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Genetic programming hyper-heuristic with vehicle collaboration for uncertain capacitated arc routing problem
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MacLachlan, Jordan, Mei, Yide, Branke, Jürgen and Zhang, Mengjie (2020) Genetic programming hyper-heuristic with vehicle collaboration for uncertain capacitated arc routing problem. Evolutionary Computation, 28 (4). pp. 563-593. doi:10.1162/evco_a_00267
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WRAP-Genetic-programming-hyper-heuristics-vehicle-arc-Branke-2019.pdf - Accepted Version - Requires a PDF viewer. Download (3609Kb) | Preview |
Official URL: https://doi.org/10.1162/evco_a_00267
Abstract
Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm.
| Item Type: | Journal Article | ||||||
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| Subjects: | H Social Sciences > HF Commerce Q Science > QA Mathematics T Technology > T Technology (General) |
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| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||
| Library of Congress Subject Headings (LCSH): | Operations research -- Mathematics, Physical distribution of goods -- Mathematics, Theory of distributions (Functional analysis) , Mathematical optimization | ||||||
| Journal or Publication Title: | Evolutionary Computation | ||||||
| Publisher: | M I T Press | ||||||
| ISSN: | 1063-6560 | ||||||
| Book Title: | Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO '19 | ||||||
| Official Date: | 2 December 2020 | ||||||
| Dates: |
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| Volume: | 28 | ||||||
| Number: | 4 | ||||||
| Page Range: | pp. 563-593 | ||||||
| DOI: | 10.1162/evco_a_00267 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | This is the author’s final version, the article has been accepted for publication in Evolutionary Computation | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
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