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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/41CC2AC0-D828-4157-A9A9-4B581DFDA28F | - |
| Identifier | https://doi.org/10.1007/s12667-021-00477-1 | - |
| Identifier | https://link.springer.com/article/10.1007/s12667-021-00477-1 | - |
| Language | en | - |
| Extent | 28 pages | en |
| Title | A teaching–learning-based optimization algorithm for the environmental prize-collecting vehicle routing problem | en |
| Creator | Trachanatzi Dimitra | en |
| Creator | Τραχανατζη Δημητρα | el |
| Creator | Rigakis Manousos | en |
| Creator | Ρηγακης Μανουσος | el |
| Creator | Marinaki Magdalini | en |
| Creator | Μαρινακη Μαγδαληνη | el |
| Creator | Marinakis Ioannis | en |
| Creator | Μαρινακης Ιωαννης | el |
| Publisher | Springer | en |
| Content Summary | The 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 Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2023-05-02 | - |
| Date of Publication | 2021 | - |
| Subject | Teaching–learning-based optimization algorithm | en |
| Subject | Prize-collecting vehicle routing problem | en |
| Subject | Environmental vehicle routing problem | en |
| Subject | Carbon emissions minimization | en |
| Bibliographic Citation | D. 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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