Decision Support for Planning Maritime Search and Rescue Operations in Canada

Abi‐Zeid, Irène; Morin, Michael; Nilo, Oscar

doi:10.5220/0007730303280339

Cited by 4 publications

(3 citation statements)

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“…Problems of the kind modeled in this paper arise in search‐and‐detection operations (see Abi‐Zeid et al, 2019; Washburn, 2002, Chap. 7 for a discussion of tools used by the US Coast Guard and the US Navy), in counter‐drug interdiction (Pietz & Royset, 2013, 2015; Zhang et al, 2020), and in counter‐piracy operations (Bourque, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The literature also includes branch‐and‐bound algorithms that solve sequences of convex subproblems (Eagle & Yee, 1990) and many heuristics (Abi‐Zeid et al, 2019; Dell et al, 1996; Grundel, 2005; Hollinger & Singh, 2008; Lanillos et al, 2012; Riehl et al, 2007; Wong et al, 2005), but they lack optimality guarantees. Routing of constrained searchers in discrete time and space has similarities with (team) orienteering and related reward‐collecting vehicle routing problems; see, for example, Cho and Batta (2021), Moskal et al (2023), Pietz and Royset (2013), and Royset and Reber (2009).…”

Section: Introductionmentioning

confidence: 99%

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Multi‐agent search for a moving and camouflaging target

Lejeune,

Royset,

2023

Naval Research Logistics

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In multi‐agent search planning for a randomly moving and camouflaging target, we examine heterogeneous searchers that differ in terms of their endurance level, travel speed, and detection ability. This leads to a convex mixed‐integer nonlinear program, which we reformulate using three linearization techniques. We develop preprocessing steps, outer approximations via lazy constraints, and bundle‐based cutting plane methods to address large‐scale instances. Further specializations emerge when the target moves according to a Markov chain. We carry out an extensive numerical study to show the computational efficiency of our methods and to derive insights regarding which approach should be favored for which type of problem instance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐agent search for a moving and camouflaging target

Lejeune,

Royset,

2023

Naval Research Logistics

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“…A threestage decision support method to optimize the type and number of resources used when developing SAR schemes to formulate an emergency response more efficiently and effectively has been developed [28]. Some studies [29]- [31] provided useful models and algorithms for increasing the probability of detection (POD) and the probability of success (POS). A feasibility study on geographic information system (GIS)-based cost distance modeling to support strategic maritime SAR planning has been provided [32].…”

Section: Introductionmentioning

confidence: 99%

Optimal Search Facilities Selection Model for Joint Aeronautical and Maritime Search

Xing

2021

IEEE Access

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Joint aeronautical and maritime search and rescue is the most effective way of performing rescues at sea. The value and effectiveness of a search and rescue (SAR) are far greater when using a coordinated air-maritime search than when using only vessels or aircraft. However, the harmonization of aeronautical and maritime SAR is complex and potentially life-threatening. When the location of the target in distress is unknown, the search process must be carried out. As the sole way to locate and rescue survivors, the search process is the most costly, hazardous, and complicated part of the whole SAR operation. This paper focuses on the key problem of the optimal selection of search facilities, that is often encountered in largearea maritime search practice and urgently needs to be solved in joint aeronautical and maritime search operations. The problem may be abstracted into an optimization model with vessel and aircraft quantitative constraints that fully considers the area of the sea region to be searched, maximum speeds, search capabilities, initial distances of vessels and aircraft from the search area, and maximum endurance of aircraft. By introducing 0-1 decision variables, the search facility selection can be judged and optimized directly and effectively. By analyzing the results with different vessel and aircraft quantities, and taking the relationship between search coverage time and the number of search facilities (cost) into account, the optimal (most economic and feasible) search facility selection scheme can be produced. INDEX TERMS Joint aeronautical and maritime search, marine safety, search facility, optimal model.

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