The dynamic bowser routing problem

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

Enhanced Operational Management of Airport Ground Support Equipment for Better Aircraft Turnaround Performance

Within the context of airport operations, this work focuses on enhancing the planning and real-time allocation of certain resources that are used to turn around an aircraft between two consecutive flights. This sort of operations takes place in the area of an airport that is called its apron. At peak times in particular, and when resource capacity is really tight, apron operations tend to be affected by either unavailability or late arrival of certain assets at the stand. The key element of this paper is the proposal of a new resource booking system, which operates in real-time, and deals with the related uncertainties. The booking mechanism aims to allow the airlines to book in advance certain resources, in particular ground support equipment. Our choice of a real case study will help us to assess the likely benefits, pros and cons of this system

On static vs dynamic (switching of) operational policies in aircraft turnaround team allocation and management

Aircraft turnaround operations represent the fulcrum of airport operations. They include all services to be provided to an aircraft between two consecutive flights. These services are executed by human operators, often organised in teams, who employ some related equipment and vehicles (e.g. conveyor belts, trolleys and tugs for baggage loading/unloading and transportation). In this paper, we focus on the real-time management of turnaround operations, and assess the relative merits and limitations of so-called dispatching rules that originate from the manufacturing literature. More precisely, we focus on the real-time allocation, on the day of operation, of teams of ground handling operators to aircraft turnarounds. This is pursued from the viewpoint of third-party service providers. We employ simulation, in conjunction with deep reinforcement learning, and work on the case of a real airport and the entirety of its turnaround operations involving multiple service providers