The Dynamic Bowser Routing Problem

Abstract

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.

A unified framework for rich routing problems with stochastic demands

“…Approximate dynamic programming (Powell, 2011) helps alleviate some of the issues in the latter case. In their recent work, Rossi et al (2017) also note the instance size limitations of dynamic programming in solving the bowser routing problem, a special version of the IRP, and propose heuristic approximations. Thus, while scenario-based approaches allow significant freedom in modeling undesirable events and recourse actions, they are computationally heavy for an already hard combinatorial problem like ours.…”

Modelos logísticos estocásticos aplicados a la cadena de suministro: una revisión de la literatura

“…Brinkmann et al [39] plantean un modelo de distribución de bicicletas con un nivel máximo de inventario (portabicicletas) y con M vehículos, se debe garantizar un número de portabicicletas vacío para que puedan ser parqueadas. Yadollahi et al [53] plantean métodos de solución para modelos estocásticos con flota de capacidad limitada e inventario máximo, y Rossi et al [52] un modelo capacitado para centros de combustible y vehículos para ruteo. Coelho et al [32] describen un modelo con un número M de vehículos homogéneos que realizan una ruta por periodo de tiempo.…”

Controlling inventory on electric roads

“…One example of such literature is the Dependent VRP [18], where travelling times depends not only on distance but on time of the day. Another recent example is the Dynamic Bowser Routing Problem [22], where a bowser delivers fuel to construction machinery (assets), which can change location on the graph with each review of the system. Having discussed and motivated our approach by establishing the differences with related literature, we next proceed and describe the problem setup.…”

“…. , t with an starting inventory of the starting inventory s i , and all deliveries up to period t, plus the losses until period t − 1 (see Lemma 5 from [22]), but also accounting for a reduction of the starting inventory of the expected exceeding capacity deliveries of periods 1, . .…”

“…17 are used to calculate the expected inventory that would exceed the capacity of each retailer when a delivery is made. It follows from the fact that in a lost sales setting shortages are reset at the end of each period and Q i t and s i are constants (Lemma 6 from [22]).…”