Preparing the Arctic: Optimally Locating Aeronautical Search and Rescue Stations along Canada’s Northwest Passage

Although historically ice-covered, the Northwest Passage (NWP)—a maritime corridor located in the Canadian Arctic—has been experiencing melting trends in recent decades. Declining sea ice concentrations would lead to improved navigability along the NWP, suggesting promising opportunities for both domestic and international shippers. With vessel traffic expected to rise, and the lack of emergency response resources currently stationed in the region, Canada would be responsible for equipping its North with a search and rescue (SAR) network that is capable of providing relief to the users of its waterways. Since the Royal Canadian Air Force (RCAF) oversees the majority of SAR activities in Canada, the distribution of its response aircraft throughout the Arctic is crucial in the design of a successful response network. To address these concerns, we formulated the location problem as an integer linear program (ILP) that looked to determine optimal sites for aeronautical SAR stations and the allocation of aircraft so that the weighted primary and secondary coverage of demand points was maximized. To do so, we modelled the response capacities of the RCAF's fleet by designing a set of response functions based on each asset's performance specifications. We analyzed 29 arrangements across two cases: one in which the secondary coverage of demand points was optional (Case A), and another in which it was mandatory (Case B). Using six to seven aircraft, our approach led to three arrangements that would best address SAR concerns in the North: Arrangement 7A which was proposed for Case A, Arrangement 6B for Case B, and Arrangement 7B as a compromise of the two

Analysis of the Effects of Spatiotemporal Demand Data Aggregation Methods on Distance and Volume Errors

Purpose — Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors. Design/methodology/approach — This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering. Findings — As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands. Originality/value — This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data

Emergency response logistics in the High Arctic - Rescue helicopter coverage in the Norwegian High Arctic in the event of an expedition cruise ship mass- evacuation

Master i beredskap og kriseledelse - Nord universitet 201

A Study of Optimal Search and Rescue Operations Planning Problems

Search and Rescue (SAR) systems are vital to provide the quick response for saving lives in the first moments of natural and man-made calamities. In this dissertation, we present and discuss factors related to SAR operations planning and develop three SAR mathematical problems. In the first part we present an overview of SAR operations, highlighting questions affecting aerial search and rescue operations since it is the main object of our Thesis. In the second part, we consider an aerial fleet planning as a resource allocation problem and propose variations in the objective function of a binary integer programming (BIP) model according to different priorities related to area, time and type of the searching operation in high seas. We then study the problem for planning rescue missions in oceanic areas, modeled as a vehicle routing problem considering a heterogeneous fleet of vehicles and respective displacements during the operation. A BIP model is proposed and routing choices are assisted by probabilistic demands at each location that, when visited, may update previous decisions. In the fifth part, we consider the problem for planning a long-range mass rescue operation, modeled as an aircraft routing problem with pick-up and delivering, weight and endurance limits. A BIP model is proposed to minimize the flying time and feasible routes depend on factors such as aircraft endurance, fuel consumption rate, payload, take-off and landing weights, local demand and airfield capacities to operate different types of aircraft. The dissertation ends with conclusions and identified issues for future research

Seabasing and joint expeditionary logistics

Student Integrated ProjectIncludes supplementary material. Executive Summary and Presentation.Recent conflicts such as Operation Desert Shield/Storm and Operation Iraqi Freedom highlight the logistics difficulties the United States faces by relying on foreign access and infrastructure and large supply stockpiles ashore to support expeditionary operations. The Navy's transformational vision for the future, Sea Power 21, involves Seabasing as a way to address these difficulties by projecting and sustaining joint forces globally from the sea. This study analyzes logistics flow to, within and from a Sea Base to an objective, and the architectures and systems needed to rapidly deploy and sustain a brigade-size force. Utilizing the Joint Capabilities Integration and Development System (JCIDS), this study incorporates a systems engineering framework to examine current systems, programs of record and proposed systems out to the year 2025. Several capability gaps that hamper a brigade-size force from seizing the initiative anywhere in the world within a 10-day period point to a need for dedicated lift assets, such as high-speed surface ships or lighter-than-air ships, to facilitate the rapid formation of the Sea Base. Additionally, the study identifies the need for large-payload/high-speed or load-once/direct-to- objective connector capabilities to minimize the number of at-sea transfers required to employ such a force from the Sea Base in 10 hrs. With these gaps addressed, the Joint Expeditionary Brigade is supportable from the Sea Base.http://archive.org/details/seabasingndjoint109456918N

Softwarization of Large-Scale IoT-based Disasters Management Systems

The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems. A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay. To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude

Search and Rescue Operations Forecasting and Optimization

United States Coast Guard District 14 is responsible for the execution of eleven statutory missions across the Pacific region. Despite having the largest geographic area of responsibility (AOR), District 14 has command of among the fewest resources to accomplish these missions. When search and rescue (SAR) emergencies occur, these events take immediate priority because the ability to rapidly coordinate available assets can be the difference between saving or losing a life. Using historic records of SAR incidents for District 14, we leverage an approach called the stochastic zonal distribution model to evaluate spatiotemporal trends in emergency rates and response strategies for the probabilistic modeling of future SAR events\u27 location and frequency. The results from this analysis inform the demand parameters of three location problem formulations, which determine the operational posture of the District 14 fleet that minimizes the response to forecasted SAR emergencies. This research provides recommendations regarding the seasonal posturing of assets around the Hawaiian is- lands, the expansion of Coast Guard stations across the Pacific Ocean, the acquisition and placement of new maritime assets, and the potential impact of forward deploying assets away from their present homeports

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space