A synchronized vessel and autonomous vehicle model for environmental monitoring: Mixed integer linear programming model and adaptive matheuristic

In offshore environmental monitoring projects, ocean currents enable the detection of chemical signals from a distance, with longer observation times at each point increasing the area that can be monitored. Leveraging this principle, the covering tour problem with varying coverage was recently intro...

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Vydáno v:Computers & operations research Ročník 183; s. 107188
Hlavní autoři: Torabi, Parisa, Hemmati, Ahmad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.11.2025
Témata:
Autonomous vehicles
AUV
Matheuristics
Synchronized vessel and drone
Traveling salesman problem with drones
Traveling salesman problem with drones
AUV
Synchronized vessel and drone
Autonomous vehicles
Matheuristics
ISSN:0305-0548
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Shrnutí:In offshore environmental monitoring projects, ocean currents enable the detection of chemical signals from a distance, with longer observation times at each point increasing the area that can be monitored. Leveraging this principle, the covering tour problem with varying coverage was recently introduced for environmental monitoring. In this paper, we introduce a generalization of this problem, where we utilize a main vessel and a fleet of autonomous underwater vehicles (AUVs), and the properties of time-varying coverage, which refers to the dynamic changes in the area that can be monitored based on the duration spent at each location, to minimize the required time to visit or cover a set of pre-specified locations in an area of interest. This problem can be presented as a rich covering salesperson problem, namely the multi-visit multi-drone covering salesperson problem with varying coverage (mCSP-VC). We present a mixed integer linear programming (MILP) model of mCSP-VC, and considering the complexity of the problem, design and implement an adaptive matheuristic algorithm and showcase its effectiveness. Moreover, we investigate the effects of altering different parameters on the solutions and provide managerial insights into optimizing monitoring operations and resource allocation.
ISSN:0305-0548
DOI:10.1016/j.cor.2025.107188