An adaptive large neighborhood search heuristic for the flying sidekick traveling salesman problem with multiple drops

Mara, Setyo Tri Windras; Rifai, Achmad Pratama; Sopha, Bertha Maya

doi:10.1016/j.eswa.2022.117647

Cited by 23 publications

(8 citation statements)

References 48 publications

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“…Several problems, such as the pollution-routing problem [13], the electric fleet size and mix VRP with time windows and recharging stations [23], the two-echelon VRP [22] and the service technician routing and scheduling problem [25], have been solved using ALNS. It has also been applied to address DRPs in various studies, such as [8,26,30,44,53]. Recently, [9,19] developed ALNS-based algorithms for dealing with DRPs with applications in post-disaster management.…”

Section: Adaptive Large Neighborhood Searchmentioning

confidence: 99%

Solving the probabilistic drone routing problem: Searching for victims in the aftermath of disasters

Almeida Coco,

Duhamel,

Santos

et al. 2024

Networks

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Several major industrial disasters happen each year around the world. They usually involve limited accessibility, poor ground conditions, and toxic wastes. As a consequence, this reduces the efficiency of humanitarian operations. In such a context, flying drones may be a viable alternative: faster, no dependency on ground conditions, and larger areas scanned. They are also better suited for following the population and the crisis dynamic. For such a purpose, various issues have to be addressed such as defining and optimizing the drone's routes, their energy consumption, choosing the relay points for recharging equipment, among others. In this study, several additional features from existing works are considered: first, a probability of identifying individuals is defined. Thus, each node can be scanned several times in order to improve the observation. In addition, the nodes are prioritized according to a given heatmap. The probabilistic drone routing problem (PDRP) consists of finding a route, that is, a sequence of trips, for each drone such that the sum of the expected number of identified individuals on all routes is maximized. Constraints on energy consumption, collision avoidance and drone‐base assignment are considered. We propose a heuristic and metaheuristics based on the adaptive large neighborhood search for the PDRP. The methods are tested on theoretical instances, as well as on a case study of the Beirut Port explosion on August 4, 2020, in order to analyze the performance of the proposed methods.

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Section: Adaptive Large Neighborhood Searchmentioning

confidence: 99%

Solving the probabilistic drone routing problem: Searching for victims in the aftermath of disasters

Almeida Coco,

Duhamel,

Santos

et al. 2024

Networks

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“…Luo et al [8] addressed the multi-drone multivisit traveling salesman problem and harnessed a multistart tabu search methodology to tackle the challenge, involving up to a hundred customers. Lastly, Mara et al [36] devised a heuristic algorithm grounded in Adaptive Large-scale Neighborhood Search (ALNS) techniques for the resolution of this problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Murry et al's [7] research shows that the MFSTSP problem is NP-hard, so the MV-MFSTSP problem is also NP-hard. The ALNS algorithm has been widely used to solve NP-hard problems [36], and the operators of ALNS are customizable. Hence, it can specifically optimize the paths for trucks and drones to work together.…”

Section: Algorithm Frameworkmentioning

confidence: 99%

“…The ALNS algorithm is commonly employed for addressing the collaborative delivery path optimization problem concerning trucks and drones [36]. Hence, to assess the algorithm's efficacy, this paper applies both the GALNS and ALNS algorithms to medium-and large-scale instances, subsequently compares their solution results (Table 5).…”

Section: Comparative Analysis Of Medium-scale and Large-scale Instancesmentioning

confidence: 99%

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Research on Optimization of Transport Path for Novel Coronavirus Detection Samples

Han,

Liu

2024

Pol. J. Environ. Stud.

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The eruption of the COVID-19 epidemic has engendered significant public health security perils, representing a grave menace to human well-being and vitality, while also exerting a profound impact on economic and societal spheres [1,2]. The virus has afflicted humanity through modes of respiratory droplet and contact transmission. To mitigate the proliferation of the virus, the Chinese government has actively carried out nucleic acid testing [3], which has also spawned research on optimizing the transport path of new coronavirus testing samples. Thus, the investigation into the conveyance path of these samples bears immense practical significance. The sample transfer process commonly entails healthcare personnel collecting samples at diverse testing sites, followed by the imperative dispatch of vehicles from the testing center to gather these samples. However, due to the variability in sample quantities, collection times, and frequencies, this results in an inefficacious utilization of vehicle cargo space and the proliferation of circuitous paths, thereby inflating transshipment costs. Certain testing sites may be inaccessible via vehicular means, necessitating

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“…Luo et al [38] studied the multi-drone multi-visit traveling salesman problem and assumed that the energy consumption of drones is associated with the flight time, self-weight, and payload; they also proposed a multi-start tabu search to answer up to 100 customers' questions. Mara et al [39] proposed a novel mathematical formula and a heuristic algorithm based on an Adaptive Large-scale Neighborhood Search algorithm (ALNS) to meet the challenge of path optimization for combined systems. Mahmoudi and Eshghi [40] considered the Energy-constrained Multiple-visit Traveling Salesman Problem (EM-TSPD) of multiple drones at non-customer rendezvous locations, provided a mathematical model for EM-TSPD, and designed a heuristic algorithm to handle medium and large-scale instances.…”

Section: Related Literaturementioning

confidence: 99%

Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand

Han,

Liu,

2023

Applied Sciences

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As a new delivery mode, the collaborative delivery of packages using trucks and drones has been proven to reduce delivery costs and delivery time. To cope with the huge cost challenges brought by strict time constraints and ever-changing customer orders in the actual delivery process, we established a two-stage optimization model based on different demand response strategies with the goal of minimizing delivery costs. To solve this problem, we designed a simulated annealing chimp optimization algorithm with a sine–cosine operator. The performance of this algorithm is improved by designing a variable-dimensional matrix encode to generate an initial solution, incorporating a sine–cosine operator and a simulated annealing mechanism to avoid falling into a local optimum. Numerical experiments verify the effectiveness of the proposed algorithm and strategy. Finally, we analyze the impact of dynamic degree on delivery cost. The proposed model and algorithm extend the theory of the vehicle routing problem with drones and also provide a feasible solution for route planning, taking into account dynamic demands and time windows.

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An adaptive large neighborhood search heuristic for the flying sidekick traveling salesman problem with multiple drops

Cited by 23 publications

References 48 publications

Solving the probabilistic drone routing problem: Searching for victims in the aftermath of disasters

Solving the probabilistic drone routing problem: Searching for victims in the aftermath of disasters

Research on Optimization of Transport Path for Novel Coronavirus Detection Samples

Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand

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