团队

Request-for-quote (RFQ) is the most commonly used mechanism for contractor selection in project outsourcing. Problem definition: We study how RFQ design interacts with the time-cost trade-off in project execution and compare two common contract forms: time-incentive contracts (penalizing lateness) and cost-sharing contracts (reimbursing direct costs). Methodology/results: Using a game-theoretic model, we characterize optimal RFQ designs under incomplete information about contractors' direct cost efficiency. Time-incentive contracts weaken competition, whereas cost-sharing contracts strengthen it, increasing work rates and shortening completion times. Clients prefer cost-sharing contracts, with this preference being stronger when time urgency is high, contractor heterogeneity is substantial, or the contractor pool is small; time-incentive contracts yield higher overall system efficiency in low-urgency settings, consistent with their prevalence in public projects. We derive a closed-form bound on the relative decrease in the client's payoff when using a fixed-term RFQ instead of a more complex upfront-fee RFQ and numerically show that fixed-term RFQs lose less than 0.28% of client payoff. Audit noise has no effect when correction costs are negligible or very high, but it introduces bidding frictions and lowers the optimal cost-sharing ratio when correction costs are moderate. Managerial implications: These insights guide contract selection by time urgency and market conditions and support the use of simple fixed-term RFQs with minimal profit loss.

Many real-world optimization problems involve uncertain parameters with probability distributions that can be estimated using contextual feature information. In contrast to the standard approach of first estimating the distribution of uncertain parameters and then optimizing the objective based on the estimation, we propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution of the random parameter while considering the structure of the optimization problem. We directly model the relationship between the conditional distribution of the random parameter and the contextual features and then estimate the probabilistic model with an objective that aligns with the downstream optimization problem. We show that our ICEO approach is asymptotically consistent under moderate regularity conditions and further provide finite performance guarantees in the form of generalization bounds. Computationally, performing estimation with the ICEO approach is a nonconvex and often nondifferentiable optimization problem. We propose a general methodology for approximating the potentially nondifferentiable mapping from estimated conditional distribution to the optimal decision by a differentiable function, which greatly improves the performance of gradient-based algorithms applied to the nonconvex problem. We also provide a polynomial optimization solution approach in the semi-algebraic case. Numerical experiments are also conducted to show the empirical success of our approach in different situations, including with limited data samples and model mismatches.

Moment-based distributionally robust optimization (DRO) provides an optimization framework to integrate statistical information with traditional optimization approaches. Under this framework, one assumes that the underlying joint distribution of random parameters runs in a distributional ambiguity set constructed by moment information and makes decisions against the worst-case distribution within the set. Although most moment-based DRO problems can be reformulated as semidefinite programming (SDP) problems that can be solved in polynomial time, solving high-dimensional SDPs is still time-consuming. Unlike existing approximation approaches that first reduce the dimensionality of random parameters and then solve the approximated SDPs, we propose an optimized dimensionality reduction (ODR) approach by integrating the dimensionality reduction of random parameters with the subsequent optimization problems. Such integration enables two outer and one inner approximations of the original problem, all of which are low-dimensional SDPs that can be solved efficiently, providing two lower bounds and one upper bound correspondingly. More importantly, these approximations can theoretically achieve the optimal value of the original high-dimensional SDPs. As these approximations are nonconvex SDPs, we develop modified alternating direction method of multipliers algorithms to solve them efficiently. We demonstrate the effectiveness of our proposed ODR approach and algorithm in solving multiproduct newsvendor and production-transportation problems. Numerical results show significant advantages of our approach regarding computational time and solution quality over the three best possible benchmark approaches. Our approach can obtain an optimal or near-optimal (mostly within 0.1%) solution and reduce the computational time by up to three orders of magnitude.

With the rapid growth of omnichannel retailing and the takeaway delivery economy, the classic point-to-point mode for on-demand delivery is deficient in delivery capacity, coverage area, dispatching efficiency, and courier safety assurance. Inspired by the success of Dabbawala, a historical Indian company for lunch delivery, we propose a novel public on-demand delivery service system that uses the public transit network to satisfy stochastic delivery demands. In particular, the proposed system includes a radial public transit network for intermediate transshipment, as well as couriers with e-bikes for terminal pick-up and drop-off. Our research aims to generate system design that minimizes the sum of penalty costs from lost sales and operational costs associated with courier terminal delivery distance. Solving the integrated system optimization problem relies on incorporating operational details, especially the allocation strategies of the lines’ capacity and the couriers’ terminal traveling modes. For the former, we propose a novel flexible design, called dual long-chain design, to improve flexibility. For the latter, we propose an elegant approximation of optimal service region partitioning that minimizes the expected terminal delivery distance and the resulting costs, without compromising delivery timeliness. Leveraging the theoretical results of the operational strategies, we simplify the integrated optimization problem and propose an efficient approximation algorithm. Finally, we validate the advantage of the proposed system over classic point-to-point delivery in satisfying demands and reducing costs through extensive numerical experiments, providing managerial insights in handling massive on-demand delivery demands and utilizing the idle capacity of the public transit system.

In this paper, we propose a Deep-DiD method that incorporates two deep neural networks in a difference-in-difference (DiD) framework to estimate heterogeneous treatment effects (HTEs). The dual-network architecture contains one neural network modeling HTEs as a nonparametric function of pretreatment features and another neural network capturing individual and time fixed effects. Through a series of simulations, we show that our method can uncover the true HTEs with high accuracy under various settings and demonstrates more robust estimation performance compared with existing methods like linear models and random forests. We apply this method to an empirical setting where a large video-sharing platform introduced a “Creator Signing Program” aimed at signing creators and motivating them to generate more high-quality video content. Leveraging a matched data set of signed and unsigned creators, we employ our Deep-DiD method to estimate the HTEs of the signing program. Our method can help the platform optimize creator selection by identifying creators with the highest-estimated treatment effects. Through out-of-sample tests, we show that creators selected by the Deep-DiD method experience substantially larger actual performance jumps than those selected by the platform. Creator selection based on the Deep-DiD method also consistently outperforms that based on linear models.

This work examines the behaviors of the online projected gradient ascent (OPGA) algorithm and its variant in a repeated oligopoly price competition under reference effects. In particular, we consider that multiple firms engage in a multiperiod price competition, where consecutive periods are linked by the reference price update and each firm has access only to its own first-order feedback. Consumers assess their willingness to pay by comparing the current price against the memory-based reference price, and their choices follow the multinomial logit (MNL) model. We use the notion of stationary Nash equilibrium (SNE), defined as the fixed point of the equilibrium pricing policy, to simultaneously capture the long-run equilibrium and stability. We first study the loss-neutral reference effects and show that if the firms employ the OPGA algorithm—adjusting the price using the first-order derivatives of their log-revenues—the price and reference price paths attain last-iterate convergence to the unique SNE, thereby guaranteeing the no-regret learning and market stability. Moreover, with appropriate step-sizes, we prove that this algorithm exhibits a convergence rate of ̃ 𝒪 ⁢(1/𝑡2) in terms of the squared distance and achieves a constant dynamic regret. Despite the simplicity of the algorithm, its convergence analysis is challenging due to the model lacking typical properties such as strong monotonicity and variational stability that are ordinarily used for the convergence analysis of online games. The inherent asymmetry nature of reference effects motivates the exploration beyond loss-neutrality. When loss-averse reference effects are introduced, we propose a variant of the original algorithm named the conservative-OPGA (C-OPGA) to handle the nonsmooth revenue functions and show that the price and reference price achieve last-iterate convergence to the set of SNEs with the rate of 𝒪⁢(1/√𝑡) . Finally, we demonstrate the practicality and robustness of OPGA and C-OPGA by theoretically showing that these algorithms can also adapt to firm-differentiated step-sizes and inexact gradients.

Problem definition: As digitization transforms the service sector and empowers service platforms, questions arise about utilizing and disseminating customer information captured by digitization to enhance platform operations. We contribute by investigating how providing customer-related information at the start of a service encounter impacts both service supply and demand in the context of entertainment service platforms. Methodology/results: We conducted a field experiment on a live-streaming platform that connects hundreds of millions of viewers with individual broadcasters. For broadcasters randomly assigned to the treatment condition (but not for broadcasters in the control condition), when a viewer entered their shows, information about the viewer appeared on the screen. Our analyses, involving a random sample of 49,998 broadcasters, demonstrate that relative to control broadcasters, treatment broadcasters expanded service supply by 12.62% by increasing both show frequency (3.31%) and show length (7.10%), thus earning 10.44% more income, based on our conservative estimate. Moreover, our intervention increased service demand (measured by viewer watch time) by 4.51%. Additional analyses and surveys in our field setting and online experiments (n = 3,115) shed light on the potential mechanisms. Viewer-related information enables broadcasters to offer personalized service and vividly perceive viewers. On the demand side, viewers appreciate personalized service and interact with broadcasters more, which collectively boost demand. On the supply side, broadcasters not only enjoy the increased interaction with viewers but also feel a stronger sense of appreciation due to the more vivid mental image of viewers, which collectively lifts service supply. Managerial implications: This research suggests that providing customer-related information at the beginning of a service encounter can increase both service demand and supply. This low-cost, information-based intervention has important implications for digital service platforms that have little control over service providers’ work schedules and service quality.

The rapid development of blockchain has inspired many traditional centralized intermediaries to transform their transaction models, especially for the peer-to-peer market. Lately, the token-based (blockchain) system (with cryptocurrency) is gaining popularity. However, little is known about the (comparative) performance of different operating types. In this study, we build an analytical framework to find the optimal strategies for the token-based and nontoken-based blockchain (as a special application scenario) platforms and derive the essential model properties and characteristics. We analytically show how the optimal mining bonus depends on the fraction of reserved tokens sold to customers and on the price-to-sales ratio. Furthermore, we obtain several actionable findings for choosing suitable platform types under different scenarios. The shift from the nontoken-based platform to the token-based platform may yield greater social welfare unless the nontoken-based system operates with a much larger ride price, which we show to be unrealistic for the considered Beijing case through numerical studies. Moreover, we find that the matching probability for the token-based platform is predominantly higher than that for the nontoken-based one. Besides, government interventions may encourage a path toward a fair consensus mechanism or a high decentralization level in order to enhance social welfare. One unanticipated finding is that a higher decentralization level may lead to a lower mining capacity shortage and so to a more efficient system, indicating that the combination of blockchain and the sharing economy has much potential.

Problem definition: How should retailers leverage aggregate (category) sales information for individual product demand prediction? Motivated by inventory risk pooling, we develop a new prediction framework that integrates category-product sales information to exploit the benefit of pooling. Methodology/results: We propose to combine data from different aggregation levels in a transfer learning framework. Our approach treats the top-level sales information as a regularization for fitting the bottom-level prediction model. We characterize the error performance of our model in linear cases and demonstrate the benefit of pooling. Moreover, our approach exploits a natural connection to regularized gradient boosting trees that enable a scalable implementation for large-scale applications. Based on an internal study with JD.com on more than 6,000 weekly observations between 2020 and 2021, we evaluate the out-of-sample forecasting performance of our approach against state-of-the-art benchmarks. The result shows that our approach delivers superior forecasting performance consistently with more than 9% improvement over the benchmark method of JD.com. We further validate its generalizability on a Walmart retail data set and through alternative pooling and prediction methods. Managerial implications: Using aggregate sales information directly may not help with product demand prediction. Our result highlights the value of transfer learning to demand prediction in retail with both theoretical and empirical support. Based on a conservative estimate of JD.com, the improved forecasts can reduce the operating cost by 0.01–0.29 renminbi (RMB) per sold unit on the retail platform, which implies significant cost savings for the low-margin e-retail business.

Problem definition: Inventory management problems with periodic and controllable resets occur in the context of managing water storage in the developing world and dynamically optimizing endcap promotion duration in retail outlets. In this paper, we consider a set of sequential decision problems in which the decision maker must not only balance holding and shortage costs but discard all inventory before a fixed number of decision epochs with the option for an early inventory reset. Methodology/results: Finding optimal policies for these problems through dynamic programming presents unique challenges because of the nonconvex nature of the resulting value functions. Moreover, this structure cannot be readily analyzed even with extended convexity definitions, such as K-convexity. Managerial implications: Our key contribution is to present sufficient conditions that ensure the optimal policy has an easily interpretable structure, which generalizes the well-known (𝑠,𝑆) policy from the operations management literature. Furthermore, we demonstrate that, under these rather mild conditions, the optimal policy exhibits a four-threshold structure. We then conclude with computational experiments, thereby illustrating the policy structures that can be extracted in various inventory management scenarios.