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Determining proper locations to establish emergency response facilities is a critical strategic element of pollution preparedness and response planning for oil spills in remote areas. Many location-allocation models are available in the literature, but Arctic contexts such as remoteness and environmental sensitivities are still inadequately investigated while building optimization models. A Mixed Integer Programming (MIP) based optimization model is developed to devise a location-allocation problem: maximizing weighted spill coverage considering spill size, environmental sensitivity, and response time. Strategic decisions - e.g. allocation of stockpiling resources to resource stations and which response stations to open - are incorporated into the model as decision variables. Input parameters of the model are estimated using numerical and geospatial data of potential oil spills and response stations. The model is illustrated for hypothetical oil spill scenarios in the Canadian Arctic. The model provides optimal allocation of resources and recommends best-suited locations to build response facilities. Data visualization tools including Network Diagrams and sensitivity analysis on different model configurations, show the adequacy of the proposed mathematical modelling approach to solve the given problem. Multiple facility locations have been compared to cover all possible oil spills along Arctic shipping routes, further revealing a few better locations considering realistic constraints. Decision makers can use such optimization modelling information – e.g., how many stations to build in the Arctic to adequately cover potential oil spills – to aid strategic decision-making of maritime shipping.

The Collaborative Multi-Vehicle Arc Routing for Connectivity Problem (MPC-ARCP) is an optimization problem that determines the roads that should be unblocked to reconnect communities that were cut off after a natural disaster. In the MPC-ARCP, isolated communities are linked to critical infrastructures (e.g. ports and airports) according to a prioritization criterion. This paper introduces E-ARCP - a software designed to solve the MPC-ARCP to optimality and to display the results in a network. This software is easily adaptable to other Arc Routing for Connectivity Problems with great reuse potential by researchers working in the field of network repair and restoration.

Safety management systems (SMSs) are widely applied across many industrial sectors, and a large body of literature has been published addressing their design, implementation, effectiveness, and associated challenges. This article presents a high-level analysis of the SMS research domain, guided by a set of questions addressing the contents, structure, and evolution the research domain, its dominant themes and focus topics, the key scientific domains and journals contributing to its development, and the key publications serving as an intellectual basis for SMS related research. The results show a rapidly increasing volume of research outputs and a shift from research based in North America and Europe to Asia and Australia. There is only a limited number of institutions enduringly contributing to the field, and there are relatively few stable research collaborations, with the number of Chinese institutions publishing SMS related research fast expanding in recent years. The domain is strongly interdisciplinary and embedded in applied domains of science, with industrial engineering the most contributing category, as well as categories focusing on the industrial application domains. A temporal evolution of the research activity in different application domains is apparent, with an initial focus on occupational health and safety, followed by process safety, patient safety, food safety, and construction safety. SMS research has a strong relation to safety culture and safety climate research, and while safety and risk management concepts and theories form an important knowledge base for most application domains, the dominant views on accident causation differ between these. Research on SMS in the food industry is relatively separated from the other application domains. Based on the findings, various future research directions are discussed.

Highly automated vehicles are making their way towards implementation in many modes of transportation, including shipping. From the safety perspective, it is critically important that such vehicles or the operators overseeing them maintain their sense of the environment, also referred to as situational awareness. The present study investigates the worldwide research effort focusing on situational awareness for autonomous transport and explores how the maritime domain could benefit from it. The results indicate that most of the research originates from the automotive sector, but the topic is developing fast in other transportation modes too. Some findings have been shared across the modes of transportation, but only to a limited extent. Although technology development is performed based on the achievements within basic research domains, there has been little feedback from applied sciences. Similarly, collaborative research is not strongly developed.

This article presents a database with geographical and demographic information characterizing the impacts to road and maritime networks, and coastal communities, of a plausible magnitude M9.0 megathrust Cascadia Subduction Zone earthquake scenario near Vancouver Island in British Columbia, Canada. The database consists of a medium and a high impact case associated with the earthquake scenario. The data include the geographical location of communities, ports, and airports/helipads/heliports, the structure of the roads network and their expected damage levels, the resilience level and population size of the communities on Vancouver Island, and the trajectories, expected delays and capacities of ferries and barges. The data originates from government and carriers’ open available reports and external datasets, and several impact models. The primary purpose of this database is to support disaster management researchers working to develop and test network models that focus on road repair and restoration, and on the multi-modal distribution of relief supplies to victims. In addition, the data can be used to test heuristic and metaheuristic approaches applied to network models in the context of natural disasters.

This article presents a database which contains a comprehensive and systematically varied set of network instances. These can be applied as benchmarks for multiple road repair and restoration problems in the context of natural disasters. The characteristics of the instances vary in terms of network size, intensity and type of disaster affecting the road network, the epicenter's location, and the number of sub-networks in which the initial network is divided after the disaster occurs. The instances were developed primarily for the Multi-vehicle Prize Collecting Arc Routing for Connectivity Problem (KPC-ARCP). These are however easily adaptable to other well-known connectivity, vehicle routing, and facility location problems in the Operations Research literature. The instances are available on a public repository, as is the Python code to generate the instances.

Blocked roads can jeopardize emergency response activities in the aftermath of disasters, such as earthquakes. In many cases, the distribution of relief supplies to affected communities, the evacuation of victims, and Search and Rescue activities cannot be resumed due to the lack of connectivity of the road network. The multi-vehicle prize collecting arc routing for connectivity problem (KPC-ARCP) aims to determine the routes of synchronized road clearing teams during the immediate response of a disaster, to reconnect the network so that the utility of the reconnection is maximized. This paper proposes an Ant Colony Optimization (ACO) algorithm to solve the KPC-ARCP and compares its performance to results of earlier studies which apply GRASP and Matheuristic methods to solve the problem. Performance comparisons consider the computation time and accuracy of the solution, and are implemented on academic and real cases in Istanbul. The runs on academic and real-world instances indicate that ACO has a reasonable performance compared to the existing methods. However, the high complexity of its parameter tuning suggests that GRASP is likely more suitable for KPC-ARCP.

Risks in the maritime domain have various sources, of which the transportation of oil and other noxious products is one of key concern to industry and public stakeholders. Operational or accidental releases of oil or other pollutants from ships or offshore facilities into the marine environment can have disastrous effects on the marine ecosystems, while also leading to very significant economical losses. Therefore, national states have implemented various mechanisms for preventing and responding to pollution in the maritime domain, with activities which are often embedded in regional cooperation frameworks clustered around certain sea areas. To support collaborative, harmonized, and risk-informed oil spill Pollution Preparedness and Response (PPR) planning for response authorities, the Baltic Marine Environment Protection Commission (HELCOM), together with its research partners, and with extensive end-user and stakeholder inputs, have developed the OpenRisk Toolbox. This toolbox includes several risk assessment tools and techniques, which can assist in providing answers to a range of PPR risk management questions in a range of organizational contexts. To better understand and ensure the applicability and usefulness of the OpenRisk Toolbox, a workshop was organized where some of these tools were tested. Selected end user and stakeholder views on the perceived usefulness of the tools were collected and analyzed. Another workshop focused on further development needs to implement the tools in organizational practices. This paper first presents the OpenRisk Toolbox, then describes the settings of the workshops. Finally, a summary of the end-user and stakeholder views on the tested tools, and on future development needs, is given.

In harsh environments, offshore oil and gas support operations are subjected to frequent logistics and supply chain operational disruption, due to harsh environmental factors with their associated risks. To capture these stochastic influential factors and support related decision making, it is helpful to develop a robust and dynamic probabilistic model.The current study presents a proactive methodology that integrates the Pure-Birth Markovian process (PBMP) with the Bayesian network (BN) for the effective analysis of offshore logistics disruption risk. The PBMP captures the stochasticity in the failure characteristics of the engineering systems for estimating the time-evolution degradation probability. The BN explores the dynamic interactions among the most important offshore logistics influential factors to analyze the disruption risk in a harsh environment. The effects of influential factors’ non-linear dependencies are propagated and updated, given evidence on the degree of disruption. The level of logistics disruption is further assessed using cost aggregation-based expectation theory. The theory explores the incurred cost/economic risk under different operational scenarios. The proposed methodology is tested on an offshore supply vessel operation to estimate the likely operational disruption risk in terms of financial loss in a harsh operating environment. The most critical influential functions are assessed to establish their degree of impact on the logistics disruption. At the upper bound probability of disruption occurrence, an economic risk/additional incurred cost of US$2.38E+05 with a variance (σ2) of 3.05×109 was predicted. The result obtained suggests that the proposed methodology is adaptive and effective for dynamic logistics disruption risk analysis in harsh offshore environments.

Even though validation is an important concept in safety research, there is comparatively little empirical research on validating specific safety assessment, assurance, and ensurance activities. Focusing on model-based safety analysis, scant work exists to define approaches to assess a model’s adequacy for its intended use. Rooted in a wider concern for evidence-based safety practices, this paper intends to provide an understanding of the extent of this problem of lack of validation to establish a baseline for future developments. The state of the practice in validation of model-based safety analysis in socio-technical systems is analyzed through an empirical study of relevant published articles in the Safety Science journal spanning a decade (2010–2019). A representative sample is first selected using the PRISMA protocol. Subsequently, various questions concerning validation are answered to gain empirical insights into the extent, trends, and patterns of validation in this literature on model-based safety analysis. The results indicate that no temporal trends are detected in the ratio of articles in which models are validated compared to the total number of papers published. Furthermore, validation has no clear correlation with the specific model type, safety-related concept, different system life cycle stages, industries, or with the countries from which articles originate. Furthermore, a wide variety of terminology for validation is observed in the studied articles. The results suggest that the safety science field concerned with developing and applying models in safety analyses would benefit from an increased focus on validation. Several directions for future work are discussed.

Risks in the maritime domain have various sources, of which the transportation of oil and other noxious substances is one of the key concerns to industry and public stakeholders (HELCOM, 2018b). With the increased presence of off-shore wind farms, off-shore production and processing facilities, new risks have also emerged over the past decades. The previous large-scale ship and off-shore accidents, such as Erika (1999), Prestige (2002) and Deepwater Horizon (2010) have demonstrated that e.g. oil spills can have disastrous impacts on the marine and coastal ecosystems (Venesjrvi R., 2016), and they can cause also significant economic losses (Yamada Y., 2009). In addition to the accidental spills, there are also operational spills, which are small, but frequent, intentional or inadvertent spillages in the sea due to ship operations (Neves A. et al., 2015).

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

Methods and technology have been developed to solve a wide range of problems in the dynamics of sea currents and to assess their “impact” on objects in the marine environment. Technology can be used for monitoring and forecasting sea currents, for solving the problems of minimizing risks and analyzing marine disasters associated with the choice of the optimal course of the ship, and assessing the pollution of coastal zones, etc. The technology includes a numerical model of marine circulation with improved resolution of coastal zones, a method for solving the inverse problem of contamination of the sea with a passive impurity, and a variational algorithm for constructing the optimal trajectory of the vessel. The methods and technology are illustrated by solving problems of Baltic Sea dynamics. The model of sea dynamics is governed by primitive equations that are solved on a grid with an improved resolution of the selected coastal zone—in this case, the Gulf of Finland. The equations of the model are formulated in a bipolar orthogonal coordinate system with an arbitrary arrangement of poles and the sigma coordinate in the vertical direction. An increase in the horizontal resolution of the allocated zone is achieved due to the displacement of the north pole in the vicinity of the city of St. Petersburg. A class of dangerous technogenic situations and natural phenomena (sea accidents, which can be investigated with the help of the proposed methodology), includes tanker accidents in the case of a possible collision with a stationary object (with “dynamic danger”) or a moving object (including another ship), accidents on oil-producing platforms and oil pipelines, and coastal pollution.