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Multi-Objective Optimizer for Multimodal Distribution Networks: Operating Cost, Carbon Footprint and Delivery Time

Multi-Objective Optimizer for Multimodal Distribution Networks: Operating Cost, Carbon Footprint and Delivery Time

Mauro Gamberi, Marco Bortolini, Francesco Pilati, Alberto Regattieri
ISBN13: 9781466686489|ISBN10: 1466686480|EISBN13: 9781466686496
DOI: 10.4018/978-1-4666-8648-9.ch013
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MLA

Gamberi, Mauro, et al. "Multi-Objective Optimizer for Multimodal Distribution Networks: Operating Cost, Carbon Footprint and Delivery Time." Using Decision Support Systems for Transportation Planning Efficiency, edited by Ebru V. Ocalir-Akunal, IGI Global, 2016, pp. 330-370. https://doi.org/10.4018/978-1-4666-8648-9.ch013

APA

Gamberi, M., Bortolini, M., Pilati, F., & Regattieri, A. (2016). Multi-Objective Optimizer for Multimodal Distribution Networks: Operating Cost, Carbon Footprint and Delivery Time. In E. Ocalir-Akunal (Ed.), Using Decision Support Systems for Transportation Planning Efficiency (pp. 330-370). IGI Global. https://doi.org/10.4018/978-1-4666-8648-9.ch013

Chicago

Gamberi, Mauro, et al. "Multi-Objective Optimizer for Multimodal Distribution Networks: Operating Cost, Carbon Footprint and Delivery Time." In Using Decision Support Systems for Transportation Planning Efficiency, edited by Ebru V. Ocalir-Akunal, 330-370. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8648-9.ch013

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Abstract

A multi-objective optimizer Decision Support System (DSS) to minimize the operating cost, the carbon footprint and the delivery time in the design of multi-modal Distribution Networks (DNs) is presented to overcome the widely adopted methodologies focused on the cost minimization, only. The proposed approach simultaneously assesses three independent objective functions, evaluating the network costs, the Carbon Footprint (CO2 emissions) and the shipping time from the producers to the final retailers. The DSS manages multimodal four-level (three-stage) DNs, best connecting the producers to the final retailers, through a set of Distribution Centres (DCs). It allows multiple transport modes and inter-modality options looking to the most effective DN configuration from the introduced multi-objective perspective. The three optimization criteria can be considered independently or solved simultaneously, through the so-called Pareto frontier approach. Finally, the proposed DSS is validated against a case study about the delivery of Italian fresh food to several European retailers.

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