Risk analysis in harbor environments using simulation

Bruzzone, Agostino G.; Mosca, Roberto; Revetria, Roberto; Rapallo, S

doi:10.1016/s0925-7535(00)00023-0

Cited by 23 publications

(9 citation statements)

References 4 publications

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“…In order to succeed in such critical tasks, it is essential to have proper support tools, which could be used to evaluate alternative solutions and foresee how decisions affect the scenario in different conditions (Bruzzone et al, 2018). In previous works, the authors developed multiple models devoted to emergency management, as well as crisis prevention and decision support (Bruzzone et al, 2000;Bruzzone et al, 2014;Bruzzone et al, 2019). Such models, based on Intelligent Agents (IA) are capable to reproduce human behavior and social interactions; this approach allows to extend model capabilities in predicting scenarios outcomes.…”

Section: Simulation For Emergency Managementmentioning

confidence: 99%

Extended Reality, Intelligent Agents and Simulation to improve Efficiency, Safety and Security in Harbors and Port Plants

Bruzzone¹,

Massei²,

Sinelshchikov³

et al. 2019

Proceedings of the 21st International Conference on Harbor, Maritime and Multimodal Logistic Modeling &Amp; Simulation (HMS 201

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Section: Simulation For Emergency Managementmentioning

confidence: 99%

Extended Reality, Intelligent Agents and Simulation to improve Efficiency, Safety and Security in Harbors and Port Plants

Bruzzone¹,

Massei²,

Sinelshchikov³

et al. 2019

Proceedings of the 21st International Conference on Harbor, Maritime and Multimodal Logistic Modeling &Amp; Simulation (HMS 201

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“…Some simulation studies are aimed specifically to understanding the disruptive effect of accidents and natural disasters. For example, Bruzzone et al (2000) presented a simulation model of harbour and maritime environments with the aim of designing harbour and maritime infrastructures in order to determine the resources, structures and services needed to face possible emergencies. A case study of an oil spill was used to determine the amount of oil that could reach the coast of Genoa.…”

Section: Simulationmentioning

confidence: 99%

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Flores¹,

Banerjee²,

Mathews³

2019

Preprint

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When stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operatewithin the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable.On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tacticaland operational planning.In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions.More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure.Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand thata facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge forinvestors and operators of rail inter-modal terminals.We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand.

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“…This safety policy addresses general questions [82], but also aspects of cost-benefit analysis [83] and degree of acceptance of risk of the stakeholders [84]. As regards contamination risks, route models are proposed for transporting dangerous freight [85,86], risk assessment [87], the risks of environmental impact in ports [88] and coastal discharges [89].…”

Section: Research Into Safety At Sea Related To the Fishing Sectormentioning

confidence: 99%

Fishing safety policy and research

Pérez-Labajos

2008

Marine Policy

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Risk analysis in harbor environments using simulation

Cited by 23 publications

References 4 publications

Extended Reality, Intelligent Agents and Simulation to improve Efficiency, Safety and Security in Harbors and Port Plants

Extended Reality, Intelligent Agents and Simulation to improve Efficiency, Safety and Security in Harbors and Port Plants

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Fishing safety policy and research

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