From single commodity to multiattribute models for locomotive optimization: A comparison of optimal integer programming and approximate dynamic programming

Belgacem Bouzaiene-Ayari, Clark Cheng, Sourav Das, Ricardo Fiorillo, Warren Buckler Powell

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

We present a general optimization framework for locomotive models that captures different levels of detail, ranging from single and multicommodity flow models that can be solved using commercial integer programming solvers, to a much more detailed multiattribute model that we solve using approximate dynamic programming (ADP). Both models have been successfully implemented at Norfolk Southern for different planning applications. We use these models, presented using a common notational framework, to demonstrate the scope of different modeling and algorithmic strategies, all of which add value to the locomotive planning problem. We demonstrate how ADP can be used for both deterministic and stochastic models that capture locomotives and trains at a very high level of detail.

Original languageEnglish (US)
Pages (from-to)366-389
Number of pages24
JournalTransportation Science
Volume50
Issue number2
DOIs
StatePublished - May 2016

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Transportation

Keywords

  • Approximate dynamic programming
  • Locomotive planning

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