Name
Abstract | ||
|---|---|---|
Milk is a highly perishable product which needs to go through an almost perfect cold chain in a milk supply chain to maintain its highest quality. To satisfy the ever-increasing demand from dairy processors to be provided with raw milk at highest quality, transporters need to ensure the milk which is collected from farms has been stored properly before the pickup occurs; i.e., from the starting point of the production in the farm until the pickup event. To address this issue, in this paper, we have proposed a model for early detection of events in a milking cycle. Using the online data coming from IoT sensors, we detect and recognize various events in a milking cycle as close as possible to their real happening in the tank. This provides the transporter with a comprehensive, clear picture of the milk cooling performance while being stored in the farm. It also assists them in making smart decisions on pickup planning and scheduling. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IEEM.2018.8607443 | 2018 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEE IEEM) |
Keywords | Field | DocType |
Decision Support, Early detection of Events, Machine learning, Raw milk quality | Early detection,Raw milk,Scheduling (computing),Internet of Things,Decision support system,Operations research,Cold chain,Milking,Supply chain,Engineering,Operations management | Conference |
ISSN | Citations | PageRank |
2157-3611 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Atefe Zakeri | 1 | 0 | 1.69 |
| Morteza Saberi | 2 | 207 | 28.66 |
| Omar Khadeer Hussain | 3 | 406 | 56.97 |
| Elizabeth Chang | 4 | 102 | 14.51 |