The problem consists in finding a transshipment facilities location that maximizes the total net utility when the handling utilities at the facilities are stochastic variables, under supply, demand, and lower and upper capacity constraints. The total net utility is given by the expected total shipping utility minus the total fixed cost of the located facilities. Shipping utilities are given by a deterministic utility for shipping freight from origins to destinations via transshipment facilities plus a stochastic handling utility at the facilities, whose probability distribution is unknown. After giving the stochastic model, by means of some results of the extreme values theory, the probability distribution of the maximum stochastic utilities is derived and the expected value of the optimum of the stochastic model is found. An efficient heuristics for solving real-life instances is also given. Computational results show a very good performance of the proposed methods both in terms of accuracy and efficiency.
The capacitated transshipment location problem with stochastic handling utilities at the facilities / Tadei, Roberto; Perboli, Guido; Ricciardi, N.; Baldi, MAURO MARIA. - In: INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH. - ISSN 0969-6016. - STAMPA. - 19:6(2012), pp. 789-807. [10.1111/j.1475-3995.2012.00847.x]
The capacitated transshipment location problem with stochastic handling utilities at the facilities
TADEI, Roberto;PERBOLI, Guido;BALDI, MAURO MARIA
2012
Abstract
The problem consists in finding a transshipment facilities location that maximizes the total net utility when the handling utilities at the facilities are stochastic variables, under supply, demand, and lower and upper capacity constraints. The total net utility is given by the expected total shipping utility minus the total fixed cost of the located facilities. Shipping utilities are given by a deterministic utility for shipping freight from origins to destinations via transshipment facilities plus a stochastic handling utility at the facilities, whose probability distribution is unknown. After giving the stochastic model, by means of some results of the extreme values theory, the probability distribution of the maximum stochastic utilities is derived and the expected value of the optimum of the stochastic model is found. An efficient heuristics for solving real-life instances is also given. Computational results show a very good performance of the proposed methods both in terms of accuracy and efficiency.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2293546
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