Traveling Salesman Problem for a Class of Carrier-Vehicle Systems

Garone, Emanuele; Naldi, Roberto; Casavola, Alessandro

doi:10.2514/1.50539

Cited by 28 publications

(19 citation statements)

References 12 publications

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“…In connection with the literature on drone delivery, we can also mention the Carrier‐Vehicle TSP, addressed by Garone et al or Gambella et al . In this problem, two vehicles with different capabilities cooperate.…”

Section: Related Workmentioning

confidence: 99%

An iterative two‐step heuristic for the parallel drone scheduling traveling salesman problem

et al. 2018

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A recent evolution in urban logistics involves the usage of drones. In this article, we address a heuristic solution of the parallel drone scheduling traveling salesman problem, recently introduced by Murray and Chu. In this problem, deliveries are split between a vehicle and drones. The vehicle performs a classical delivery tour, while the drones are constrained to perform back and forth trips. The objective is to minimize completion time. We propose an iterative two‐step heuristic, composed of: a coding step that transforms a solution into a customer sequence, and a decoding step that decomposes the customer sequence into a tour for the vehicle and trips for the drones. Decoding is expressed as a bicriteria shortest path problem and is carried out by dynamic programming. Experiments conducted on benchmark instances confirm the efficiency of the approach and give some insights on this drone delivery system.

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Section: Related Workmentioning

confidence: 99%

An iterative two‐step heuristic for the parallel drone scheduling traveling salesman problem

et al. 2018

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“…Building on the results from these simpler cases, they propose a heuristic for an arbitrary number of target points. The more general version of the CVTSP, in which the order of the target points is not a priori given, is studied 3 in Garone et al (2010bGarone et al ( , 2011. The authors observe that the CVP can be formulated as a convex optimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…For the resulting solution, they establish a worst-case approximation error bound, which depends on the approximation error in the first stage as well as some data-dependent parameters. In Garone et al (2011), the authors propose enumerating all possible sequences of target points and solving the CVTSP with each fixed sequence for instances with up to 5 target points.…”

Section: Introductionmentioning

confidence: 99%

Exact and Heuristic Algorithms for the Carrier–Vehicle Traveling Salesman Problem

Erdoğan

Yıldırım

2021

Transportation Science

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This paper presents new structural properties for the carrier–vehicle traveling salesman problem. The authors provide a new mixed-integer second-order conic optimization formulation, with associated optimality cuts based on the structural properties, and an iterated local search (ILS) algorithm. Computational experiments on instances from the literature demonstrate the superiority of the new formulation to the existing models and algorithms in the literature, and the high-quality solutions found by the ILS algorithm.

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“…Task assignment is an essential sub-problem of the MCMPP, which was extensively studied in robotics, UAVs, operations research, etc. From the modeling perspective, the task assignment model is very relevant to classic combinatorial optimization problems, such as vehicle routing problem (VRP) [ 3 ], job-shop scheduling problem (JSP) [ 4 ], traveling salesman problem [ 5 ], team orienteering problem [ 6 ], etc. Classic combinatorial optimization problems or variant problems can be transformed into mathematical models for missions such as cooperative reconnaissance, data collection, search and rescue, etc.…”

Section: Introductionmentioning

confidence: 99%

Solving the Multi-Functional Heterogeneous UAV Cooperative Mission Planning Problem Using Multi-Swarm Fruit Fly Optimization Algorithm

Luo¹,

Zheng

Guo

2020

Sensors

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The complexity of unmanned aerial vehicle (UAV) missions is increasing with the rapid development of UAV technology. Multiple UAVs usually cooperate in the form of teams to improve the efficiency of mission execution. The UAVs are equipped with multiple sensors with complementary functions to adapt to the complex mission constraints. Reasonable task assignment, task scheduling, and UAV trajectory planning are the prerequisites for efficient cooperation of multi-functional heterogeneous UAVs. In this paper, a multi-swarm fruit fly optimization algorithm (MFOA) with dual strategy switching is proposed to solve the multi-functional heterogeneous UAV cooperative mission planning problem with the criterion of simultaneously minimizing the makespan and the total mission time. First, the multi-swarm mechanism is introduced to enhance the global search capability of the fruit fly optimization algorithm. Second, in the smell-based search phase, the local search strategies and large-scale search strategies are designed to drive multiple fruit fly swarms, and the dual strategy switching method is presented. Third, in the vision-based search stage, the greedy selection strategy is adopted. Finally, numerical simulation experiments are designed. The simulation results show that the MFOA algorithm is more effective and stable for solving the multi-functional heterogeneous UAV cooperative mission planning problem compared with other algorithms.

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Traveling Salesman Problem for a Class of Carrier-Vehicle Systems

Cited by 28 publications

References 12 publications

An iterative two‐step heuristic for the parallel drone scheduling traveling salesman problem

An iterative two‐step heuristic for the parallel drone scheduling traveling salesman problem

Exact and Heuristic Algorithms for the Carrier–Vehicle Traveling Salesman Problem

Solving the Multi-Functional Heterogeneous UAV Cooperative Mission Planning Problem Using Multi-Swarm Fruit Fly Optimization Algorithm

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