We study a data-driven single-period inventory management problem with uncertain demand, wherein a large amount of textual word-of-mouth and historical data are accessible. Unlike two-step frameworks (i.e., predict-then-optimization), we propose an end-to-end (E2E) framework that uses a deep...

Recent technological developments related to the extraction and processing of data have given rise to widespread concerns about a reduction of privacy in the workplace. For a growing number of low-income and subordinated racial minority work forces in the United States, however, on-the-job data...

The linear ordering problem (LOP), which consists in ordering M objects from their pairwise comparisons, is commonly applied in many areas of research. While efforts have been made to devise efficient LOP algorithms, little was done to verify if the obtained solutions are valid and thus if they...

We consider the situation when the number of claims is unavailable, and a Tweedie’s compound Poisson model is fitted to the observed pure premium. Currently, there are two different models based on the Tweedie distribution: a single generalized linear model (GLM) for mean and a double...

We build forecasting models to predict second-hand car prices capable of predicting multiple car makes and models. Using a total of 141 car attributes we fit 15 machine learning models and rank their ability to accurately predict used automobile prices according to two performance criteria. In...

Actors in various settings have been increasingly relying on algorithmic tools to support their decision-making. Much of the public debate concerning algorithms - especially the associated regulation of new technologies - rests on the assumption that humans can assess the quality of algorithms....

The uncapacitated facility location problem (UFLP) is a real-world binary optimization problem that aims to find the number of opened facilities that minimizes the total cost of exchange between customer and facilities and opening cost of these facilities. UFLP is in class of NP-Hard and, there...

Machine learning algorithms are increasingly able to predict what goods and services particular people will buy, and at what price. It is possible to imagine a situation in which relatively uniform, or coarsely set, prices and product characteristics are replaced by far more in the way of...

The judgments of human beings can be biased; they can also be noisy. Across a wide range of settings, use of algorithms is likely to improve accuracy, because algorithms will reduce both bias and noise. Indeed, algorithms can help identify the role of human biases; they might even identify biases...

Single-period dynamic assortment planning involves the retailer's decision of selecting a set of products to offer and determining their initial inventory levels, considering stochastic demand and dynamic substitution. The objective is to maximize the expected revenue, subject to a capacity...