This paper investigates the operations of a novel service mode, mobile charging, whereby a for-profit platform operator arranges vehicles equipped with charging piles to deliver charging to customers' electric vehicles in low-battery levels. We model the platform-customer interaction with a...

We consider the problem of online pricing with offline data, where the decision maker has in hand pre-existed offline data and then takes online sequential actions to maximize expected cumulative revenue as well as learning the optimal pricing. We focus on a simple and specific problem setting...

Electric vehicles (EVs) are superior to internal combustion engine-powered vehicles (ICVs) in limiting oil dependency and reducing carbon emissions. Governments worldwide have enacted various subsidy policies to promote EV adoption. In this paper, we focus on answering two related key...

Battery swapping enables drivers of electric vehicles to exchange their drained batteries for recharged batteries at swapping stations. The business model for a swapping station involves several important decisions. For example, how many charging devices (fast devices vs. normal devices) and...

When product recalls happen, firms not only have to incur additional logistics costs but also suffer from a damaged reputation. Besides, they may also supply disruption risk, under which a supplier could fail to deliver the quantity ordered at all. In this paper, we discuss how to mitigate...

We develop a framework for continuous search for information on a choice set of multiple alternatives, and apply it to consumer search in a product market. When a consumer considers purchasing a product in a product category, the consumer can gather information sequentially on several products....

Starting with historical demand trajectories of similar products and their covariate information, we develop a data-driven approach to dynamic inventory planning for new products. Specifically, we employ non-parametric machine learning methods to model the uncertain future demand and incorporate...

Problem Definition: We design an automatic decision-making system for an e-commerce platform to help manage the inventory of millions of stock keep units sold on the platform. The platform has accumulated a significant amount of contextual information, which may be useful for predicting both the...

When supply disruptions occur, firms want to employ an effective pricing strategy to reduce losses. However, firms typically don't know precisely how customers will react to price changes in the short term, during a disruption. In this paper, we investigate three different pricing strategies,...

In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve...