Quality-aaware modeling and optimal scheduling for perishable good distribution networks: The case of banana logistics

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

Modern technologies have enabled approaches to estimate freshness of perishable products during production and distribution. Nevertheless, the loss of perishable goods is still high due to the deteriorating nature and inefficiencies in supply chains. This research focuses on improving the scheduling of banana logistics using real-time quality information. Bananas are typically shipped from tropical production sites to other places in the world. With temperature controlled reefer containers and sensor technologies, bananas can be monitored during transport and situations like early ripening can be predicted. In order to minimize spoilage, we propose a mathematical model for scheduling logistics activities with the consideration of both the biological process and the logistics procedure of bananas. Results of simulation experiments indicate that the method could reduce spoilage using real-time monitoring and scheduling.
Original languageEnglish
Title of host publicationComputational Logistics
Subtitle of host publicationProceedings 8th International Conference (ICCL 2017)
EditorsTolga Bektaş, Stefano Coniglio, Antonio Martinez-Sykora, Stefan Voß
Place of PublicationCham, Switzerland
PublisherSpringer
Pages483-497
ISBN (Electronic)978-3-319-68496-3
ISBN (Print)978-3-319-68495-6
DOIs
Publication statusPublished - 2017
EventICCL 2017: 8th International Conference on Computational Logistics - Southampton, United Kingdom
Duration: 18 Oct 201720 Oct 2017

Publication series

NameLecture Notes in Computer Science
Volume10572

Conference

ConferenceICCL 2017: 8th International Conference on Computational Logistics
CountryUnited Kingdom
CitySouthampton
Period18/10/1720/10/17

Keywords

  • Perishable goods logistics
  • Banana supply chain
  • Green-life
  • Ripeness
  • Quality-aware modeling

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