Optimizing batch fermentation scheduling in smart breweries with artificial intelligence methods

This paper presents an artificial intelligence-based approach for optimizing batch fermentation scheduling in smart breweries. The proposed method applies a Differential Evolution (DE) algorithm to provide efficient two-stage fermentation schedules, considering tank capacity limitations, availability constraints, sequential process requirements, and an ever-changing production environment. The aim is to minimize the production makespan and ensure feasible tank allocations for each fermentation stage in a short time. Experimental results demonstrate that the proposed approach generates optimized schedules in under one second - a task that would typically take a human planner several hours of manual work - confirming its effectiveness and strong potential for real-world deployment in dynamic brewery environments.