The dynamic bowser routing problem

Roberto Rossi, Maurizio Tomasella, Belen Martin-Barragan, Tim Embley, Christopher Walsh, Matthew Langston

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We investigate opportunities offered by telematics and analytics to enable better informed, and more integrated, collaborative management decisions across construction sites. We focus on efficient refuelling of assets across construction sites. More specically, we develop decision support models that, by leveraging data supplied by different assets, schedule refuelling operations by minimising the distance travelled by the refuelling truck | the so-called \bowser" | as well as fuel shortages. Motivatedby a practical case study elicited in the context of a project we recently conductedat Crossrail, we introduce the Dynamic Bowser Routing Problem. In this problem the decision maker aims to dynamically refuel, by dispatching a bowser truck, a set of assets which consume fuel and whose location changes over time; the goal is to ensure that assets do not run out of fuel and that the bowser covers the minimum possible distance. We investigate deterministic and stochastic variants of this problem and introduce effective and scalable mathematical programming models to tackle these cases. We demonstrate the effectiveness of our approaches in the context of an extensive computational study designed around data collected on site.
Original languageEnglish
Pages (from-to)108-126
JournalEuropean Journal of Operational Research
Issue number1
Early online date14 Nov 2018
Publication statusPublished - 16 May 2019

Keywords / Materials (for Non-textual outputs)

  • routing
  • dynamic bowser routing problem
  • stochastic bowser routing problem
  • mixed-integer linear programming
  • construction


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