Green Vehicle Routing Optimization Based on Dynamic Constraint Selection Co-evolutionary Algorithm Article Swipe
Lujie Zhou
,
Haifei Zhang
,
Junhao Fu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3974493/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3974493/v1
Aiming at the problems of single solution objective in the existing green vehicle routing optimization process and real-time speed change during vehicle travel, a multi-objective green vehicle routing problem with time window constraints in time-varying conditions(MOGVRPTW-TV) is established, then a co-evolutionary framework-based constrained multi-objective evolutionary algorithm for the solving of the model is proposed. First, in order to better match the actual logistics distribution, this model considers the impact of time-varying speed and capacitated variation on carbon emissions based on capacitated constraints and time window constraints. Second, a constrained multi-objective evolutionary algorithm based on the co-evolutionary framework was proposed for model solving. The algorithm treats the complete problem model as a complex task and introduces a shift crowding distance calculation that considers both individual distribution and convergence information when solving this complex task, effectively balancing the convergence and diversity of solutions. Then, a dynamic constraint selection strategy is designed for the implementation of the simple task, which only considers the effects of some constraints on the populations, and the two populations exchange information through offspring populations to achieve overall optimization. Simulations are performed on different instances and the results show that the proposed algorithm is effective in avoiding traffic congestion periods, decreasing overall distribution costs, and reducing fuel consumption and carbon emissions of vehicles.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3974493/v1
- https://www.researchsquare.com/article/rs-3974493/latest.pdf
- OA Status
- gold
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392594513
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392594513Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-3974493/v1Digital Object Identifier
- Title
-
Green Vehicle Routing Optimization Based on Dynamic Constraint Selection Co-evolutionary AlgorithmWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-08Full publication date if available
- Authors
-
Lujie Zhou, Haifei Zhang, Junhao FuList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3974493/v1Publisher landing page
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https://www.researchsquare.com/article/rs-3974493/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-3974493/latest.pdfDirect OA link when available
- Concepts
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Vehicle routing problem, Selection (genetic algorithm), Constraint (computer-aided design), Mathematical optimization, Computer science, Routing (electronic design automation), Algorithm, Evolutionary algorithm, Artificial intelligence, Mathematics, Computer network, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.solutions. | 141 |
| abstract_inverted_index.Simulations | 181 |
| abstract_inverted_index.calculation | 120 |
| abstract_inverted_index.capacitated | 74, 81 |
| abstract_inverted_index.constrained | 43, 89 |
| abstract_inverted_index.constraints | 33, 82, 164 |
| abstract_inverted_index.consumption | 209 |
| abstract_inverted_index.convergence | 127, 137 |
| abstract_inverted_index.effectively | 134 |
| abstract_inverted_index.information | 128, 173 |
| abstract_inverted_index.populations | 171, 176 |
| abstract_inverted_index.constraints. | 86 |
| abstract_inverted_index.distribution | 125, 204 |
| abstract_inverted_index.established, | 38 |
| abstract_inverted_index.evolutionary | 45, 91 |
| abstract_inverted_index.optimization | 15 |
| abstract_inverted_index.populations, | 167 |
| abstract_inverted_index.time-varying | 35, 71 |
| abstract_inverted_index.distribution, | 64 |
| abstract_inverted_index.optimization. | 180 |
| abstract_inverted_index.implementation | 152 |
| abstract_inverted_index.co-evolutionary | 41, 96 |
| abstract_inverted_index.framework-based | 42 |
| abstract_inverted_index.multi-objective | 25, 44, 90 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.conditions(MOGVRPTW-TV) | 36 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.03978222 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |