The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach

Liu, Zeyu; Li, Xueping; Khojandi, Anahita

doi:10.1016/j.tre.2022.102816

Cited by 36 publications

(12 citation statements)

References 78 publications

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“…The concept of truck-drone pair has been on the rising tide to provide logistics support for the last mile. In [ 41 ], authors proposed the (FSTSP-STT) scheme to address the parcel delivery problem as a modified FSTSP, which is similar to the vehicular routing problem (VRP), and formulated it using Markov decision process (MDP) by considering the stochasticity of the travel time. The proposed formulated scheme is answered by using deep Q-networks (DQN), which combines reinforcement learning (RL) and deep neural networks, and further refined by the attention mechanism.…”

Section: Drone Routing Algorithmsmentioning

confidence: 99%

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Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security

Raivi

Huda

Arafat

et al. 2023

Sensors

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Recently, owing to the high mobility and low cost of drones, drone-based delivery systems have shown considerable potential for ensuring flexible and reliable parcel delivery. Several crucial design issues must be considered to design such systems, including route planning, payload weight consideration, distance measurement, and customer location. In this paper, we present a survey of emerging drone routing algorithms for drone-based delivery systems, emphasizing three major drone routing aspects: trajectory planning, charging, and security. We focus on practical design considerations to ensure efficient, flexible, and reliable parcel delivery. We first discuss the potential issues arising when designing such systems. Next, we present a novel taxonomy based on the above-mentioned three aspects. We extensively review each algorithm for drone routing in terms of key features and operational characteristics. Furthermore, we compare the algorithms in terms of their main idea, advantages, limitations, and performance aspects. Finally, we present open research challenges to motivate further research in this field. In particular, we focus on the major aspects that researchers and engineers need to consider in order to design effective and reliable drone routing algorithms for drone-based delivery systems.

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Section: Drone Routing Algorithmsmentioning

confidence: 99%

“… Taxonomy of algorithms for drone routing in drone-based delivery systems: A-Ptr-Net [ 6 ], JRCS [ 12 ], FSTSP-STT [ 41 ], RL-TDCD [ 42 ], CRP-TD [ 15 ], EHTDDP [ 16 ], ODCS [ 44 ], CBPTCS [ 11 ], DDC [ 17 ], CDNC [ 51 ], Covadel [ 56 ], PA-NOP [ 57 ], GaRuDa [ 58 ], DeliveryCoin [ 60 ]. …”

Section: Drone Routing Algorithmsmentioning

confidence: 99%

Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security

Raivi

Huda

Arafat

et al. 2023

Sensors

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“…The DVRPRTTs studied in the literature are also motivated by different applications, including freight pickup and delivery (Güner et al, 2017;Rifki et al, 2020), dial-a-ride with ride-sharing (Liang et al, 2020), drone-assisted parcel delivery (Liu et al, 2022), etc. Almost all DVRPRTTs incorporate stochastic travel time information and have service time windows associated with customers.…”

Section: Dvrp With Random Travel Timesmentioning

confidence: 99%

“…Lorini et al (2011) and Respen et al (2019) evaluate the diversion opportunities in DVRPRTTs, while Güner et al (2017) and Vodopivec and Miller-Hooks (2017) develop proactive waiting strategies to exploit the dynamic traffic information. Liu et al (2022) introduce a flying sidekick TSP with stochastic travel times, in which a pair of truck and drone is employed to deliver parcels: the truck travels in the road network and serves a subset of customers, meanwhile the drone is dispatched from the truck to deliver parcels to the other customers. We recall that random travel times are also considered in some DPDPs (see Table 5), but these problems mostly do not incorporate stochastic information.…”

Section: Dvrp With Random Travel Timesmentioning

confidence: 99%

“…The other DVRPRTTs are mostly modeled as MDPs. To solve these MDPs, Toriello et al (2014) and Yu and Yang (2019) develop approximate linear programming approaches; Kim et al (2016) propose rollout-based approaches to avoid curses of dimensionality; Köster et al (2018) introduce a heuristic policy which aims at minimizing the expected travel time and avoiding the potentially critical areas in the city; Liu et al (2022) develop a reinforcement-learning approach based on deep Q-network and advance actor-critic algorithms. Most DVRPRTT instances solved in the recent literature are based on real-world road networks (Kim et al, 2016;Güner et al, 2017;Köster et al, 2018;Liang et al, 2020;Liu et al, 2022), while Toriello et al (2014), Respen et al (2019), and Yu and Yang (2019) generate instances randomly or based on SVRP benchmarks.…”

Section: Dvrp With Random Travel Timesmentioning

confidence: 99%

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Dynamic vehicle routing with random requests: A literature review

Zhang

Woensel

2023

International Journal of Production Economics

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A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

Faiz,

Vogiatzis,

Liu

et al. 2024

Networks

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Providing first aid and other supplies (e.g., epi‐pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emerging technologies like uncrewed autonomous vehicles can help humanitarian logistics providers reach otherwise stranded populations after transportation network failures. However, due to the failures in telecommunication infrastructure, demand for emergency aid can become uncertain. To address the challenges of delivering emergency aid to trapped populations with failing infrastructure networks, we propose a novel robust computational framework for a two‐echelon vehicle routing problem that uses uncrewed autonomous vehicles (UAVs), or drones, for the deliveries. We formulate the problem as a two‐stage robust optimization model to handle demand uncertainty. Then, we propose a column‐and‐constraint generation approach for worst‐case demand scenario generation for a given set of truck and UAV routes. Moreover, we develop a decomposition scheme inspired by the column generation approach to generate UAV routes for a set of demand scenarios heuristically. Finally, we combine the decomposition scheme within the column‐and‐constraint generation approach to determine robust routes for both trucks (first echelon vehicles) and UAVs (second echelon vehicles), the time that affected communities are served, and the quantities of aid materials delivered. To validate our proposed algorithms, we use a simulated dataset that aims to recreate emergency aid requests in different areas of Puerto Rico after Hurricane Maria in 2017.

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The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach

Cited by 36 publications

References 78 publications

Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security

Drone Routing for Drone-Based Delivery Systems: A Review of Trajectory Planning, Charging, and Security

Dynamic vehicle routing with random requests: A literature review

A robust optimization framework for two‐echelon vehicle and UAV routing for post‐disaster humanitarian logistics operations

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