Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling (Studies in Systems, Decision and Control (108))

Ratajczak-Ropel, Ewa; Skakovski, Aleksander

ISBN 10: 3319874225 / ISBN 13: 9783319874227

Published by Springer, 2018

About this Item

Print on Demand pp. 256.

Seller Inventory # 380218262

Bibliographic Details

• Title: Population-Based Approaches to the Resource-Constrained and Discrete-Continuous Scheduling (Studies in Systems, Decision and Control (108))
• Publisher: Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 3319874225
• ISBN 13: 9783319874227
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Includes recent research in population-based approaches

Presents the application of population-based approaches to resource constrained and discrete continuous scheduling

Written by experts in the field

From the Back Cover

This book addresses two of the most difficult and computationally intractable classes of problems: discrete resource constrained scheduling, and discrete-continuous scheduling. The first part of the book discusses problems belonging to the first class, while the second part deals with problems belonging to the second class. Both parts together offer valuable insights into the possibility of implementing modern techniques and tools with a view to obtaining high-quality solutions to practical and, at the same time, computationally difficult problems. It offers a valuable source of information for practitioners dealing with the real-world scheduling problems in industry, management and administration.

The authors have been working on the respective problems for the last decade, gaining scientific recognition through publications and active participation in the international scientific conferences, and their results are obtained using population-based methods. Dr E. Ratajczk-Ropel explores multiple agent and A-Team concepts, while Dr A. Skakovski focuses on evolutionary algorithms with a particular focus on the population learning paradigm.