The paper deals with the problem of minimizing reshuffling of containers in an inland intermodal terminal. The problem is tackled according to a simulation-optimization approach. A simulation model computes the operational costs of containers, related to storage and pick-up operations in an inland yard. The optimization is carried out by two genetic algorithms that work in series. The introduction of the second genetic algorithm and the concept of trust region are the original contributions of the paper to the literature. The proposed optimization method has been tested on a theoretical example of realistic size. Results highlighted that the double genetic algorithm reduces the total operational costs by 7% with respect to the single genetic algorithm.

Optimization of container operations at inland intermodal terminals / Colombaroni, Chiara; Fusco, Gaetano; Isaenko, Natalia; Quadrifoglio, Luca. - ELETTRONICO. - (2017), pp. 69-74. ((Intervento presentato al convegno 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 tenutosi a Napoli, Italia (Hotel Royal Continental) nel 2017 [10.1109/MTITS.2017.8005603].

Optimization of container operations at inland intermodal terminals

Colombaroni, Chiara
;
Fusco, Gaetano;Isaenko, Natalia;Quadrifoglio, Luca
2017

Abstract

The paper deals with the problem of minimizing reshuffling of containers in an inland intermodal terminal. The problem is tackled according to a simulation-optimization approach. A simulation model computes the operational costs of containers, related to storage and pick-up operations in an inland yard. The optimization is carried out by two genetic algorithms that work in series. The introduction of the second genetic algorithm and the concept of trust region are the original contributions of the paper to the literature. The proposed optimization method has been tested on a theoretical example of realistic size. Results highlighted that the double genetic algorithm reduces the total operational costs by 7% with respect to the single genetic algorithm.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1092214
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