A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone

Bogyrbayeva, Aigerim; Yoon, Taehyun; Ko, Hanbum; Lim, Sungbin; Yun, Hyokun; Kwon, Changhyun

doi:10.1016/j.trc.2022.103981

Cited by 33 publications

(9 citation statements)

References 24 publications

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“…The enhanced GA exhibits improved performance, affirming the effectiveness of the proposed enhancement. A hybrid model proposed in [12] uses attention encoder and Long Short-Term Memory (LSTM) network decoder to overcome the coordination failure between the vehicles when state-less attention-based decoder is used. The hybrid model which was experimented on min-max Capacitated Vehicle Routing Problem (mmCVRP) improves the solution quality and computational efficiency over the baseline methods.…”

Section: Related Workmentioning

confidence: 99%

Integrated Q-Learning with Firefly Algorithm for Transportation Problems

Pratiba,

Ridhanya,

Ridhisha

et al. 2024

EAI Endorsed Trans Energy Web

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The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes.

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Section: Related Workmentioning

confidence: 99%

Integrated Q-Learning with Firefly Algorithm for Transportation Problems

Pratiba,

Ridhanya,

Ridhisha

et al. 2024

EAI Endorsed Trans Energy Web

View full text Add to dashboard Cite

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“…Specifically, RL is now used successfully in the drone industry ( Bogyrbayeva et al, 2023 ). This is mentioned not just in this article, but also in other recently published works.…”

Section: Related Workmentioning

confidence: 99%

Towards the portability of knowledge in reinforcement learning-based systems for automatic drone navigation

Barreiro

Lara

Manrique

et al. 2023

PeerJ Computer Science

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In the field of artificial intelligence (AI) one of the main challenges today is to make the knowledge acquired when performing a certain task in a given scenario applicable to similar yet different tasks to be performed with a certain degree of precision in other environments. This idea of knowledge portability is of great use in Cyber-Physical Systems (CPS) that face important challenges in terms of reliability and autonomy. This article presents a CPS where unmanned vehicles (drones) are equipped with a reinforcement learning system so they may automatically learn to perform various navigation tasks in environments with physical obstacles. The implemented system is capable of isolating the agents’ knowledge and transferring it to other agents that do not have prior knowledge of their environment so they may successfully navigate environments with obstacles. A complete study has been performed to ascertain the degree to which the knowledge obtained by an agent in a scenario may be successfully transferred to other agents in order to perform tasks in other scenarios without prior knowledge of the same, obtaining positive results in terms of the success rate and learning time required to complete the task set in each case. In particular, those two indicators showed better results (higher success rate and lower learning time) with our proposal compared to the baseline in 47 out of the 60 tests conducted (78.3%).

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“…Wang et al [24] examined the TSPD from a manager's perspective, with biobjectives of operating cost and completion time, and proposed an improved nondominated sorting genetic algorithm to solve the problem. Additionally, several studies focused on exploring precise methods for the FSTSP/TSPD [25][26][27][28] and heuristic algorithms [29,30].…”

Section: Related Literaturementioning

confidence: 99%

Multivisit Drone-Vehicle Routing Problem with Simultaneous Pickup and Delivery considering No-Fly Zones

Liu,

Han,

Zhang

et al. 2023

Discrete Dynamics in Nature and Society

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The employment of drones for the distribution of goods represents a significant avenue for addressing logistical challenges at the end of the supply chain. The truck-drone cooperative delivery model overcomes drone limitations such as limited capacity and endurance and has emerged as a crucial mode of drone participation in logistics delivery. This delivery model effectively reduces delivery costs and shortens delivery times. Herein, we examine a variant of the truck-drone routing problem, which encompasses the strategic deployment and routing of multiple fleets of trucks, each equipped with an auxiliary drone. The objective is to fulfill all the pickup and delivery demands of a designated customer base while minimizing the overall route cost. Within this problem domain, drones are authorized to serve multiple customers within their capacity and endurance limits, providing both pickup and delivery services during each trip. However, the utilization of drones for servicing all customers is impeded by the existence of no-fly zones that have been implemented in numerous cities worldwide. These prescribed no-fly zones cause significant challenges when attempting to optimize the routing of truck-drone operations. Thus, this study constructs a mixed integer linear programming (MILP) model for the path optimization problem of joint service of trucks and drones considering no-fly zones and simultaneous pickup and delivery. Given the intricacy of the MILP model, we propose a two-stage heuristic algorithm based on a simulated annealing approach, combined with strategies for rectifying infeasible solutions and expediting algorithmic processes. During the phase of computational experimentation, we explore the advantages derived from enabling drones to serve multiple customers and assess the effectiveness of the proposed model and two-stage heuristic algorithm. Finally, sensitivity analysis is conducted on two key parameters.

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A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone

Cited by 33 publications

References 24 publications

Integrated Q-Learning with Firefly Algorithm for Transportation Problems

Integrated Q-Learning with Firefly Algorithm for Transportation Problems

Towards the portability of knowledge in reinforcement learning-based systems for automatic drone navigation

Multivisit Drone-Vehicle Routing Problem with Simultaneous Pickup and Delivery considering No-Fly Zones

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