A case study on using evolutionary algorithms to optimize bakery production planning

• A bakery production line with 26 stages and 40 products was modeled using MATLAB.
• Production planning of this line was optimized using PSO and ACO separately.
• The makespan and the total idle time of machines were used as cost functions.
• Significant decreases of cost functions were reached within an optimization runtime limited to 15 min.

The production of bakery goods is strictly time sensitive due to the complex biochemical processes during dough fermentation, which leads to special requirements for production planning and scheduling. Instead of mathematical methods scheduling is often completely based on the practical experience of the responsible employees in bakeries. This sometimes inconsiderate scheduling approach often leads to sub-optimal performance of companies. This paper presents the modeling of the production in bakeries as a kind of no-wait hybrid flow-shop following the definitions in Scheduling Theory, concerning the constraints and frame conditions given by the employed processes properties. Particle Swarm Optimization and Ant Colony Optimization, two widely used evolutionary algorithms for solving scheduling problems, were adapted and used to analyse and optimize the production planning of an example bakery. In combination with the created model both algorithms proved capable to provide optimized results for the scheduling operation within a predefined runtime of 15 min.