Optimum placement of sea rescue resources

Azofra, M.; Pérez-Labajos, C.A.; Blanco, Beatriz; Achútegui, Juan J.

doi:10.1016/j.ssci.2006.09.002

Cited by 47 publications

(29 citation statements)

References 2 publications

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“…The maximum number of island rescue bases is limited by constraints (7). Constraint (8) means that the time between demand site i and supply site j that meets the demand site i is below the maximum access time T. Constraints (9) restricts x j to become one only if island j is selected as a base. Constraints (10) restricts y j to become one only if there is a base j that can reach cell i in time.…”

Section: Selection Of the Optimal Locationmentioning

confidence: 99%

“…Maritime SAR bases have been established in many countries. However, most bases are located along coastal areas; consequently, rescue ships may not arrive at the accident site in remote sea areas within an effective rescue time [8]. In this context, islands are being considered as suitable base sites to improve the capacity of maritime SAR in remote sea areas [6].…”

Section: Introductionmentioning

confidence: 99%

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Integrating Island Spatial Information and Integer Optimization for Locating Maritime Search and Rescue Bases: A Case Study in the South China Sea

Zhou

Cheng

Zhang

et al. 2019

IJGI

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Maritime search and rescue (SAR) operations are critical for ensuring safety at sea. Islands have been considered as feasible solutions for the construction of new maritime SAR bases to improve the capacity of SAR operations in remote sea areas. This paper proposes a new framework, based on island spatial information, for determining the optimal locations for maritime SAR bases. The framework comprises four steps. First, candidate islands for the construction of maritime SAR bases are selected. Second, the potential rescue demand is estimated by employing ship location data and marine incident data. In the third step, the response time from candidate islands to any site at sea is calculated, with explicit consideration of the impact of sea conditions on the ship’s speed. Fourth, the final island locations are proposed by solving the maximal covering location problem (MCLP). The proposed framework was applied to the South China Sea. The results showed that there would be a decrease of 1.09 h in terms of the mean access time for the South China Sea if the six selected island bases were constructed, whilst the primary coverage increased from 62.63% to 80.02% when using a 6-hour threshold. This new framework is expected to contribute to improvements in safety at sea and should be applicable to any sea area where the construction of island rescue bases is being considered.

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Section: Selection Of the Optimal Locationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating Island Spatial Information and Integer Optimization for Locating Maritime Search and Rescue Bases: A Case Study in the South China Sea

Zhou

Cheng

Zhang

et al. 2019

IJGI

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“…Instead, user's patronage was basically divided among all facilities by applying The Gravity Rule. Azofra et al (2007) studied the placement of sea rescue resources based on gravitational models which allowed us to define individual and zonal distribution models. A model in which the market capture is maximized with the assumption of less travel and waiting time, which are the major concerns of the customer to choose a facility, was proposed for locating the facilities by Marianov et al (2008).…”

Section: Fig 1 the Graphical Representation Of Nodes In A Transportmentioning

confidence: 99%

Many to many hub and spoke location routing problem based on the gravity rule

Khosravi¹,

Jokar²

2018

10.5267/j.uscm

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This paper examines the spoke and hub location decisions in a routing problem. To minimize the total cost, the study analyzes on how to locate the spokes, hubs and the allocation of spoke nodes to hub nodes, the routing among the nodes and the number of vehicles assigned to each hub thoroughly. As there might be no facility assigned to some points, unsatisfied demands must be distributed to other nodes with available facilities. Furthermore, the realized demand is determined by considering the perceived utility of each path, using The Gravity rule. For this purpose, the proposed nonlinear model is transformed into a linear programming model, where some tightening rules and preprocessing procedures are applied, and also the sequential and integrated approaches are developed to solve the problem. In the sequential method, spokes are allocated, and hubs are selected based on the location of the spokes, after which the routing in the local tour is determined. Meanwhile, in the integrated approach, the aggregated model is solved. A heuristic is presented to address the integrated model. Numerical experiments are run on both approaches, to compare both, and obtain insights from the model.

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“…Azofra et al (2007) propose a general methodology based on gravitational models for defining individual and zonal distribution models. In formalizing these distribution models, the authors consider a series of factors, which include the following: the characteristics of the accident; the vessel and the resulting damage; the type of the accident and the establishment of a scale of severity; the distribution of resources (e.g., helicopters, tug Downloaded from informs.org by [129.93.16.3] on 10 June 2016, at 13:15 .…”

Section: Literature Reviewmentioning

confidence: 99%

Determining the Distribution of Coast Guard Vessels

Uzun¹,

Dağdeviren²,

Kabak³

2016

Interfaces

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A typical coast guard has complex responsibilities, subject to its multiple missions in maritime jurisdiction regions. These maritime jurisdiction regions can be partitioned into responsibility areas, and each type of coast guard vessel has a set of attributes for each responsibility area. The distribution of coast guard assets deployed in their respective areas of responsibility should be linked to the types of events occurring in these areas. The objective of this study is to determine the appropriate distribution of coast guard assets in various maritime zones; we do this by evaluating models that assign coast guard surface vessel types to missions in these maritime zones. We use the analytic network process model to compare various vessel types and determine the task-type weights for each vessel type. Weighted goal programming allows us to combine data, including the weights we derive from the use of other decision-aid tools. Our objective is to determine the best compromise solution for distributing coast guard assets into four main responsibility zones. The Turkish Coast Guard implemented the model in 2014, and decided to use it once a year prior to executing the main process in which it allocates coast guard assets.

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Optimum placement of sea rescue resources

Cited by 47 publications

References 2 publications

Integrating Island Spatial Information and Integer Optimization for Locating Maritime Search and Rescue Bases: A Case Study in the South China Sea

Integrating Island Spatial Information and Integer Optimization for Locating Maritime Search and Rescue Bases: A Case Study in the South China Sea

Many to many hub and spoke location routing problem based on the gravity rule

Determining the Distribution of Coast Guard Vessels

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