A mixed integer programming model for optimizing multi-level operations process in railroad yards

Tie Shi, Xuesong Zhou

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

A typical railroad hump yard contains multiple layers of complex operations. The railcars coming with inbound trains through the yard need to be humped into different classification tracks according to the destination, and then assembled to generate the desired outbound trains. During this complex procedure, the processing time of railcars and various resource constraints at different railroad yard facilities could significantly affect the overall performance of yard operations, individually and in combination. It is theoretically challenging to represent a large number of practical operation rules through tractable mathematical programming models. This paper first presents a time-expanded multi-layer network flow model to describe the connection between different layers of yard operations. A mixed integer programming model is developed to optimize the overall performance by jointly considering tightly interconnected facilities. We adopt a cumulative flow count representation to model the spatial capacity constraints in terms of the number of railcars in classification yards. A novel lot-sizing modeling framework and related valid inequality formulations are introduced to model the assembling jobs for outbound trains. We also develop an aggregated flow assignment model and earliest due date-based heuristic rules to determine the humping jobs sequence for reducing the search space. Numerical experiments are conducted to examine the solution quality and computational efficiency under different types of formulation strategies.

Original languageEnglish (US)
Pages (from-to)19-39
Number of pages21
JournalTransportation Research Part B: Methodological
Volume80
DOIs
StatePublished - Oct 1 2015

Fingerprint

Railroad yards and terminals
Railroads
Integer programming
railroad
programming
Classification yards
Network layers
Mathematical programming
Computational efficiency
performance
Railroad
Mixed integer programming
heuristics
efficiency
experiment
Train
Processing

Keywords

  • Cumulative flow count
  • Mixed integer programming
  • Operations plan
  • Railroad yard
  • Valid inequality

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Transportation

Cite this

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title = "A mixed integer programming model for optimizing multi-level operations process in railroad yards",
abstract = "A typical railroad hump yard contains multiple layers of complex operations. The railcars coming with inbound trains through the yard need to be humped into different classification tracks according to the destination, and then assembled to generate the desired outbound trains. During this complex procedure, the processing time of railcars and various resource constraints at different railroad yard facilities could significantly affect the overall performance of yard operations, individually and in combination. It is theoretically challenging to represent a large number of practical operation rules through tractable mathematical programming models. This paper first presents a time-expanded multi-layer network flow model to describe the connection between different layers of yard operations. A mixed integer programming model is developed to optimize the overall performance by jointly considering tightly interconnected facilities. We adopt a cumulative flow count representation to model the spatial capacity constraints in terms of the number of railcars in classification yards. A novel lot-sizing modeling framework and related valid inequality formulations are introduced to model the assembling jobs for outbound trains. We also develop an aggregated flow assignment model and earliest due date-based heuristic rules to determine the humping jobs sequence for reducing the search space. Numerical experiments are conducted to examine the solution quality and computational efficiency under different types of formulation strategies.",
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AB - A typical railroad hump yard contains multiple layers of complex operations. The railcars coming with inbound trains through the yard need to be humped into different classification tracks according to the destination, and then assembled to generate the desired outbound trains. During this complex procedure, the processing time of railcars and various resource constraints at different railroad yard facilities could significantly affect the overall performance of yard operations, individually and in combination. It is theoretically challenging to represent a large number of practical operation rules through tractable mathematical programming models. This paper first presents a time-expanded multi-layer network flow model to describe the connection between different layers of yard operations. A mixed integer programming model is developed to optimize the overall performance by jointly considering tightly interconnected facilities. We adopt a cumulative flow count representation to model the spatial capacity constraints in terms of the number of railcars in classification yards. A novel lot-sizing modeling framework and related valid inequality formulations are introduced to model the assembling jobs for outbound trains. We also develop an aggregated flow assignment model and earliest due date-based heuristic rules to determine the humping jobs sequence for reducing the search space. Numerical experiments are conducted to examine the solution quality and computational efficiency under different types of formulation strategies.

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