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Title: Combining Monte Carlo simulation with heuristics to solve a rich and real-life multi-depot vehicle routing problem
Author: Alemany Giménez, Gabriel
García Sánchez, Álvaro
Armas Adrián, Jésica de
García Meizoso, Roberto
Juan Pérez, Ángel Alejandro
Ortega Mier, Miguel
Keywords: vehicle routing
Monte Carlo methods
goods distribution
order processing
statistical distributions
Issue Date: Dec-2016
Publisher: Winter Simulation Conference (WSC). Proceedings
Citation: Alemany, G., Garcia, A., De Armas, J., Garcia, R., Juan, A. & Ortega, M. (2016). Combining Monte Carlo Simulation with Heuristics to Solve a Rich and Real-life Multi-depot Vehicle Routing Problem. Winter Simulation Conference (WSC). Proceedings, 2016 (). 2466-2474. doi: 10.1109/WSC.2016.7822285
Published in: Winter Simulation Conference, Washington, DC., EUA, 11-14, desembre de 2016
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Abstract: This paper presents an optimization approach which integrates Monte Carlo simulation (MCS) within a heuristic algorithm in order to deal with a rich and real-life vehicle routing problem. A set of customers' orders must be delivered from different depots and using a heterogeneous fleet of vehicles. Also, since the capacity of the firm's depots is limited, some vehicles might need to be replenished using external tanks. The MCS component, which is based on the use of a skewed probability distribution, allows to transform a deterministic heuristic into a probabilistic procedure. The geometric distribution is used to guide the local search process during the generation of high-quality solutions. The efficiency of our approach is tested against a real-world instance. The results show that our algorithm is capable of providing noticeable savings in short computing times.
Language: English
ISBN: 9781509044863
ISSN: 1558-4305MIAR
Appears in Collections:Articles

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