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Cited by 441

The DOI matches its Crossref record; 194 of the 194 references it cites were found in a registry or an index.

Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions

Chung, Sung Hoon, Sah, Bhawesh, Lee, Jinkun·2020·Computers & Operations Research

Abstract

Abstract This paper surveys the state-of-the-art optimization approaches in the civil application of drone operations (DO) and drone-truck combined operations (DTCO) including construction/infrastructure, agriculture, transportation/logistics, security/disaster management, entertainment/media, etc. In particular, this paper reviews ongoing research on various optimization issues related to DO and DTCO including mathematical models, solution methods, synchronization between a drone and a truck, and barriers in implementing DO and DTCO. First, the paper introduces DO and DTCO and their applications, and explores some previous works including survey papers. In addition, this paper surveys the state of the art of DO and DTCO studies and discusses the research gaps in the literature. Furthermore, the detailed review of DTCO models and solution methods are reviewed. Finally, future research directions are discussed.

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Metadata

Authors
Chung, S. H., Sah, B., & Lee, J.
Venue
Computers & Operations Research
Publisher
Elsevier BV
Published
2020-11
Volume
123
Pages
105004
ISSN
0305-0548 (ISSN-L)
OpenAlex
W3032947628
Cited by
441
References
194
FWCI
32.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions. https://app.cochat.ai/papers/10.1016/j.cor.2020.105004
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Chung, S. H., Sah, B., & Lee, J. (2020). Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions. Computers & Operations Research. https://doi.org/10.1016/j.cor.2020.105004

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.