Solving the Electric Share‐A‐Ride Problem Using a Hybrid Variable Neighborhood Search Algorithm Article Swipe
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/atr/6687585
This paper extends the share‐a‐ride problem (SARP) by incorporating electric vehicles (EVs) to reduce greenhouse gas (GHG) emissions, thus addressing environmental concerns. We introduce this new extension as the electric share‐a‐ride problem (E‐SARP). We aim to generate E‐SARP routing plans where EVs serve all passenger and parcel requests while visiting charging stations (CSs) as necessary for recharging. The objective is to maximize total profit from fulfilling passenger and parcel requests. We develop a mixed‐integer programming (MIP) model and propose a hybrid algorithm based on the variable neighborhood search (VNS) framework, integrated with a simulated annealing (SA) acceptance criterion (HVNS). The MIP model provides optimal solutions for small E‐SARP instances using the CPLEX solver, while the HVNS algorithm is designed to solve larger E‐SARP instances. Numerical experiments are conducted to assess the performance of the proposed HVNS and to provide managerial insights, demonstrating that the use of EVs can effectively address environmental concerns without significantly compromising the profitability of the transportation network.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/atr/6687585
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/atr/6687585
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409267555Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/atr/6687585Digital Object Identifier
- Title
-
Solving the Electric Share‐A‐Ride Problem Using a Hybrid Variable Neighborhood Search AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Vincent F. Yu, S. Hoang, Pham Tuan Anh, Cheng-Ta YehList of authors in order
- Landing page
-
https://doi.org/10.1155/atr/6687585Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/atr/6687585Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/atr/6687585Direct OA link when available
- Concepts
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Variable neighborhood search, Variable (mathematics), Computer science, Mathematical optimization, Algorithm, Metaheuristic, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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39Number 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.emissions, | 17 |
| abstract_inverted_index.framework, | 89 |
| abstract_inverted_index.fulfilling | 65 |
| abstract_inverted_index.greenhouse | 14 |
| abstract_inverted_index.instances. | 123 |
| abstract_inverted_index.integrated | 90 |
| abstract_inverted_index.managerial | 139 |
| abstract_inverted_index.(E‐SARP). | 32 |
| abstract_inverted_index.effectively | 148 |
| abstract_inverted_index.experiments | 125 |
| abstract_inverted_index.performance | 131 |
| abstract_inverted_index.programming | 74 |
| abstract_inverted_index.recharging. | 56 |
| abstract_inverted_index.compromising | 154 |
| abstract_inverted_index.neighborhood | 86 |
| abstract_inverted_index.demonstrating | 141 |
| abstract_inverted_index.environmental | 20, 150 |
| abstract_inverted_index.incorporating | 8 |
| abstract_inverted_index.profitability | 156 |
| abstract_inverted_index.significantly | 153 |
| abstract_inverted_index.transportation | 159 |
| abstract_inverted_index.mixed‐integer | 73 |
| abstract_inverted_index.share‐a‐ride | 4, 30 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.12249542 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |