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Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach
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“To account for real-time changes in traffic conditions, various optimization algorithms such as the improved ant colony algorithm using congestion avoidance (IACA CAA ) [86], quantum approximate optimization algorithm (QAOA) [74], and strength Pareto evolutionary algorithm (SPEA) [87] are introduced.”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [86]
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Metadata
- Authors
- Liu, C., Kou, G., Zhou, X., Peng, Y., Sheng, H., & Alsaadi, F. E.
- Affiliations
- Southwestern University of Finance and Economics (CN)
- Hunan University of Technology and Business (CN)
- University of Electronic Science and Technology of China (CN)
- King Abdulaziz University (SA)
- Corresponding author
- Kou, Gang
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 188
- Pages
- 104813
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2956685526
- Cited by
- 150
- References
- 54
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 23 · 2023: 29 · 2022: 29 · 2021: 22 · 2020: 10
- FWCI
- 11.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Knowledge-Based Systems (publishedVersion)
- Funders
- Philosophy and Social Science Foundation of Hunan Province (2019JJ40147, 18YBA267, GLX235)
- National Natural Science Foundation of China (71725001, 71471149)
- Education Department of Hunan Province (17C0881, 18A297)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Vehicle Routing Optimization Methods
- Social Sciences › Transportation › Urban and Freight Transport Logistics
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- urban logistics, traffic congestion avoidance, CO2 emissions, fuel consumption, improved ant colony algorithm, distribution cost minimization, peak hour traffic
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