IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v58y2020i14p4164-4181.html
   My bibliography  Save this article

Energy-efficient frozen food transports: the Refrigerated Routing Problem

Author

Listed:
  • Antonella Meneghetti
  • Sara Ceschia

Abstract

Given the growing importance of cold chains and the need to promote sustainable processes, energy efficiency in refrigerated transports is investigated at operational level. The Refrigerated Routing Problem is defined, involving multi-drop deliveries of palletised unit loads of frozen food from a central depot to clients. The objective is to select the route with minimum fuel consumption for both traction and refrigeration. The problem formulation considers speed variation due to traffic congestion phenomena, as well as decreasing load on board along the route as successive clients are visited. Transmission load for exposure of the vehicle to outdoor temperatures and infiltration load at door opening are modelled, taking into account outdoor conditions varying along the day and the year. The resulting multi-period problem is modelled and solved by means of Constraint Programming. Test scenarios come from a real local network for frozen bread dough distributed to supermarkets. Results show how fuel minimisation leads to the selection of different routes in comparison to the traditional total travel distance or time objectives. Energy savings are affected by demand distribution among the clients, departure time, number of visits per tour, seasonality and location of the delivery network.

Suggested Citation

  • Antonella Meneghetti & Sara Ceschia, 2020. "Energy-efficient frozen food transports: the Refrigerated Routing Problem," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4164-4181, July.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:14:p:4164-4181
    DOI: 10.1080/00207543.2019.1640407
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2019.1640407
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2019.1640407?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Isabel Cerrillo & Pablo Saralegui-Díez & Rubén Morilla-Romero-de-la-Osa & Manuel González de Molina & Gloria I. Guzmán, 2023. "Nutritional Analysis of the Spanish Population: A New Approach Using Public Data on Consumption," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    2. Meneghetti, Antonella & Dal Magro, Fabio & Romagnoli, Alessandro, 2021. "Renewable energy penetration in food delivery: Coupling photovoltaics with transport refrigerated units," Energy, Elsevier, vol. 232(C).
    3. Beatrice Marchi & Simone Zanoni, 2022. "Cold Chain Energy Analysis for Sustainable Food and Beverage Supply," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    4. Runfeng Yu & Lifen Yun & Chen Chen & Yuanjie Tang & Hongqiang Fan & Yi Qin, 2023. "Vehicle Routing Optimization for Vaccine Distribution Considering Reducing Energy Consumption," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    5. Angelo Maiorino & Adrián Mota-Babiloni & Fabio Petruzziello & Manuel Gesù Del Duca & Andrea Ariano & Ciro Aprea, 2022. "A Comprehensive Energy Model for an Optimal Design of a Hybrid Refrigerated Van," Energies, MDPI, vol. 15(13), pages 1-23, July.
    6. Feiyue Qiu & Guodao Zhang & Ping-Kuo Chen & Cheng Wang & Yi Pan & Xin Sheng & Dewei Kong, 2020. "A Novel Multi-Objective Model for the Cold Chain Logistics Considering Multiple Effects," Sustainability, MDPI, vol. 12(19), pages 1-28, September.
    7. Yu Song & Xi Fang, 2023. "An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery," Mathematics, MDPI, vol. 11(15), pages 1-25, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tprsxx:v:58:y:2020:i:14:p:4164-4181. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.