Overall energy efficiency is crucial for green production and carbon dioxide emission, especially for energy-intensive industries such as steel making. To deal with unrelated parallel machine scheduling for steelmaking-continuous casting that is the bottleneck and energy-intensive process, this...

We consider a periodic-review single-product multi-echelon inventory problem with instantaneous replenishment. In each period, the decision-maker makes ordering decisions for all echelons. Any unsatisfied demand is backordered, and any excess inventory is carried to the next period. In contrast...

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...