CONCEPT OF SUPERVISED MACHINE LEARNING ALGORITHMS BASED ON THE K-NEAREST NEIGHBORS METHOD FOR LOAD BALANCING ON PHARMACEUTICAL SORTING LINES
DOI:
https://doi.org/10.26906/SUNZ.2024.1.028Keywords:
Industry 40, Smart Manufacturing, Logistics 40, Warehousing 40, Internet of Things, Lean Production, Load Balancing, Sorting Conveyor Line, Pharmaceutical ProductionAbstract
This article presents a study on the concept of supervised machine learning algorithms based on the k-nearest neighbors method to optimize the load balancing process on pharmaceutical sorting lines. The pharmaceutical industry faces constantly changing loads and diverse packaging characteristics, requiring efficient strategies for managing production processes. The author proposes a concept based on the nearest neighbors principle to determine the optimal load for each sorting zone. The use of this method allows considering the context and similarity of sorting zones, leading to accurate and adaptive solutions. Sorting robots operating on pharmaceutical conveyor lines become more efficient in distributing packages across all working zones, resulting in reduced order assembly time and resource optimization.Downloads
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