A Hybrid Large Neighborhood Search Algorithm for Solving the Multi Depot UAV Swarm Routing Problem

Li, Xiaohui; Li, Peifan; Zhao, Yi; Zhang, Lu; Yuan, Dong

doi:10.1109/access.2021.3098863

Cited by 14 publications

(3 citation statements)

References 48 publications

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“…The model is solved for instances up to 75 customers. In another paper, Li et al [13] studied a multi-depot drone routing problem minimizing the total number of dispatched drones and the total travel distance. The model accounts for a maximum drone flight time.…”

Section: Literature Reviewmentioning

confidence: 99%

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Bruni

Khodaparasti

2022

Sustainability

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In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver.

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Section: Literature Reviewmentioning

confidence: 99%

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Bruni

Khodaparasti

2022

Sustainability

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show abstract

“…The model is solved for instances up to 75 customers. In another paper, Li et al [36] studied a multi-depot drone routing problem to minimize the total number of drones used and the total traveled distance. The model accounts for a maximum drone flight time.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

2023

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This paper studies the drone-aided last-mile delivery problem with shared depot resources. Our research motivation comes from E-commerce logistics, where big companies such as Amazon, are already filing up patents for the development of drone-friendly fulfillment centers towers that could serve as both charging hubs and convenient pit stops for delivery drones to pick up and drop off packages efficiently. We mainly focus on the tactical decisions about the selection of shared fulfillment centers used as the drone launch and retrieve stations and the fleet size plans. The operational drone route decisions are also incorporated into a unified framework to account for the mutual impact between tactical and operational plans. Moreover, we consider explicitly the non-linear and load-dependent nature of the energy consumption function for drone batteries. The problem is formulated as a mixed integer program with linear constraints, developed in the realm of layered networks, where the non-linear nature of energy consumption and its load dependency are incorporated and efficiently handled without the need of approximating non-linear terms. The proposed model is tested on an extensive set of instances with up to 75 customers, showing its computational efficiency. Insights about the route costs and spatial configuration of depots are also discussed.

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“…While dealing with functional heterogeneity, they proposed a Hungarian algorithm by focusing on handling uncertainties. Li et al presented a hybrid large-neighborhood search algorithm that solves a multiple depot autonomous aerial vehicle (AAV) routing problem [ 18 ]. The article is focused on addressing an open constraint on return depots without considering heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Task Allocation and Planning for a Heterogeneous Multi-Robot System to Minimize the Last Task Completion Time

Patil

Bae

Park

2022

Sensors

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This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min–max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal–dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, the newly developed algorithm can solve more generalized problems, including asymmetric cost problems with a min–max objective. Though producing optimal solutions requires high computational loads, the authors aim to find reasonable sub-optimal solutions within a short computation time. The algorithm was repeatedly tested in a simulation with varying problem sizes to verify its effectiveness. The computational results show that the algorithm can produce reliable solutions to apply in real-time operations within a reasonable time.

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A Hybrid Large Neighborhood Search Algorithm for Solving the Multi Depot UAV Swarm Routing Problem

Cited by 14 publications

References 48 publications

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

An Algorithm for Task Allocation and Planning for a Heterogeneous Multi-Robot System to Minimize the Last Task Completion Time

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