Optimal routing for automated emergency vehicle response for incident intervention in a traffic network

Congestion constitutes a major problem in modern urban traffic networks if not well managed. Its monstrous effects, on occasions, can paralyze a traffic network eating deep into the productive hours of commuters as well as vehicles and persons on essential services. Particularly affected are incidence-intervention vehicles such as emergency vehicles and fire-fighting vehicles. Whatever the cause of the congestion, its effect is counter-productive and an indication of an inefficient traffic network. This work, as presented in this paper, is concerned about the issue of traffic route management for emergency service (emergency vehicle) for which a delay of few minutes may cause tremendous loss of lives and properties. The route management scheme built for this purpose integrates information obtained from the use of Radio Frequency Signals for Traffic Light Preemption at Intersections in a Proteus Simulator environment and the use Arc GIS as a mode of routing the emergency vehicle from base to the incidence location, then to Health Facilities and from thence back to the emergency vehicle base in an optimal routing time. Traffic information are loaded into the Arc GIS environment which predicts the required tri-legged optimal routing and its duration using Dijkstra’s algorithm. Different scenarios of emergency vehicle, incidence and health facility locations were exploited using the scheme and compared with situations without their implementation. The proposed scheme outperforms the trial and error routing of emergency vehicles and can be embedded into traffic advisory system or as stand-alone emergency vehicle management system.Keywords: GIS, Dijkstra’s algorithm, Facility Location, Emergency-Vehicle, Optimal Routin

An adaptive large neighborhood search heuristic for periodic supply vessel planning problem

Confidential until 23. May 202

Análise da alocação de embarcações de apoio offshore com o uso de heurísticas e simulação de eventos discretos

Offshore oil and gas exploration and production activities have imposed major challenges to planning and operation of resources and logistics infrastructure. Proper dimensioning and use of supply vessels are essential tasks to hold an efficient and costeffective operation, since the amount of cargo handled is substantial. In this work, four different approaches for vessels allocation to maritime units and cargos are presented: one known as FIFO rule, two allocation greedy heuristics and one exact optimization method. To be possible compare the approaches the development of a discrete-event system simulator, which represents the operating conditions of Brazilian offshore scenario, was necessary. Associate with the simulator, the selection of a group of indicators that allowed an operation overview. The results evidence the useful of the approaches based on heuristics, because they present values close to the optimization method, but with shorter execution time.As atividades de exploração e produção de óleo e gás (E&P) offshore têm imposto grandes desafios à área de planejamento e operação de recursos e infraestrutura logísticos. A utilização adequada das embarcações supridoras é fundamental para a manutenção de uma operação eficiente e com custo aceitável, dado que a quantidade de cargas movimentada é grande. Neste trabalho são apresentadas e comparadas quatro estratégias diferentes de alocação de barcos às cargas e às unidades marítimas: a primeira conhecida como regra FIFO, duas heurísticas gulosas de alocação e um método exato de otimização. Para a realização dessa comparação foi desenvolvido um simulador de eventos discretos que visa replicar as condições operacionais presentes no cenário offshore brasileiro e selecionado um grupo de indicadores que forneçam um panorama geral dos principais pontos da operação. Ao final são apresentados resultados que demonstram a validade da utilização de estratégias baseadas em heurísticas, já que apresentam valores próximos dos obtidos com o método exato de otimização, porém, em um menor tempo computacional e sem a necessidade da utilização de programas externos