A decomposition-based iterative optimization algorithm for traveling salesman problem with drone

Yurek, Emine Es; Özmutlu, H. Cenk

doi:10.1016/j.trc.2018.04.009

Cited by 194 publications

(106 citation statements)

References 7 publications

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“…Poikonen et al [40] deal with the vehicle routing problem with drones dispatching from the top of trucks. The same topic is investigated in a more recent paper by Yurek and Ozmutlu [41], focusing on minimising the combined delivery completion time. Dorling et al [15] derive and experimentally validate an energy consumption model for multirotor drones within a vehicle routing optimisation problem.…”

Section: Contextmentioning

confidence: 99%

Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities

Aurambout

Gkoumas

Ciuffo

2019

Eur. Transp. Res. Rev.

193

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Rapid technological developments in autonomous unmanned aerial vehicles (UAV or drones) and an evolving legislation may soon open the way for their large-scale implementation in the last mile delivery of products. The use of drones could drastically decrease labour costs and has been hyped as a potential disruptor to the parcel delivery industry. Online retailers and delivery companies such as Amazon, are already filing up patents for the development of multi-level fulfilment centres for unmanned aerial vehicles or "drone-beehives" that would allow the deployment of this technology within built environment. A substantial amount of research has been carried out in the last years on the potential use of drones for parcel delivery, principally in the area of logistic optimisation. However, little is known about the potential market and economic viability of such services in Europe. This paper presents a modelling framework using EU-wide high-resolution population and land-use data to estimate the potential optimal location of drone-beehives based on economic viability criterion. It estimates the potential number of EU28 citizens that could potentially benefit from last mile-drone delivery services under four scenarios. The performed analyses indicates that under the scenario considered as the most technologically realistic, up to 7% of EU citizens could get access to such services. When considering technological improvements scenarios, the share reaches 30%. Furthermore, results suggest that due to the differences in population and land-use patterns in the different Member States, the potential drone coverage across Europe could be very heterogeneous, with the UK, Germany, Italy and France appearing as the most likely countries where drone-beehives may have the most efficient development.

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Section: Contextmentioning

confidence: 99%

Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities

Aurambout

Gkoumas

Ciuffo

2019

Eur. Transp. Res. Rev.

193

View full text Add to dashboard Cite

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“…Agatz et al 2018denoted the FSTSP as Traveling Salesman Problem with Drones (TSPD), provided approximation results comparing TSPD and TSP optimal solution, and developed several route-first cluster second heuristics which vary in the initial tour generation and assignment of drone delivery nodes. Yurek and Ozmutlu (2018) solved the TSPD using a two-stage iterative decomposition approach where truck routes are determined in the first stage, and drone nodes are assigned in the second stage. Ha et al (2018) focused on the min-cost TSPD variant of Murray and Chu (2015)'s FSTSP and developed a greedy randomized adaptive search procedure which builds TSPD routes from TSP routes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The second solution procedure we developed was a decomposition based three-stage heuristic (3SH). Decomposition heuristics have been found to be useful for problems of this nature in the literature review (Kim et al, 2017;Yurek and Ozmutlu, 2018;Hong et al, 2018) and in traditional location routing problems (Wu et al, 2002;Melo et al, 2009). The final step of the 3SH procedure involves a local exchange heuristic which has been found to be efficient for complex facility location problem variants (Halper et al, 2015).…”

Section: Solution Approachmentioning

confidence: 99%

Maximum coverage capacitated facility location problem with range constrained drones

Chauhan

Unnikrishnan

Figliozzi

2019

Transportation Research Part C: Emerging Technologies

143

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Given a set of demand and potential facility locations and a set of fully available charged drones, an agency seeks to locate a pre-specified number of capacitated facilities and assign drones to the located facilities to serve the demands. The facilities serve as drone launching sites for distributing the resources. Each drone makes several one-to-one trips from the facility location to the demand points and back until the battery range is met. The planning period is short-term and therefore the recharging of drone batteries is not considered. This paper presents an integer linear programming formulation with the objective of maximizing coverage while explicitly incorporating the drone energy consumption and range constraints. The new formulation is called the Maximum Coverage Facility Location Problem with Drones or simply MCFLPD. The MCFLPD is a complex problem and even for relatively small problem sizes a state of the art MIP solver may require unacceptably long running times to find feasible solutions. Computational efficiency of MCFLPD solutions is a key factor since conditions associated with customer demands or weather conditions (e.g., wind direction and speed) may change suddenly and require a fast global reoptimization. To better balance solution quality and running times novel greedy and three-stage heuristics (3SH) are developed. The 3SH is based on decomposition and local exchange principles and involves a facility location and allocation problem, multiple knapsack subproblems, and a final local random search stage. On average the 3SH solutions are within 5% of the best Gurobi solutions but at a small fraction of the running time. Multiple scenarios are run to highlight the importance of changes in drone battery capabilities on coverage.

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“…In terms of the solution methods for TSP-D, two heuristic algorithms, "UAV First UGV Second" and "UGV First UAV Second", are designed. At present, scholars have further study the iterative algorithm with better solution quality [28], and give an accurate solution of a branch and bound based on theoretical analysis [29].…”

Section: B Nested Vehicle Routingmentioning

confidence: 99%

Commanding Cooperative UGV-UAV With Nested Vehicle Routing for Emergency Resource Delivery

et al. 2020

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Unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been widely used in delivery. In the context of the COVID-2019, in order to control the development of the epidemic, many places have adopted measures to isolate and close the area once a confirmed case is found. While reducing the contact between people, it also blocks the normal driving of vehicles. Only by changing the traditional logistics and distribution methods can customers who have been in a closed and isolated area for a long time be served. Therefore, we use the Cooperative UGV-UAV to achieve it. In this paper, when commanding cooperative UGV and UAV for emergency resource delivery, we mainly focus on two questions: how to accept the operation order (OPORD) from the commander, how to generate a nested vehicle routing planning. We first employ one intelligent task understanding module to drive the intelligent unmanned vehicles to accept and process the C-BML (Coalition Battle Management Language) formatted OPORD with 5W (what, who, where, why, when) elements. Then, we slove the nested vehicle routing planning problem as a mixed integer linear program (MILP) with the outputs of what is the UGV route, what is the UGV sortie, and how to control the customers' distribution between the UGV and the UAV. Experimental results of random instances and case study show that using the iterative improvement algorithm increase the speed rate of solving more than 10%.

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A decomposition-based iterative optimization algorithm for traveling salesman problem with drone

Cited by 194 publications

References 7 publications

Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities

Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities

Maximum coverage capacitated facility location problem with range constrained drones

Commanding Cooperative UGV-UAV With Nested Vehicle Routing for Emergency Resource Delivery

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