Papalitsas, C.; Karakostas, P.; Andronikos, T.; Sioutas, S.; Giannakis, K. Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection. Preprints2017, 2017110128. https://doi.org/10.20944/preprints201711.0128.v1
APA Style
Papalitsas, C., Karakostas, P., Andronikos, T., Sioutas, S., & Giannakis, K. (2017). Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection. Preprints. https://doi.org/10.20944/preprints201711.0128.v1
Chicago/Turabian Style
Papalitsas, C., Spyros Sioutas and Konstantinos Giannakis. 2017 "Quantum Inspired General Variable Neighborhood Search (qGVNS) for Dynamic Garbage Collection" Preprints. https://doi.org/10.20944/preprints201711.0128.v1
Abstract
General Variable Neighborhood Search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. Quantum General Variable Neighborhood Search (qGVNS) is a novel, quantum inspired extension of the conventional GVNS. Its quantum nature derives from the fact that it takes advantage and incorporates tools and techniques from the field of quantum computation. Travelling Salesman Problem (TSP) is a well known NP-Hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes for Geographical Systems (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of symmetric TSP instances (symmetric in order to faithfully simulate GPS problems), which are shown in a series of figures and tables, allow us to conclude that the novel qGVNS algorithm can provide an efficient solution for this type of geographical problems.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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