Firms increasingly deploy algorithmic pricing approaches to determine what to charge for
their goods and services. Algorithmic pricing can discriminate prices both dynamically over …

In the last few years, reinforcement learning (RL), also called adaptive (or approximate)
dynamic programming, has emerged as a powerful tool for solving complex sequential …

Abstract This paper considers Safe Policy Improvement (SPI) in Batch Reinforcement
Learning (Batch RL): from a fixed dataset and without direct access to the true environment …

In this paper we develop a theoretical analysis of the performance of sampling-based fitted
value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decision …

This book is written for students and researchers in the field of industrial engineering,
computer science, operations research, management science, electrical engineering, and …

The manufacturing world is subject to ever-increasing cost optimization pressures.
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …

Dynamic pricing is considered a possibility to gain an advantage over competitors in modern
online markets. The past advancements in Reinforcement Learning (RL) provided more …

The model of a stochastic production/inventory system that is subject to deterioration failures
is developed and examined in this paper. Customer interarrival times are assumed to be …

Stock trading strategies pose challenging applications of machine learning for significant
commercial yields in the finance industry, drawing the attention of both economists and …

Dieses deutschsprachige Lehrbuch zum Thema Revenue Management richtet sich
gleichermaßen an Studenten der Betriebswirtschaftslehre mit quantitativer Ausrichtung …