Solving Traveling Salesman Problem with Time Windows Using Hybrid Pointer Networks with Time Features Article Swipe
Majed Alharbi
,
Ahmed Stohy
,
Mohammed Elhenawy
,
Mahmoud Masoud
,
Hamiden El-Wahed Khalifa
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/su132212906
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/su132212906
This paper introduces a time efficient deep learning-based solution to the traveling salesman problem with time window (TSPTW). Our goal is to reduce the total tour length traveled by -*the agent without violating any time limitations. This will aid in decreasing the time required to supply any type of service, as well as lowering the emissions produced by automobiles, allowing our planet to recover from air pollution emissions. The proposed model is a variation of the pointer networks that has a better ability to encode the TSPTW problems. The model proposed in this paper is inspired from our previous work that introduces a hybrid context encoder and a multi attention decoder. The hybrid encoder primarily comprises the transformer encoder and the graph encoder; these encoders encode the feature vector before passing it to the attention decoder layer. The decoder consists of transformer context and graph context as well. The output attentions from the two decoders are aggregated and used to select the following step in the trip. To the best of our knowledge, our network is the first neural model that will be able to solve medium-size TSPTW problems. Moreover, we conducted sensitivity analysis to explore how the model performance changes as the time window width in the training and testing data changes. The experimental work shows that our proposed model outperforms the state-of-the-art model for TSPTW of sizes 20, 50 and 100 nodes/cities. We expect that our model will become state-of-the-art methodology for solving the TSPTW problems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su132212906
- https://www.mdpi.com/2071-1050/13/22/12906/pdf?version=1641537675
- OA Status
- gold
- Cited By
- 7
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3215179302
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3215179302Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/su132212906Digital Object Identifier
- Title
-
Solving Traveling Salesman Problem with Time Windows Using Hybrid Pointer Networks with Time FeaturesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-22Full publication date if available
- Authors
-
Majed Alharbi, Ahmed Stohy, Mohammed Elhenawy, Mahmoud Masoud, Hamiden El-Wahed KhalifaList of authors in order
- Landing page
-
https://doi.org/10.3390/su132212906Publisher landing page
- PDF URL
-
https://www.mdpi.com/2071-1050/13/22/12906/pdf?version=1641537675Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2071-1050/13/22/12906/pdf?version=1641537675Direct OA link when available
- Concepts
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Computer science, ENCODE, Encoder, Traverse, Transformer, Pointer (user interface), Artificial neural network, Real-time computing, Artificial intelligence, Engineering, Geodesy, Biochemistry, Chemistry, Operating system, Electrical engineering, Gene, Geography, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 2, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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