We investigate opportunities offered by telematics and analytics to enable
better informed, and more integrated, collaborative management decisions on
construction sites. We focus on efficient refuelling of assets across
construction sites. More specifically, we develop decision support models that,
by leveraging data supplied by different assets, schedule refuelling operations
by minimising the distance travelled by the bowser truck as well as fuel
shortages. Motivated by a practical case study elicited in the context of a
project we recently conducted at 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 as well as supplied
by our project partners.
Keywords: Routing; Dynamic Bowser Routing Problem; Stochastic Bowser Routing
Problem; Mixed-Integer Linear Programming; Construction
