Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30821
Title: Ant Colony optimization with Heuristic Repair for the Dynamic Vehicle Routing Problem
Authors: Bonilha, Iaê S. 
Mavrovouniotis, Michalis 
Müller, Felipe M. 
Ellinas, Georgios 
Polycarpou, Marios M. 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Ant colony optimization;dynamic vehicle routing problem;heuristic repair
Issue Date: 1-Dec-2020
Source: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020Virtual, Canberra, Australia, 1 - 4 December 2020
Conference: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 
Abstract: Ant colony optimization (ACO) algorithms have proved to be suitable for solving dynamic optimization problems. The intrinsic characteristics of ACO algorithms enables them to transfer knowledge from past optimized environments via their pheromone trails to shorten the optimization process in the current environment. In this work, change-related information is also utilized when a dynamic change occurs. The dynamic vehicle routing problem is addressed where nodes are removed, representing customers that have already been visited, or added, representing customers that placed a new order and need to be visited. These change-related information are used to heuristically repair the solution of the previous environment, based on effective moves of the unstringing and stringing operator. Experimental results show that utilizing change-related information is beneficial in the generated dynamic test cases.
URI: https://hdl.handle.net/20.500.14279/30821
ISBN: 9781728125473
DOI: 10.1109/SSCI47803.2020.9308156
Rights: © IEEE
Type: Conference Papers
Affiliation : Federal University of Santa Maria 
University of Cyprus 
Federal University of Santa Maria 
Appears in Collections:Άρθρα/Articles

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