Minimizing dispersion in multiple drone routing

Dhein, Guilherme; Zanetti, Marcelo Serrano; Araújo, Olinto César Bassi de; Cardoso, Ghendy

doi:10.1016/j.cor.2019.04.022

Cited by 8 publications

(7 citation statements)

References 30 publications

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“…This objective function is minimized in order to keep vehicles traveling with reduced relative distance. A formal definition of MDRP can be found in Dhein et al (2019), where a mathematical model is provided.…”

Section: Minimum Dispersion Routing Problemmentioning

confidence: 99%

See 1 more Smart Citation

On the Np-Hardness of the Minimum Dispersion Routing Problem

Dhein,

Zanetti,

Araújo

2023

Pesqui. Oper.

Self Cite

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In the Minimum Dispersion Routing Problem, a set of vertices must be served by tours with trajectories defined in order to reduce the dispersion of vehicles. Tours must be related to each other and impose a spatial and temporal synchronization among vehicles, quantified by an original dispersion metric used in an objective function to be minimized. In this paper, we demonstrate that Euclidean Traveling Salesman Problem solutions can be found by MDRP solvers. We describe a method to reduce Euclidean Traveling Salesman Problem instances to Minimum Dispersion Routing Problem instances in polynomial time, proving that the last one is NP-Hard.

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Section: Minimum Dispersion Routing Problemmentioning

confidence: 99%

“…The Minimum Dispersion Routing Problem (MDRP) is a recently proposed routing problem, first described in Dhein et al (2019). An MDRP solution is composed of a set of tours that present spatial similarity, but also present a temporal synchronization that keeps vehicles close to each other while traveling their tours.…”

Section: Introductionmentioning

confidence: 99%

On the Np-Hardness of the Minimum Dispersion Routing Problem

Dhein,

Zanetti,

Araújo

2023

Pesqui. Oper.

Self Cite

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“…Their approach keeps the drone safe from the obstacle within an effective range. Another article suggests a search for known trajectory options [16]. In their proposal, a usable genetic algorithm is used, which would be very useful in choosing the optimal crawl route for our study as well.…”

Section: Path Planningmentioning

confidence: 99%

A Path Planning Model for Stock Inventory Using a Drone

et al. 2022

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In this study, a model and solution are shown for controlling the inventory of a logistics warehouse in which neither satellite positioning nor IoT solutions can be used. Following a review of the literature on path planning, a model is put forward using a drone that can be moved in all directions and is suitable for imaging and transmission. The proposed model involves three steps. In the first step, a traversal path definition provides an optimal solution, which is pre-processing. This is in line with the structure and capabilities of the warehouse. In the second step, the pre-processed path determines the real-time movement of the drone during processing, including camera movements and image capture. The third step is post-processing, i.e., the processing of images for QR code identification, the interpretation of the QR code, and the examination of matches and discrepancies for inventory control. A key benefit for the users of this model is that the result can be achieved without any external orientation tools, relying solely on its own movement and the organization of a pre-planned route. The proposed model can be effective not only for inventory control, but also for exploring the structure of a warehouse shelving system and determining empty cells.

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“…Constraints (8) and ( 9) ensure that if a RPA leaves a DC, that RPA is assigned only to that DC. Constraint (10) ensures that no additional edges stem from each node to itself. Constraints (11) and (12) examine the starting time of service for per demand point.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Although RPASs are increasingly popular in numerous areas of application in industries and universities [10,11], nonetheless, challenges such as heavy air traffic in urban and non-urban permitted flying areas, flight time constraints, batteries or loading limits, and unexpected weather conditions necessitate the design of a model for optimal routing throughout the supply chain of distribution.…”

Section: Introductionmentioning

confidence: 99%

Secured Multi-Dimensional Robust Optimization Model for Remotely Piloted Aircraft System (RPAS) Delivery Network Based on the SORA Standard

Mahmoodi

Hashemi

Laliberté

et al. 2022

Designs

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The range of applications of RPAs in various industries indicates that their increased usage could reduce operational costs and time. Remotely piloted aircraft systems (RPASs) can be deployed quickly and effectively in numerous distribution systems and even during a crisis by eliminating existing problems in ground transport due to their structure and flexibility. Moreover, they can also be useful in data collection in damaged areas by correctly defining the condition of flight trajectories. Hence, defining a framework and model for better regulation and management of RPAS-based systems appears necessary; a model that could accurately predict what will happen in practice through the real simulation of the circumstances of distribution systems. Therefore, this study attempts to propose a multi-objective location-routing optimization model by specifying time window constraints, simultaneous pick-up and delivery demands, and the possibility of recharging the used batteries to reduce, firstly, transport costs, secondly, delivery times, and thirdly, estimated risks. Furthermore, the delivery time of the model has been optimized to increase its accuracy based on the uncertain conditions of possible traffic scenarios. It is also imperative to note that the assessment of risk indicators was conducted based on the Specific Operations Risk Assessment (SORA) standard to define the third objective function, which was conducted in a few previous studies. Finally, it shows how the developed NSGA-II algorithm in this study performed successfully and reduced the objective function by 31%. Comparing the obtained results using an NSGA-II meta-heuristic approach, through the rigorous method GAMS, indicates that the results are valid and reliable.

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Minimizing dispersion in multiple drone routing

Cited by 8 publications

References 30 publications

On the Np-Hardness of the Minimum Dispersion Routing Problem

On the Np-Hardness of the Minimum Dispersion Routing Problem

A Path Planning Model for Stock Inventory Using a Drone

Secured Multi-Dimensional Robust Optimization Model for Remotely Piloted Aircraft System (RPAS) Delivery Network Based on the SORA Standard

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