Publications & Manuscripts

• Intertemporal Pricing via Nonparametric Estimation: Integrating Reference Effects and Consumer Heterogeneity
  with Junyu Cao, Zuo-Jun Max Shen
  Manufacturing & Service Operations Management (Articles in Advance) 2022
  abstract ssrn slides code real data journal
  🏆 Finalist, 2020 MSOM Data-Driven Research Challenge
  📺 Media: ORAI_China

  We consider intertemporal pricing in the presence of reference effects and consumer heterogeneity. Our research question encompasses how to estimate heterogeneous consumer reference effects from data and how to efficiently compute the optimal pricing policy. Understanding reference effects is essential for designing pricing policies in modern retailing. Our work contributes to this area by incorporating consumer heterogeneity under arbitrary distributions. We propose a mixed logit demand model that allows arbitrary joint distributions of valuations, responsiveness to prices, and responsiveness to reference prices among consumers. We use a nonparametric estimation method to learn consumer heterogeneity from transaction data. Further, we formulate the pricing optimization as an infinite horizon dynamic programming problem and solve it by applying a modified policy iteration algorithm. Moreover, we investigate the structure of optimal pricing policies and prove the sub-optimality of constant pricing policies even when all consumers are loss-averse according to the classical definition. Our numerical studies show that our estimation and optimization framework improves the expected revenue of retailers via accounting for heterogeneity. We validate our model using real data from JD.com, a large E-commerce retailer, and find empirical evidence of consumer heterogeneity. In practice, ignoring consumer heterogeneity may lead to a significant loss of revenue. Furthermore, heterogeneous reference effect offers a strong motive for promotions and price fluctuations.

• A Nonparametric Maximum Likelihood Approach to Mixture of Regression
  with Adityanand Guntuboyina
  Under revision for resubmission to Journal of the American Statistical Association
  abstract arxiv slides code
  🏆 Winner, 2020 IISA Best Student Paper (Theory/Methodology Category)

  Mixture of regression models are useful for regression analysis in heterogeneous populations where a single regression model may not be appropriate for the entire population. We study the nonparametric maximum likelihood estimator (NPMLE) for fitting these models. The NPMLE is based on convex optimization and does not require prior specification of the number of mixture components. We establish existence of the NPMLE and prove finite-sample parametric (up to logarithmic multiplicative factors) Hellinger error bounds for the predicted density functions. We also provide an effective procedure for computing the NPMLE without ad-hoc discretization and prove a theoretical convergence rate under certain assumptions. Numerical experiments on simulated data for both discrete and non-discrete mixing distributions demonstrate the remarkable performance of our approach. We also illustrate the approach on two real data sets.

• Multi-Product Dynamic Pricing with Reference Effects Under Logit Demand
  with Mengzi Amy Guo, Zuo-Jun Max Shen
  Submitted to Operations Research
  abstract ssrn
  

  We consider an infinite-horizon multi-product dynamic pricing problem with reference effects in a monopolistic setting, where the reference price is an exponentially weighted average of historical prices. In each period, the demand follows the multinomial logit (MNL) model, where the utility depends on both the current price and the reference price, and consumers can have product-differentiated sensitivities to the price and the reference price. We conduct thorough analyses of the myopic pricing policy, including its solution, long-run convergence behavior, and performance guarantee compared to the long-term discounted revenue of the optimal pricing policy. Furthermore, we establish the structural properties of the optimal pricing policy. When consumers are loss-neutral towards all products, we explicitly characterize the optimal pricing policy if it converges to a steady state, and based on our characterization we show that the steady state price can be computed efficiently by a binary search. Interestingly, we find that such a convergence behavior of the optimal pricing policy heavily relies on the upper bound of the admissible price range, and a low price upper bound facilitates the policy to converge. In contrast, when consumers are gain-seeking towards all products, we prove that the optimal pricing policy admits no steady state regardless of the price range. Nevertheless, if consumers are only gain-seeking towards certain but not all products, the optimal pricing policy can potentially be convergent. In addition, our numerical experiments show that loss-aversion over all products does not rule out price fluctuations. This finding is at odds with the classic belief on loss-averse consumers and hence, highlights the significance of accounting for cross-product effects through the MNL demand.

• Learning While Repositioning in On-Demand Vehicle Sharing Networks
  with Shunan Jiang, Zuo-Jun Max Shen
  Submitted to Management Science
  abstract ssrn
  

  We consider the vehicle repositioning problem for a vehicle sharing network with a fixed number of vehicles distributed across multiple locations. The vehicle sharing service is on-demand and one-way, which means that customers can pick up one vehicle from any location without prior reservation and drop off the vehicle to any location in network after the usage. Due to uncertainty in both customer arriving and vehicle returning, the service provider needs to periodically reposition the vehicles to match the supply with the demand while minimizing the total costs of repositioning labor and lost sales. The repositioning problem is critical in successful management of on-demand one-way vehicle sharing services, and it is challenging both analytically and computationally. The optimal repositioning policy under a general $n$-location network is inaccessible without knowing the optimal value function. Existing literature on computing repositioning policies assumes that the demand distribution is either known or can be simulated effectively from samples collected beforehand. In contrast, we develop an online learning method to dynamically reposition vehicles without knowing the demand distribution in advance. We study the performance of our algorithm by comparing it with the best base-stock policy. The best base-stock policy is a generalization of the popular inventory control policy to the vehicle repositioning problem. We prove that the $T$-period aggregate regret of our algorithm is bounded by $O(T^{\frac{n}{ n+1}}(\log T)^{\frac{1}{ n+1}})$. Numerical experiments on synthetic data demonstrate the effectiveness of our method.

• Supply Chain Forecast Sharing Under Asymmetric Forecast Preferences
  with Lin Zhao, Mengshi Lu, Zuo-Jun Max Shen, Kemal Guler
  Under revision at Production and Operations Management
  abstract ssrn
  

  Forecast sharing has been widely adopted to coordinate capacity planning in supply chains. However, the effectiveness of forecast sharing can be hindered by forecast inflation caused by the forecaster's asymmetric preferences toward underforecasts or overforecasts. In this paper, we study how suppliers can benefit from knowing the asymmetric preferences in forecasts shared by their customers. We employ a multi-period Bayesian repeated newsvendor model to depict the impact of forecast preferences on how a supplier updates its demand information and prepares its production capacity based on the shared forecast. We characterize the value of the forecast preference information, which is the increase in the supplier's expected profit through improved capacity planning enabled by considering asymmetric forecast preferences. We show that firms can benefit from the forecast preference information, and the benefit is more substantial under higher degrees of preference asymmetry, lower estimation error, and more profitable operations. We also show that it may not be optimal for the supplier to know the exact forecast preference. In the presence of estimation errors, the supplier may benefit from overestimating or underestimating the forecast preference. We further study the impact of the forecast preference information on the supplier's expected profit and derive a finite-sample bound on its regret. We apply the model in a numerical study with data from a real-world forecast sharing practice in a PC manufacturing supply chain. The results show that there exists significant forecast inflation in the shared forecasts. Furthermore, the preference toward overforecast is higher when the forecast horizon is shorter or when the product family's total demand is lower. Using forecast preferences reflected in the data set, we quantify the value of forecast preference information, which is shown to be significant and increasing in the manufacturer's estimation accuracy and the supplier's profitability.

• Quantum Computing Methods for Supply Chain Management
  with Zuo-Jun Max Shen, Junyu Liu
  Proceedings of 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)
  abstract arxiv published
  

  Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by the IBM Qiskit and the qBraid system.

Ph.D. Dissertation

• Operations and Data Analytics in Platforms: Theory and Applications
  Hansheng Jiang
  Department of Industrial Engineering and Operations Research, UC Berkeley, 2023.