Artificial Intelligence overview for optimizing production scheduling in a picture framing company
Artificial Intelligence is significantly transforming manufacturing and impacting warehouse planning, production, operations, and logistics. This article explores AI applications in these areas, analyzing its role in optimizing scheduling and comparing established methods for efficiency. In warehous...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2025-01-01
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| Series: | Technical Transactions |
| Subjects: | |
| Online Access: | https://doi.org/10.37705/TechTrans/e2025006 |
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| Summary: | Artificial Intelligence is significantly transforming manufacturing and impacting warehouse planning, production, operations, and logistics. This article explores AI applications in these areas, analyzing its role in optimizing scheduling and comparing established methods for efficiency. In warehouse planning, AI optimizes inventory placement, automates processes, and enables predictive maintenance. In production, it improves scheduling, predicts machine failures, and personalizes products. AI enhances operations by analyzing sensor data to predict failures and schedule maintenance. In logistics, it optimizes transport routes, automates shipping, and manages inventory. The study examines scheduling using priority rules, complete search, and genetic algorithms. These methods were tested on data from a picture framing company with make-to-order production. Scheduling quality was measured by minimizing makespan and comparing computational efficiency. The genetic algorithm outperformed complete search, scheduling 70 orders on 10 machines in just over 500 ms, significantly reducing computational time. This efficiency is crucial for larger problems where complete search becomes impractical. The findings highlight AI’s potential to improve scheduling in manufacturing, making it a valuable tool for complex production. AI-driven solutions can enhance efficiency across industries, providing policymakers with a pathway to support advanced manufacturing technologies. |
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| ISSN: | 2353-737X |