Artikel

Parallel computation framework for optimizing trailer routes in bulk transportation

We consider a rich tanker trailer routing problem with stochastic transit times for chemicals and liquid bulk orders. A typical route of the tanker trailer comprises of sourcing a cleaned and prepped trailer from a pre-wash location, pickup and delivery of chemical orders, cleaning the tanker trailer at a post-wash location after order delivery and prepping for the next order. Unlike traditional vehicle routing problems, the chemical interaction properties of these orders must be accounted for to prevent risk of contamination which could impose complex product-sequencing constraints. For each chemical order, we maintain a list of restricted and approved prior orders, and a route is considered to be feasible if it complies with the prior order compatibility relationships. We present a parallel computation framework that envelops column generation technique for large-scale route evaluations to determine the optimal trailer-order-wash combinations while meeting the chemical compatibility constraints. We perform several experiments to demonstrate the efficacy of our proposed method. Experimental results show that the proposed parallel computation yields a significant improvement in the run time performance. Additionally, we perform sensitivity analysis to show the impact of wash capacity on order coverage.

Sprache
Englisch

Erschienen in
Journal: Journal of Industrial Engineering International ; ISSN: 2251-712X ; Volume: 15 ; Year: 2019 ; Issue: 3 ; Pages: 487-497 ; Heidelberg: Springer

Klassifikation
Management
Thema
Vehicle routing problem
Stochastic transit times
Compatibility constraints
Column generation
Parallel computation

Ereignis
Geistige Schöpfung
(wer)
Delli, Ugandhar
Sinha, Ashesh Kumar
Ereignis
Veröffentlichung
(wer)
Springer
(wo)
Heidelberg
(wann)
2019

DOI
doi:10.1007/s40092-019-0308-8
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

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Objekttyp

  • Artikel

Beteiligte

  • Delli, Ugandhar
  • Sinha, Ashesh Kumar
  • Springer

Entstanden

  • 2019

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