Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

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

Trachanatzi Dimitra, Rigakis Manousos, Marinaki Magdalini, Marinakis Ioannis

Full record


URI: http://purl.tuc.gr/dl/dias/41CC2AC0-D828-4157-A9A9-4B581DFDA28F
Year 2021
Type of Item Peer-Reviewed Journal Publication
License
Details
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. https://doi.org/10.1007/s12667-021-00477-1
Appears in Collections

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.

Services

Statistics