Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery
This paper presents an evolution-based hyperheuristic (EHH) for addressing the capacitated location-routing problem (CLRP) and one of its more practicable variants, namely, CLRP with simultaneous pickup and delivery (CLRPSPD), which are significant and NP-hard model in the complex logistics system....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9291434 |
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| author | Yanwei Zhao Longlong Leng Jingling Zhang Chunmiao Zhang Wanliang Wang |
| author_facet | Yanwei Zhao Longlong Leng Jingling Zhang Chunmiao Zhang Wanliang Wang |
| author_sort | Yanwei Zhao |
| collection | DOAJ |
| description | This paper presents an evolution-based hyperheuristic (EHH) for addressing the capacitated location-routing problem (CLRP) and one of its more practicable variants, namely, CLRP with simultaneous pickup and delivery (CLRPSPD), which are significant and NP-hard model in the complex logistics system. The proposed approaches manage a pool of low-level heuristics (LLH), implementing a set of simple, cheap, and knowledge-poor operators such as “shift” and “swap” to guide the search. Quantum (QS), ant (AS), and particle-inspired (PS) high-level learning strategies (HLH) are developed as evolutionary selection strategies (ESs) to improve the performance of the hyperheuristic framework. Meanwhile, random permutation (RP), tabu search (TS), and fitness rate rank-based multiarmed bandit (FRR-MAB) are also introduced as baselines for comparisons. We evaluated pairings of nine different selection strategies and four acceptance mechanisms and monitored the performance of the first four outstanding pairs in 36 pairs by solving three sets of benchmark instances from the literature. Experimental results show that the proposed approaches outperform most fine-tuned bespoke state-of-the-art approaches in the literature, and PS-AM and AS-AM perform better when compared to the rest of the pairs in terms of obtaining a good trade-off of solution quality and computing time. |
| format | Article |
| id | doaj-art-a35a73126968401ca2b4a37210f24871 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a35a73126968401ca2b4a37210f248712025-02-03T01:04:19ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/92914349291434Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and DeliveryYanwei Zhao0Longlong Leng1Jingling Zhang2Chunmiao Zhang3Wanliang Wang4Key Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, ChinaKey Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, ChinaKey Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, ChinaKey Laboratory of Special Equipment Manufacturing and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science, Zhejiang University of Technology, Hangzhou 310023, ChinaThis paper presents an evolution-based hyperheuristic (EHH) for addressing the capacitated location-routing problem (CLRP) and one of its more practicable variants, namely, CLRP with simultaneous pickup and delivery (CLRPSPD), which are significant and NP-hard model in the complex logistics system. The proposed approaches manage a pool of low-level heuristics (LLH), implementing a set of simple, cheap, and knowledge-poor operators such as “shift” and “swap” to guide the search. Quantum (QS), ant (AS), and particle-inspired (PS) high-level learning strategies (HLH) are developed as evolutionary selection strategies (ESs) to improve the performance of the hyperheuristic framework. Meanwhile, random permutation (RP), tabu search (TS), and fitness rate rank-based multiarmed bandit (FRR-MAB) are also introduced as baselines for comparisons. We evaluated pairings of nine different selection strategies and four acceptance mechanisms and monitored the performance of the first four outstanding pairs in 36 pairs by solving three sets of benchmark instances from the literature. Experimental results show that the proposed approaches outperform most fine-tuned bespoke state-of-the-art approaches in the literature, and PS-AM and AS-AM perform better when compared to the rest of the pairs in terms of obtaining a good trade-off of solution quality and computing time.http://dx.doi.org/10.1155/2020/9291434 |
| spellingShingle | Yanwei Zhao Longlong Leng Jingling Zhang Chunmiao Zhang Wanliang Wang Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery Complexity |
| title | Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery |
| title_full | Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery |
| title_fullStr | Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery |
| title_full_unstemmed | Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery |
| title_short | Evolutionary Hyperheuristics for Location-Routing Problem with Simultaneous Pickup and Delivery |
| title_sort | evolutionary hyperheuristics for location routing problem with simultaneous pickup and delivery |
| url | http://dx.doi.org/10.1155/2020/9291434 |
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