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Inverting the Truck-Drone Network Problem to Find Best Case Configuration

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  • Robert Rich

Abstract

Many industries are looking for ways to economically use truck/rail/ship fitted with drone technologies to augment the “last mile” delivery effort. While drone technologies abound, few, if any studies look at the proper configuration of the drone based on significant features of the problem: delivery density, operating area, drone range, and speed. Here, we first present the truck-drone problem and then invert the network routing problem such that the best case drone speed and range are fitted to the truck for a given scenario based on the network delivery density. By inverting the problem, a business can quickly determine the drone configuration (proper drone range and speed) necessary to optimize the delivery system. Additionally, we provide a more usable version of the truck-drone routing problem as a mixed integer program that can be easily adopted with standardized software used to solve linear programming. Furthermore, our computational metaheuristics and experiments conducted in support of this work are available for download. The metaheuristics used herein surpass current best-in-class algorithms found in literature.

Suggested Citation

  • Robert Rich, 2020. "Inverting the Truck-Drone Network Problem to Find Best Case Configuration," Advances in Operations Research, Hindawi, vol. 2020, pages 1-10, January.
    • Handle: RePEc:hin:jnlaor:4053983
    DOI: 10.1155/2020/4053983
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    Cited by:

    1. Büyüközkan, Gülçin & Ilıcak, Öykü, 2022. "Smart urban logistics: Literature review and future directions," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).

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