Multi-Task Multi-Objective Evolutionary Search Based on Deep Reinforcement Learning for Multi-Objective Vehicle Routing Problems with Time Windows Article Swipe
Jianjun Deng
,
Junjie Wang
,
Xiaojun Wang
,
Yiqiao Cai
,
Peizhong Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/sym16081030
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/sym16081030
The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a promising solution for the VRPTW. However, the challenge of addressing the VRPTW with many conflicting objectives (MOVRPTW) still remains for DRL. The MOVRPTW considers five conflicting objectives simultaneously: minimizing the number of vehicles required, the total travel distance, the travel time of the longest route, the total waiting time for early arrivals, and the total delay time for late arrivals. To tackle the MOVRPTW, this study introduces the MTMO/DRP-AT, a multi-task multi-objective evolutionary search algorithm, by making full use of both DRL and the multitasking mechanism. In the MTMO/DRL-AT, a two-objective MOVRPTW is constructed as an assisted task, with the objectives being to minimize the total travel distance and the travel time of the longest route. Both the main task and the assisted task are simultaneously solved in a multitasking scenario. Each task is decomposed into scalar optimization subproblems, which are then solved by an attention model trained using DRL. The outputs of these trained models serve as the initial solutions for the MTMO/DRL-AT. Subsequently, the proposed algorithm incorporates knowledge transfer and multiple local search operators to further enhance the quality of these promising solutions. The simulation results on real-world benchmarks highlight the superior performance of the MTMO/DRL-AT compared to several other algorithms in solving the MOVRPTW.
Related Topics
Concepts
Human multitasking
Vehicle routing problem
Reinforcement learning
Task (project management)
Computer science
Mathematical optimization
Evolutionary algorithm
Routing (electronic design automation)
Artificial intelligence
Mathematics
Engineering
Cognitive psychology
Systems engineering
Computer network
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym16081030
- OA Status
- gold
- Cited By
- 7
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401513233
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401513233Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/sym16081030Digital Object Identifier
- Title
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Multi-Task Multi-Objective Evolutionary Search Based on Deep Reinforcement Learning for Multi-Objective Vehicle Routing Problems with Time WindowsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-08-12Full publication date if available
- Authors
-
Jianjun Deng, Junjie Wang, Xiaojun Wang, Yiqiao Cai, Peizhong LiuList of authors in order
- Landing page
-
https://doi.org/10.3390/sym16081030Publisher landing page
- 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://doi.org/10.3390/sym16081030Direct OA link when available
- Concepts
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Human multitasking, Vehicle routing problem, Reinforcement learning, Task (project management), Computer science, Mathematical optimization, Evolutionary algorithm, Routing (electronic design automation), Artificial intelligence, Mathematics, Engineering, Cognitive psychology, Systems engineering, Computer network, PsychologyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2224737250, https://openalex.org/W2262276395, https://openalex.org/W2906979112, https://openalex.org/W3213576683, https://openalex.org/W6791997802, https://openalex.org/W2018691209, https://openalex.org/W2116675171, https://openalex.org/W2888247042, https://openalex.org/W2038507294, https://openalex.org/W2092673054, https://openalex.org/W2783781089, https://openalex.org/W2948175577, https://openalex.org/W6684191040, https://openalex.org/W2963167310, https://openalex.org/W3043239066, https://openalex.org/W3200355012, https://openalex.org/W4393416373, https://openalex.org/W2413527939, https://openalex.org/W2281646010, https://openalex.org/W2998409228, https://openalex.org/W3010321770, https://openalex.org/W2009003336, https://openalex.org/W2972792265, https://openalex.org/W2935382363, https://openalex.org/W4293723280, https://openalex.org/W2040622444, https://openalex.org/W6725207838, https://openalex.org/W6748487558, https://openalex.org/W6739439883, https://openalex.org/W2805798351, https://openalex.org/W3097400948, https://openalex.org/W3112776497, https://openalex.org/W2588058706, https://openalex.org/W4281397393, https://openalex.org/W2143381319, https://openalex.org/W2024008934, https://openalex.org/W2046376809, https://openalex.org/W2126105956, https://openalex.org/W1595159159, https://openalex.org/W2101097701, https://openalex.org/W1543520236, https://openalex.org/W1558919105, https://openalex.org/W2146713522, https://openalex.org/W2049736842, https://openalex.org/W2096166399, https://openalex.org/W3138035584, https://openalex.org/W2163605009, https://openalex.org/W4295138992, https://openalex.org/W3100195331, https://openalex.org/W2647779349 |
| referenced_works_count | 50 |
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| corresponding_author_ids | https://openalex.org/A5059272149, https://openalex.org/A5101666936 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I119045251 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.9254684 |
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