We address the stochastic scheduled service network design problem with quality targets and uncertainty on travel times. This important problem, raising in the tactical planning process of consolidation-based freight carriers, has been little studied up to now. We define the problem considering quality targets for on-time operation of services and delivery of demand loads to destinations. We introduce a two-stage mixed-integer stochastic model defined over a space-time network, with quality targets modeled through penalties. We also propose an effective progressive-hedging-based meta-heuristic, based on a partial-decomposition concept aiming to address the challenges raised by the presence of flow-distribution decisions in the first-stage problem and by the flow-related degeneracy particular to network design. The results of an extensive numerical experimentation emphasize the worthiness of the formulation, as well as the very good performance of the proposed meta-heuristic when compared to a well-known commercial solver. (C) 2020 Elsevier B.V. All rights reserved.

Scheduled service network design with quality targets and stochastic travel times

Lanza, G.
;
2021-01-01

Abstract

We address the stochastic scheduled service network design problem with quality targets and uncertainty on travel times. This important problem, raising in the tactical planning process of consolidation-based freight carriers, has been little studied up to now. We define the problem considering quality targets for on-time operation of services and delivery of demand loads to destinations. We introduce a two-stage mixed-integer stochastic model defined over a space-time network, with quality targets modeled through penalties. We also propose an effective progressive-hedging-based meta-heuristic, based on a partial-decomposition concept aiming to address the challenges raised by the presence of flow-distribution decisions in the first-stage problem and by the flow-related degeneracy particular to network design. The results of an extensive numerical experimentation emphasize the worthiness of the formulation, as well as the very good performance of the proposed meta-heuristic when compared to a well-known commercial solver. (C) 2020 Elsevier B.V. All rights reserved.
2021
Lanza, G.; Crainic, T. G.; Rei, W.; Ricciardi, N.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1225967
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