Open Problems in Optimization and Data Analysis

ISBN-13: 9783319991412 / Angielski / Twarda / 2018 / 330 str.

Panos M. Pardalos; Athanasios Migdalas

Spis treści

Chapter 1- Social Influence-based Optimization Problems.- Chapter 2- New Statistical Robust Estimators, Open Problems.- Chapter 3- Optimal Location Problems for Electric Vehicles Charging Stations: Models and Challenges.- Chapter 4- Supply and Demand Selection Problems in Supply Chain Planning.- Chapter 5- Open problems in green supply chain modelingband optimization with carbon emission targets.- Chapter 6- Variants and Formulations of the Vehicle Routing Problem.- Chapter 7- New MIP model for Multiprocessor Scheduling Problem with Communication Delays.- Chapter 8- On Optimization Problems in Urban Transport.- Chapter 9- Some aspects of the Stackelberg Leader/Follower Model.- Chapter 10- Open research areas in distance geometry.- Chapter 11- A Circle Packing Problem and its Connection to Malfatti’s Problem.- Chapter 12- Review of basic local searches for solving the Minimum sum-of-squares clustering problem.- Chapter 13- On the Design of Metaheuristics-based Algorithm Portfolios.- Chapter 14- Integral Simplex Methods for the Set Partitioning Problem: Globalization and Anti-Cycling.- Chapter 15- Open problems on Benders Decomposition Algorithm.- Chapter 16- An Example of Nondecomposition in Data Fitting by Piecewise Monotonic Divided  Differences of Order Higher than Two.

Blurb

Computational and theoretical open problems in optimization, computational geometry, data science, logistics, statistics, supply chain modeling, and data analysis are examined in this book.  Each contribution provides the fundamentals  needed to fully comprehend the impact of individual problems. Current theoretical, algorithmic, and practical methods used to circumvent each problem are provided to stimulate a new effort towards innovative and efficient solutions. Aimed towards graduate students and researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, this book provides a broad comprehensive approach to understanding the significance of specific challenging or open problems within each discipline.

The contributions contained in this book are based on lectures focused on “Challenges and Open Problems in Optimization and Data Science” presented at the Deucalion Summer Institute for Advanced Studies in Optimization, Mathematics, and Data Science in August 2016.