City Logistics has attracted considerable interest from the operations research and logistics communities during past decades. It resulted in a broad variety of promising approaches from different fields of combinatorial optimisation. However, research on urban freight transportation is currently slowing down due to two different lacks, limiting the exploratory capacity and compromise the technology transfer to the industry. First, the majority of the instances in the literature are based on the generalisation of classical instances, often not created for urban applications, or on artificial data, i.e. data not coming from any historical or empirical datasets. Thus, the validation of models and methods becomes more difficult, being the results not directly compared with real or realistic settings. Second, even when some data sources become available, there is no standard way to mixing data gathered from different sources and, from them, generate new instances for urban applications. This study aims to overcome these issues, proposing a simulation–optimisation framework for building instances and assess operational settings. To illustrate the usefulness of the framework, the authors conduct a case study, in order to evaluate the impact of multimodal delivery options to face the demand from e-commerce, in an urban context as Turin (Italy).

Simulation–optimisation framework for City Logistics: an application on multimodal last-mile delivery / Perboli, Guido; Rosano, Mariangela; Saint-Guillain, Michael; Rizzo, Pietro. - In: IET INTELLIGENT TRANSPORT SYSTEMS. - ISSN 1751-956X. - 12:4(2018), pp. 262-269. [10.1049/iet-its.2017.0357]

Simulation–optimisation framework for City Logistics: an application on multimodal last-mile delivery

Perboli, Guido;Rosano, Mariangela;
2018

Abstract

City Logistics has attracted considerable interest from the operations research and logistics communities during past decades. It resulted in a broad variety of promising approaches from different fields of combinatorial optimisation. However, research on urban freight transportation is currently slowing down due to two different lacks, limiting the exploratory capacity and compromise the technology transfer to the industry. First, the majority of the instances in the literature are based on the generalisation of classical instances, often not created for urban applications, or on artificial data, i.e. data not coming from any historical or empirical datasets. Thus, the validation of models and methods becomes more difficult, being the results not directly compared with real or realistic settings. Second, even when some data sources become available, there is no standard way to mixing data gathered from different sources and, from them, generate new instances for urban applications. This study aims to overcome these issues, proposing a simulation–optimisation framework for building instances and assess operational settings. To illustrate the usefulness of the framework, the authors conduct a case study, in order to evaluate the impact of multimodal delivery options to face the demand from e-commerce, in an urban context as Turin (Italy).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2706093
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