General variable neighborhood search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. We propose a novel extension of the conventional GVNS. Our approach incorporates ideas and techniques from the field of quantum computation during the shaking phase. The 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 via the Global Positioning System (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 TSP instances, which are shown in a series of figures and tables, allow us to conclude that the novel GVNS algorithm can provide an efficient solution for this type of geographical problem.

No abstract

GVNS is a well known and widely used metaheuristic for solving efficiently many NP-Hard Combinatorial Optimization problems. In this paper, the qGVNS, which is a new quantum inspired variant of GVNS, is being introduced. This variant differs in terms of the perturbation phase because it achieves the shaking moves by adopting quantum computing principles. The functionality and efficiency of qGVNS have been tested using a comparative study (compared with the equivalent GVNS results) in selected TSPLib instances, both in first and best improvement.

No abstract

No abstract