Flexible and Sustainable Bread Production in SMEs: A Metaheuristic Scheduling Approach

Abstract

Small and medium-sized bakery enterprises (SMEs) often face inefficiencies due to manual scheduling and limited automation. These challenges are most evident in coordinating multiple interdependent production stages such as fermentation, proofing, and baking. A common problem is suboptimal oven usage—long idle periods between batches lead to excessive energy consumption and environmental burden. This paper introduces an intelligent scheduling approach that combines agent-based modeling with metaheuristic optimization. Bakery production is modeled as a no-wait flexible flow process, and the system applies bio-inspired algorithms, specifically the Whale Optimization Algorithm (WOA) and Firefly Algorithm (FA),  to generate optimized daily schedules. The approach adapts to real-world operational constraints, enabling dynamic scheduling across diverse bread types with differing production times, such as sourdough and brioche.

Simulation experiments based on realistic bakery production scenarios show that the proposed scheduling approach can shorten total production time and reduce oven idle periods. The system supports both productivity and environmental sustainability by enabling SMEs to better manage time in energy-intensive stages of production, such as baking. This research contributes to the UN Sustainable Development Goals, particularly SDG 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production), by proposing an accessible digital approach to support SME transformation toward smarter and greener food manufacturing.

Keywords: Metaheuristic Optimization, Agent-Based Scheduling, Bakery Production, Sustainable Manufacturing, SME Production Efficiency