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 a double genetic algorithm that applies two genetic algorithms in series. The first optimizes the locations of the containers to store in the yard and identifies the blocking containers that have to be reshuffled. The second genetic algorithm takes the solution of the first and optimizes the reshuffling of blocking containers together with the unloaded ones. The proposed optimization method has been tested on a theoretical example of a realistic size. Results highlighted that the double genetic algorithm reduces the total operational costs by 5% with respect to the single genetic algorithm.
Optimization and Simulation Approach of Containers handling operations at Intermodal Terminals / Colombaroni, Chiara; Fusco, Gaetano; Isaenko, Natalia. - In: ADVANCES IN TRANSPORTATION STUDIES. - ISSN 1824-5463. - 3:(2019), pp. 93-106.
Optimization and Simulation Approach of Containers handling operations at Intermodal Terminals
Colombaroni, Chiara
;Fusco, Gaetano;Isaenko, Natalia
2019
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 a double genetic algorithm that applies two genetic algorithms in series. The first optimizes the locations of the containers to store in the yard and identifies the blocking containers that have to be reshuffled. The second genetic algorithm takes the solution of the first and optimizes the reshuffling of blocking containers together with the unloaded ones. The proposed optimization method has been tested on a theoretical example of a realistic size. Results highlighted that the double genetic algorithm reduces the total operational costs by 5% with respect to the single genetic algorithm.File | Dimensione | Formato | |
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Note: http://www.atsinternationaljournal.com/index.php/2019-issues/special-issue-2019-vol3/1110-optimization-and-simulation-approach-of-containers-handling-operations-at-intermodal-terminals
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