Skip to main content

Nested Partitions Method, Theory and Applications

  • Book
  • © 2009

Overview

  • Nested Partitions (NP) framework combines well-known optimization techniques with problem-specific local search heuristics for increased effectiveness
  • Emphasizes the solution of real-world problems in a broad range of areas, from telecommunications to manufacturing and military operations and design of nanoscale systems

Part of the book series: International Series in Operations Research & Management Science (ISOR, volume 109)

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (12 chapters)

  1. Introduction

  2. Methodology

  3. Applications

Keywords

About this book

Thesubjectofthisbookisthenested partitions method(NP),arelativelynew optimization method that has been found to be very e?ective solving discrete optimization problems. Such discrete problems are common in many practical applications and the NP method is thus useful in diverse application areas. It can be applied to both operational and planning problems and has been demonstrated to e?ectively solve complex problems in both manufacturing and service industries. To illustrate its broad applicability and e?ectiveness, in this book we will show how the NP method has been successful in solving complex problems in planning and scheduling, logistics and transportation, supply chain design, data mining, and health care. All of these diverse app- cationshaveonecharacteristicincommon:theyallleadtocomplexlarge-scale discreteoptimizationproblemsthatareintractableusingtraditionaloptimi- tion methods. 1.1 Large-Scale Optimization IndevelopingtheNPmethodwewillconsideroptimization problemsthatcan be stated mathematically in the following generic form: minf(x), (1.1) x?X where the solution space or feasible region X is either a discrete or bounded ? set of feasible solutions. We denote a solution to this problem x and the ? ? objective function value f = f (x ).

Authors and Affiliations

  • University of Wisconsin-Madison, USA

    Leyuan Shi

  • Iowa State University, USA

    Sigurdur Ólafsson

Bibliographic Information

Publish with us