802 research outputs found
Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: an application to fish aggregating devices
The paper addresses the synergies from combining a heuristic method with a predictive technique to solve the Dynamic Traveling Salesman Problem (DTSP). Particularly, we build a genetic algorithm that feeds on Newton's motion equation to show how route optimization can be improved when targets are constantly moving. Our empirical evidence stems from the recovery of fish aggregating devices (FADs) by tuna vessels. Based on historical real data provided by GPS buoys attached to the FADs, we first estimate their trajectories to feed a genetic algorithm that searches for the best route considering their future locations. Our solution, which we name Genetic Algorithm based on Trajectory Prediction (GATP), shows that the distance traveled is significantly shorter than implementing other commonly used methods.European Regional Development Fund | Ref. 10SEC300036PRMinisterio de EconomĂa y Competitividad | Ref. ECO2013-45706
Southern Adventist University Undergraduate Catalog 2023-2024
Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp
Solving Travelling Thief Problems using Coordination Based Methods
A travelling thief problem (TTP) is a proxy to real-life problems such as
postal collection. TTP comprises an entanglement of a travelling salesman
problem (TSP) and a knapsack problem (KP) since items of KP are scattered over
cities of TSP, and a thief has to visit cities to collect items. In TTP, city
selection and item selection decisions need close coordination since the
thief's travelling speed depends on the knapsack's weight and the order of
visiting cities affects the order of item collection. Existing TTP solvers deal
with city selection and item selection separately, keeping decisions for one
type unchanged while dealing with the other type. This separation essentially
means very poor coordination between two types of decision. In this paper, we
first show that a simple local search based coordination approach does not work
in TTP. Then, to address the aforementioned problems, we propose a human
designed coordination heuristic that makes changes to collection plans during
exploration of cyclic tours. We further propose another human designed
coordination heuristic that explicitly exploits the cyclic tours in item
selections during collection plan exploration. Lastly, we propose a machine
learning based coordination heuristic that captures characteristics of the two
human designed coordination heuristics. Our proposed coordination based
approaches help our TTP solver significantly outperform existing
state-of-the-art TTP solvers on a set of benchmark problems. Our solver is
named Cooperation Coordination (CoCo) and its source code is available from
https://github.com/majid75/CoCoComment: expanded and revised version of arXiv:1911.0312
Undergraduate and Graduate Course Descriptions, 2023 Spring
Wright State University undergraduate and graduate course descriptions from Spring 2023
2023-2024 Undergraduate Catalog
2023-2024 undergraduate catalog for Morehead State University
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