696. Lot-Sizing and Scheduling problem in the Beverage Industry: Challenges, Classification, and Future Directions
Invited abstract in session TA-20: Applications of combinatorial optimisation in industry and services 1, stream Combinatorial Optimization.
Tuesday, 8:30-10:00Room: Esther Simpson 2.11
Authors (first author is the speaker)
| 1. | Deisemara Ferreira
|
| Department of Physics, Chemistry and Mathematics, Federal University of São Carlos | |
| 2. | Víctor Mario Noble-Ramos
|
| Department of Industrial Engineering, Universidad de Córdoba (Colombia) | |
| 3. | Douglas Alem
|
| Business School, University of Edinburgh | |
| 4. | Reinaldo Morabito
|
| Dept. of Production Engineering, Federal University of São Carlos |
Abstract
This research presents a comprehensive literature review on lot-sizing and scheduling (L&S) problem in the beverage industry, highlighting its unique challenges and production characteristics. Unlike general manufacturing, beverage production involves two interdependent stages, synchronization constraints, and perishability concerns that complicate planning and scheduling decisions. We introduce a novel classification framework that systematically categorizes L&S approaches tailored to beverage manufacturing. Our review covers over 38 studies on soft drinks, fruit-based beverages, beer, and yogurt, identifying key trends, research gaps, and future directions. We emphasize the need for addressing machine maintenance, data uncertainty, and the potential role of emerging. This study provides valuable insights for researchers and practitioners aiming to enhance production efficiency in the beverage sector.
Acknowledgments: This work was supported by the São Paulo Research Foundation (FAPESP) under Grants numbers 2023/08977–5, 2022/05803-3; the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant number 315874/2021–0; and the Coordination for the Improvement of Higher Education Personnel Brazil (CAPES) - Finance Code 001.
Keywords
- Programming, Mixed-Integer
- Industrial Optimization
Status: accepted
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