A critical review of MIP models for lot-sizing and scheduling in the beverage industry

Abstract

This paper presents a comprehensive literature review of studies that have formulated mixed-integer programming models to address lot-sizing and scheduling problems in the beverage industry. For the first time, we highlight the distinguishing characteristics and challenges of beverage production and scheduling, such as the existence of two production stages, the need for synchronization between stages, and perishability issues associated with the liquids in production tanks—issues that have previously been overlooked by specialized literature. Given the absence of a classification scheme to systematize these characteristics, we introduce a novel and extensive set of classification criteria for lot-sizing and scheduling models tailored to the beverage sector. Our review provides an up-to-date summary of over 50 mixed-integer programming models and their real-world applications in the production of soft drinks, fruit-based beverages, beer, and yogurts. We also identify gaps in the literature and suggest promising directions for future research, underscoring the importance of addressing machine maintenance and data uncertainty, as well as the potential contributions of Machine Learning & Industry 4.0 and 5.0 to enhancing lot-sizing and scheduling within the beverage industry.