Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/104370
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Industrial and Systems Engineering | en_US |
| dc.creator | Tsang, YP | en_US |
| dc.creator | Choy, KL | en_US |
| dc.creator | Wu, CH | en_US |
| dc.creator | Ho, GTS | en_US |
| dc.creator | Lam, HY | en_US |
| dc.creator | Tang, V | en_US |
| dc.date.accessioned | 2024-02-05T08:48:39Z | - |
| dc.date.available | 2024-02-05T08:48:39Z | - |
| dc.identifier.issn | 0956-7135 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/104370 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier Ltd | en_US |
| dc.rights | © 2018 Elsevier Ltd. All rights reserved. | en_US |
| dc.rights | © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
| dc.rights | The following publication Tsang, Y. P., Choy, K. L., Wu, C. H., Ho, G. T. S., Lam, H. Y., & Tang, V. (2018). An intelligent model for assuring food quality in managing a multi-temperature food distribution centre. Food Control, 90, 81–97 is available at https://doi.org/10.1016/j.foodcont.2018.02.030. | en_US |
| dc.subject | Genetic algorithm | en_US |
| dc.subject | Internet of things | en_US |
| dc.subject | Multi-temperature food distribution | en_US |
| dc.subject | Taguchi method | en_US |
| dc.subject | Vehicle routing problem | en_US |
| dc.title | An intelligent model for assuring food quality in managing a multi-temperature food distribution centre | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.spage | 81 | en_US |
| dc.identifier.epage | 97 | en_US |
| dc.identifier.volume | 90 | en_US |
| dc.identifier.doi | 10.1016/j.foodcont.2018.02.030 | en_US |
| dcterms.abstract | In the globalized cold chain network, the effective distribution of perishable food is of utmost importance when transporting multiple types of food with different handling requirements, such as temperature and humidity, for minimizing the food spoilage rate during transportation. Currently, mismanagement of premium fruit and vegetables leads to a huge amount of capital loss such that logistics service providers (LSPs) apply refrigerated trucks to deliver them for the sake of minimizing the food spoilage rate during transportation. Since different types of food have their own different handling requirements, traditional refrigerated distribution management at a fixed environmental condition is insufficient. Without considering such requirements, traditional route planning by merely minimizing the travelling distance is ineffective in maintaining food quality, resulting in an increased likelihood of food deterioration and food chilling injury. In addition, there is a lack of real-time product monitoring to control violations of the required handling requirements in order to prevent delivery of spoilt food to customers. In this paper, an internet of things (IoT)-based route planning system (IRPS) is proposed (i) to design a multi-temperature packaging model, (ii) to develop real-time product monitoring during transportation, and (iii) to optimize routing solutions. Under the IoT framework, the ambient environmental information can be collected automatically by building a wireless sensor network so as to develop total product monitoring during the distribution process. Experiments using the Taguchi method are conducted to examine the most effective packaging model for various products in terms of maximizing duration of optimal environment conditions in tertiary packaging. By integrating the above results and travelling constraints, the optimal delivery routes can be formulated by using genetic algorithms (GAs). With the aid of IRPS, the food spoilage rate during transportation and the time needed in route planning and in the delivery of deteriorated food can be reduced, while customer satisfaction is enhanced. | en_US |
| dcterms.accessRights | open access | en_US |
| dcterms.bibliographicCitation | Food control, Aug. 2018, v. 90, p. 81-97 | en_US |
| dcterms.isPartOf | Food control | en_US |
| dcterms.issued | 2018-08 | - |
| dc.identifier.scopus | 2-s2.0-85042647514 | - |
| dc.identifier.eissn | 1873-7129 | en_US |
| dc.description.validate | 202402 bcch | en_US |
| dc.description.oa | Accepted Manuscript | en_US |
| dc.identifier.FolderNumber | ISE-0612 | - |
| dc.description.fundingSource | Self-funded | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.identifier.OPUS | 6823951 | - |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Tsang_Intelligent_Model_Assuring.pdf | Pre-Published version | 2.4 MB | Adobe PDF | View/Open |
Page views
90
Last Week
8
8
Last month
Citations as of Nov 30, 2025
Downloads
225
Citations as of Nov 30, 2025
SCOPUSTM
Citations
102
Citations as of Dec 19, 2025
WEB OF SCIENCETM
Citations
82
Citations as of Dec 18, 2025
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.



