Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming

Ham, Andy

doi:10.1016/j.trc.2018.03.025

Cited by 233 publications

(112 citation statements)

References 14 publications

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“…The authors of Reference [27] extended the problem PDSTSP proposed in Reference [20] by considering two different types of drone tasks: drop and pickup. Once a drone achieves a drop, it can either return to the depot to deliver the next parcels or fly directly to another customer for pickup.…”

Section: Delivery By N-trucks and M-dronesmentioning

confidence: 99%

“…• HGA performs approximately 1.5 times slower than GRASP but can still deliver better solutions in less than 1 min and 5 min for 50 and 100 node instances, respectively. 2018 [27] • • In the instance proposed in [17], the best improvement occurred in one instance with 75 nodes (also called customers) where the drone traveled twice as fast as the truck and the least improvements were observed for instances where both vehicles presented the same speed.…”

Section: Analysis Of the Reviewed Papersmentioning

confidence: 99%

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A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles

Khoufi

Laouiti

Adjih

2019

Drones

117

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The use of Unmanned Aerial Vehicles (UAVs) is rapidly growing in popularity. Initially introduced for military purposes, over the past few years, UAVs and related technologies have successfully transitioned to a whole new range of civilian applications such as delivery, logistics, surveillance, entertainment, and so forth. They have opened new possibilities such as allowing operation in otherwise difficult or hazardous areas, for instance. For all applications, one foremost concern is the selection of the paths and trajectories of UAVs, and at the same time, UAVs control comes with many challenges, as they have limited energy, limited load capacity and are vulnerable to difficult weather conditions. Generally, efficiently operating a drone can be mathematically formalized as a path optimization problem under some constraints. This shares some commonalities with similar problems that have been extensively studied in the context of urban vehicles and it is only natural that the recent literature has extended the latter to fit aerial vehicle constraints. The knowledge of such problems, their formulation, the resolution methods proposed—through the variants induced specifically by UAVs features—are of interest for practitioners for any UAV application. Hence, in this study, we propose a review of existing literature devoted to such UAV path optimization problems, focusing specifically on the sub-class of problems that consider the mobility on a macroscopic scale. These are related to the two existing general classic ones—the Traveling Salesman Problem and the Vehicle Routing Problem. We analyze the recent literature that adapted the problems to the UAV context, provide an extensive classification and taxonomy of their problems and their formulation and also give a synthetic overview of the resolution techniques, performance metrics and obtained numerical results.

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Section: Delivery By N-trucks and M-dronesmentioning

confidence: 99%

Section: Analysis Of the Reviewed Papersmentioning

confidence: 99%

A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles

Khoufi

Laouiti

Adjih

2019

Drones

117

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“…A problem where multiple drones, multiple trucks and multiple depots are considered is presented in [14]. This problem is a generalisation of the PDSTSP where drones can perform pickup after dropping parcels, customers can be visited twice in different time windows, and single and multiple depots instances are considered.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For all the experiments presented in the paper we will have δ = 20. Note that inequalities (14) improve the quality of the linear relaxation of the enriched model, although they do not impose anything new to the optimal solution. Further changes are introduced to the algorithm to make the MILP more tractable.…”

Section: Implementation Strategy For Large Instancesmentioning

confidence: 99%

Matheuristic algorithms for the parallel drone scheduling traveling salesman problem

2020

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In a near future drones are likely to become a viable way of distributing parcels in a urban environment. In this paper we consider the parallel drone scheduling traveling salesman problem, where a set of customers requiring a delivery is split between a truck and a fleet of drones, with the aim of minimizing the total time required to service all the customers.We present a set of matheuristic methods for the problem. The new approaches are validated via an experimental campaign on two sets of benchmarks available in the literature. It is shown that the approaches we propose perform very well on small/medium size instances. Solving a mixed integer linear programming model to optimality leads to the first optimality proof for all the instances with 20 customers considered, while the heuristics are shown to be fast and effective on the same dataset. When considering larger instances with 48 to 229 customers, the results are competitive with stateof-the-art methods and lead to 28 new best known solutions out of the 90 instances considered.

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“…Numerical analysis is applied to minimize the total costs by exploring the relationship among four parameters: working period, drone speed, demand density of service area, and battery capacity. Furthermore, multiple vehicles and time windows are considered in Ham (2018),…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Echelon Routing Problem for Parcel Delivery by Cooperated Truck and Drone

Liu¹,

Shi²,

Wu³

et al. 2019

Preprint

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A new variant of two-echelon routing problem is investigated, where the truck and the drone are used to cooperatively complete the deliveries of all parcels. The truck not only acts as a tool for parcel delivery, but also serves as a moving depot for the drone. The drone can carry several parcels and take off from the truck, while returning to the truck after completing the delivery. The energy consumption model for the routing process of the drone is analyzed, when it is utilized to deliver multiple parcels. A two-stage route-based modelling approach is proposed to optimize both the truck’s main route and the drone’s adjoint flying routes. A hybrid heuristic integrating nearest neighbor and cost saving strategies is developed to quickly construct a feasible solution. The simulated annealing algorithm is applied to improve the quality of the solution, where a Tabu list is employed to improve the search efficiency. Random instances at different scales are used to test the performance of the proposed algorithm. A case study based on the practical road network in Changsha, China, is presented, through which the sensitivity analysis is conducted with respect to some critical factors.

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Integrated scheduling of m-truck, m-drone, and m-depot constrained by time-window, drop-pickup, and m-visit using constraint programming

Cited by 233 publications

References 14 publications

A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles

A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles

Matheuristic algorithms for the parallel drone scheduling traveling salesman problem

Two-Echelon Routing Problem for Parcel Delivery by Cooperated Truck and Drone

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