We consider the stochastic capacitated transshipment problem for freight transportation where an optimal location of the transshipment facilities, which minimizes the total cost, must be found. The total cost is given by the sum of the total fixed cost plus the expected minimum total flow cost, when the throughput costs of the facilities are random variables with unknown probability distribution. By applying the asymptotic approximation method derived from the Extreme Value Theory, a deterministic non-linear model, which belongs to a wide class of Entropy maximizing models, is then obtained. In this paper, we present a deep analysis of the impact of different probability distributions of the random costs on the problem optimum, as well as on the number of facilities located. The computational results show a very good performance of the deterministic model when compared to the stochastic one.

The capacitated transshipment location problem under uncertainty: a computational study / Baldi, MAURO MARIA; Ghirardi, Marco; Perboli, Guido; Tadei, Roberto. - In: PROCEDIA: SOCIAL & BEHAVIORAL SCIENCES. - ISSN 1877-0428. - 39:(2012), pp. 424-436. (Intervento presentato al convegno 7th International Conference on City Logistics tenutosi a Mallorca, Spain nel June 7-9, 2011) [10.1016/j.sbspro.2012.03.119].

The capacitated transshipment location problem under uncertainty: a computational study

Abstract

We consider the stochastic capacitated transshipment problem for freight transportation where an optimal location of the transshipment facilities, which minimizes the total cost, must be found. The total cost is given by the sum of the total fixed cost plus the expected minimum total flow cost, when the throughput costs of the facilities are random variables with unknown probability distribution. By applying the asymptotic approximation method derived from the Extreme Value Theory, a deterministic non-linear model, which belongs to a wide class of Entropy maximizing models, is then obtained. In this paper, we present a deep analysis of the impact of different probability distributions of the random costs on the problem optimum, as well as on the number of facilities located. The computational results show a very good performance of the deterministic model when compared to the stochastic one.
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/11583/2381913`