Abstract
GVNS, which stands for General Variable Neighborhood Search, is an established and commonly used metaheuristic for the expeditious solution of optimization problems that belong to the NP-hard class. This paper introduces an expansion of the standard GVNS that borrows principles from quantum computing during the shaking stage. The Traveling Salesman Problem with Time Windows (TSP-TW) is a characteristic NP-hard variation in the standard Traveling Salesman Problem. One can utilize TSP-TW as the basis of Global Positioning System (GPS) modeling and routing. The focus of this work is the study of the possible advantages that the proposed unconventional GVNS may offer to the case of garbage collector trucks GPS. We provide an in-depth presentation of our method accompanied with comprehensive experimental results. The experimental information gathered on a multitude of TSP-TW cases, which are contained in a series of tables, enable us to deduce that the novel GVNS approached introduced here can serve as an effective solution for this sort of geographical problems.
Highlights
Optimization problems can be classified according to the difficulty associated with the computation of the optimal solution among all the available and feasible solutions
The experimental information gathered on a multitude of Traveling Salesman Problem with Time Windows (TSP-TW) cases, which are contained in a series of tables, enable us to deduce that the novel General VNS (GVNS) approached introduced here can serve as an effective solution for this sort of geographical problems
In the rest of the paper we describe the approach we adopted to tackle the problem by utilizing the metaheuristic Variable Neighborhood Search
Summary
Optimization problems can be classified according to the difficulty associated with the computation of the optimal solution among all the available and feasible solutions. They can be further categorized into two groups based on whether they can be formulated using variables taking values from a discrete or continuous domain. The problems associated with continuous variables are multimodal and constrained problems. The steps involved in solving optimization problems are: drawing of a diagram that depicts the scenario, assign symbols to the diagram, conduct an analysis of the diagram that relates to known and unknown variables, and use Calculus to find extreme values
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