Application of Nature-Inspired Multi-Objective Optimization Algorithms to Improve the Bakery Production Efficiency †

Abstract

This contribution investigates the performance of nature-inspired multi-objective optimization algorithms to reduce the makespan and oven idle time of bakery manufacturing using a hybrid no-wait flow shop scheduling model. As an example, the production data from a bakery with 40 products is investigated. We use the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) to determine the tradeoffs between the two objectives. The computational results reveal that the nature-inspired optimization algorithms provide solutions with a significant 8.7% reduction in makespan. Nonetheless, the algorithms provide solutions with a longer oven idle time to achieve the single goal of makespan minimization. This consequently elevates energy waste and production expenditure. The current study shows that an alternative Pareto optimal solution significantly reduces oven idle time while losing a marginal amount of makespan. Furthermore, the Pareto solution reduces oven idle time by 93 min by expanding the makespan by only 8 min. The proposed approach has the potential to be an influential tool for small- and medium-sized bakeries seeking economic growth and, as a result, gain in market competition.