LAPSE:2023.28241
Published Article
LAPSE:2023.28241
Energy Consumption Optimization of Milk-Run-Based In-Plant Supply Solutions: An Industry 4.0 Approach
April 11, 2023
Smart factories are equipped with Industry 4.0 technologies including smart sensors, digital twin, big data, and embedded software solutions. The application of these technologies contributes to better decision-making, and this real-time decision-making can improve the efficiency of both manufacturing and related logistics processes. In this article, the transformation of conventional milk-run-based in-plant supply solutions into a cyber−physical milk-run supply is described, where the application of Industry 4.0 technologies makes it possible to make real-time decisions regarding scheduling, routing, and resource planning. After a literature review, this paper introduces the structure of Industry 4.0 technologies supported by milk-run-based in-plant supply. A mathematical model of milk-run processes is described including both scheduling and routing problems of in-plant supply. This mathematical model makes it possible to analyze the impact of Industry 4.0 technologies on the efficiency, performance, and flexibility of in-plant supply logistics. The scenarios’ analysis validates the mathematical model and shows that significant performance improvement and energy savings can be achieved using Industry 4.0 technologies. This performance improvement can lead to a more cost-efficient and sustainable in-plant supply solution, where not only logistics aspects but also energy efficiency and emissions can be taken into consideration.
Keywords
digital twin, in-plant supply, Industry 4.0, milk-run, routing, Scheduling
Suggested Citation
Akkad MZ, Bányai T. Energy Consumption Optimization of Milk-Run-Based In-Plant Supply Solutions: An Industry 4.0 Approach. (2023). LAPSE:2023.28241
Author Affiliations
Akkad MZ: Institute of Logistics, University of Miskolc, 3515 Miskolc, Hungary [ORCID]
Bányai T: Institute of Logistics, University of Miskolc, 3515 Miskolc, Hungary [ORCID]
Journal Name
Processes
Volume
11
Issue
3
First Page
799
Year
2023
Publication Date
2023-03-07
Published Version
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr11030799, Publication Type: Journal Article
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LAPSE:2023.28241
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doi:10.3390/pr11030799
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Apr 11, 2023
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