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Open AccessArticle

Extending and Solving the Refrigerated Routing Problem

DPIA—Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, 33100 Udine, Italy
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All the authors contributed equally to this work.
Energies 2020, 13(23), 6214; https://doi.org/10.3390/en13236214
Received: 15 September 2020 / Revised: 16 November 2020 / Accepted: 20 November 2020 / Published: 26 November 2020
(This article belongs to the Special Issue Refrigeration Systems and Applications 2020)
In recent years, cold food chains have shown an impressive growth, mainly due to customers life style changes. Consequently, the transportation of refrigerated food is becoming a crucial aspect of the chain, aiming at ensuring efficiency and sustainability of the process while keeping a high level of product quality. The recently defined Refrigerated Routing Problem (RRP) consists of finding the optimal delivery tour that minimises the fuel consumption for both the traction and the refrigeration components. The total fuel consumption is related, in a complex way, to the distance travelled, the vehicle load and speed, and the outdoor temperature. All these factors depend, in turn, on the traffic and the climate conditions of the region where deliveries take place and they change during the day and the year. The original RRP has been extended to take into account also the total driving cost and to add the possibility to slow down the deliveries by allowing arbitrarily long waiting times when this is beneficial for the objective function. The new RRP is formulated and solved as both a Mixed Integer Programming and a novel Constraint Programming model. Moreover, a Local Search metaheuristic technique (namely Late Acceptance Hill Climbing), based on a combination of different neighborhood structures, is also proposed. The results obtained by the different solution methods on a set of benchmarks scenarios are compared and discussed. View Full-Text
Keywords: energy efficiency; sustainable transports; cold food chain; rich vehicle routing problem; mixed integer programming; constraint programming; local search energy efficiency; sustainable transports; cold food chain; rich vehicle routing problem; mixed integer programming; constraint programming; local search
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MDPI and ACS Style

Ceschia, S.; Di Gaspero, L.; Meneghetti, A. Extending and Solving the Refrigerated Routing Problem. Energies 2020, 13, 6214. https://doi.org/10.3390/en13236214

AMA Style

Ceschia S, Di Gaspero L, Meneghetti A. Extending and Solving the Refrigerated Routing Problem. Energies. 2020; 13(23):6214. https://doi.org/10.3390/en13236214

Chicago/Turabian Style

Ceschia, Sara; Di Gaspero, Luca; Meneghetti, Antonella. 2020. "Extending and Solving the Refrigerated Routing Problem" Energies 13, no. 23: 6214. https://doi.org/10.3390/en13236214

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