A survey on metaheuristics for optimization in food manufacturing industry

• A state-of-the-art survey on metaheuristics application in the food manufacturing industry is presented.
• The metaheuristics covered include local search, Simulated Annealing, Tabu search, Ant Colony, Different Evolution, Genetic Algorithms, Particle Swarm and others.
• The food manufacturing optimization covers both process and system optimization.

This paper surveys recent articles on the applications of metaheuristics for solving optimization problems in the food manufacturing industry. Metaheuristics for decision making has attracted significant research and industry attention due to the increasing complexity of models and quick decision making requirements in the industry. Metaheuristics have been applied to food processing/production technologies including fermentation, thermal drying and distillation and other system wide optimization such as transportation, storage (warehousing), production planning and scheduling. In terms of metaheuristics algorithms, Genetic Algorithm and Differential Evolution are the most popular while other algorithms have also demonstrated their effectiveness in addressing various optimization problems. Most problems were typically formulated as single objective mathematical models constructed from experimental or collected data. Recently, multi-objective optimization is becoming more popular because it is able to consider problems from several perspectives and attain more practical results.