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4397. Formulation of Strategy Towards the ARCTIC in terms of Search and Rescue – Needs, Innovations, and AI/OR based solutions
Invited abstract in session MD-2: EPOCG, stream EURO Prize for OR for the Common Good.
Monday, 14:30-16:00Room: Glassalen (building: 101)
Authors (first author is the speaker)
| 1. | Ashraf Labib
|
| Strategy and Business Systems, University of Portsmouth | |
| 2. | Dylan Jones
|
| Mathematics, University of Portsmouth |
Abstract
This seminar presents a usage of operational research methodologies during the ARCSAR (arcsar.eu) and AI-ARC EU-H2020 projects for enhancing safety and avoiding environmental damage in the Arctic and North Atlantic (ANA) maritime domain. The ARCSAR and AI-ARC (ai-arc.eu) projects arose due to concerns regarding the effects of increased maritime activity in the multi-national ANA region, caused by the melting ice due to climate change. The authors principal focus in the ARCSAR project has been working with search and rescue (SAR) and marine environmental response (MER) practitioners and other ANA stakeholders to classify and prioritise innovation needs. The overall aim is to lower the likelihood and social, environmental and economic consequences of large-scale incidents in ANA waters such as (i) a cruise ship sinking, (ii) a significant oil spill or (iii) a nuclear leakage. The AI-ARC project focused on developing and demonstrating an AI-based virtual control room that can improve decisions related to situational awareness, and assessment of risks against maritime incidents such as grounding, collision, oil spill, etc.
The seminar will first describe the holistic operational research process undertaken. This first involved a hierarchical classification of current and future ANA innovation needs, based at the highest level around the international maritime organisation (IMO)’s polar code. To achieve this, a series of workshops, questionnaires and interviews and literature review were used. These drew responses from 131 ANA stakeholders, including SAR and MER practitioners, environmentalists, NGO and charity representatives, indigenous peoples group representatives, industrialists and academics. This resulted in a hierarchical network of 20 need and 75 sub-need categories. A quantitative methodology using a combined PICK chart and Multi-Criteria Decision Making was then used to develop a priority set of 17 sub-needs. The PICK chart classified the needs according to importance and difficulty whilst a novel balanced knapsack goal programming model was developed to choose a high importance set of sub-needs, whilst considering the balance between long-term (more challenging) and shorter term (more implementable) innovation needs and across the six polar need categories.
The process of disseminating the methodology to ANA stakeholders and its usage to inform ARCSAR activities will be described. This involved a series of stakeholder workshops, where SWOT analyses around a set of the priority list innovations were undertaken. The priority needs were also used to inform significant activities, including a LiveEX ship evacuation exercise in Svalbard Norway. With respect to AI-ARC, the development of the risk index using AI based FL rule-based system and MCDM techniques to analyse data related to real-time AIS (ship conditions and position) combined with weather and human judgements will also be presented.
Finally, some highlights of the policy and practice impacts of the development of innovations to tackle the identified needs will be detailed. These include:
The development of a set of training materials and procedures to use by ANA communities in the case of a nuclear incident
An Arctic lessons learned arena for SAR and MER practitioners to share good practice
Development of an AI-based virtual control room for safer ANA maritime navigation
Enhanced training exercises and protocols for handling oil-on-ice in the case of an ANA oil spill
Evidence given to, and cited by, a UK parliamentary enquiry on future Arctic policy
Enhanced SAR operations in the Antarctic maritime region due to participation and knowledge transfer by Maritime New Zealand
Overall conclusions will then be drawn, including transferability of the developed methodology to other complex, multi-disciplinary situations where innovations to enhance safety and ensure wellbeing are needed.
Keywords
- Maritime applications
- Artificial Intelligence
Status: accepted
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