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A teaching–learning-based optimization algorithm for the environmental prize-collecting vehicle routing problem

Trachanatzi Dimitra, Rigakis Manousos, Marinaki Magdalini, Marinakis Ioannis

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URIhttp://purl.tuc.gr/dl/dias/41CC2AC0-D828-4157-A9A9-4B581DFDA28F-
Identifierhttps://doi.org/10.1007/s12667-021-00477-1-
Identifierhttps://link.springer.com/article/10.1007/s12667-021-00477-1-
Languageen-
Extent28 pagesen
TitleA teaching–learning-based optimization algorithm for the environmental prize-collecting vehicle routing problemen
CreatorTrachanatzi Dimitraen
CreatorΤραχανατζη Δημητραel
CreatorRigakis Manousosen
CreatorΡηγακης Μανουσοςel
CreatorMarinaki Magdalinien
CreatorΜαρινακη Μαγδαληνηel
CreatorMarinakis Ioannisen
CreatorΜαρινακης Ιωαννηςel
PublisherSpringeren
Content SummaryThe present research proposes a new Vehicle Routing Problem (VRP) variant, the Environmental Prize-Collecting Vehicle Routing Problem (E-PCVRP). According to the original PCVRP formulation, the scope of the problem is to maximize the total collected prize from the visited nodes and simultaneously minimize the fixed vehicle-utilization cost and the variable cost. In the E-PCVRP formulation, the variable cost is not solely expressed as a vehicle-covered distance but as a load-distance function for CO2 emissions minimization. The Teaching–Learning-Based Optimization (TLBO) algorithm is selected as the solution approach. However, TLBO is designed to address continuous optimization problems, while the solution of the E-PCVRP requires a discrete-numbered representation. Thus, a heuristic encoding/decoding technique is proposed to map the solution in a continuous domain, i.e., the Cartesian space, and transform it back to the original form after applying the learning mechanisms, utilizing the Euclidean Distance. The encoding/decoding process is denoted as CRE, and it has been incorporated into the standard TLBO algorithmic scheme, and as such, the proposed TLBO-CRE algorithmic solution approach emerges. The effectiveness of the TLBO-CRE is demonstrated over computational experiments and statistical analysis in comparison to the performance of other bio-inspired algorithms and a mathematical solver.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-02-
Date of Publication2021-
SubjectTeaching–learning-based optimization algorithmen
SubjectPrize-collecting vehicle routing problemen
SubjectEnvironmental vehicle routing problemen
SubjectCarbon emissions minimizationen
Bibliographic CitationD. Trachanatzi, M. Rigakis, M. Marinaki and Y. Marinakis, “A teaching–learning-based optimization algorithm for the environmental prize-collecting vehicle routing problem,” Energy Syst., Aug. 2021, doi: 10.1007/s12667-021-00477-1.en

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