Cheung, K. L., J.-S. Song, Y. Zhang. 2017. Cost Reduction Through Operations Reversal. European Journal of Operational Research. 259(1) 100-112.

In some manufacturing and service processes, several stages must be performed, but there is some freedom in the ordering of stages. Operations reversal means switching the order of two stages. Several authors have studied the benefits of operations reversal, focusing on the reduction of a certain variable's variance or a related measure. This paper focuses instead on cost. We construct a model with the standard objective of minimizing the long-run average inventory-related cost. First, by using stochastic orders, we identify conditions under which operations reversal reduces cost. We find that in some cases the variability and cost objectives agree on when operations reversal is beneficial, but in other cases, they disagree. In particular, when demands are multinomially distributed, variability reduction may be accompanied by cost increase. We show that to guarantee a lower cost, we need certain properties on the aggregated demand at the choice-level (such as demands for sweaters of the same color). Finally, we examine the effects of cost parameters and lead times on operations reversal under the cost measure.