The sustainable supply chains optimization is a high-dimensional multi-objective optimization problem. The involved costs can be categorized as economic, environmental, and social. Metaheuristics can be used for tackling this kind of problem efficiently. This short note deals with a comparative analysis of the main metaheuristics (according to recent surveys) for a sustainable supply chain model. The considered techniques are genetic algorithm, particle swarm optimization, simulated annealing, and non-dominated sorted genetic algorithm. Moreover, two hybrid models are also included, i.e. genetic algorithm combined with simulated annealing and genetic algorithm combined with particle swarm optimization. The latter provides the best result with a constraint satisfaction rate equal to 0.9968.
Performance assessment of the main metaheuristics for sustainable supply chains
Tomasiello S.
2024-01-01
Abstract
The sustainable supply chains optimization is a high-dimensional multi-objective optimization problem. The involved costs can be categorized as economic, environmental, and social. Metaheuristics can be used for tackling this kind of problem efficiently. This short note deals with a comparative analysis of the main metaheuristics (according to recent surveys) for a sustainable supply chain model. The considered techniques are genetic algorithm, particle swarm optimization, simulated annealing, and non-dominated sorted genetic algorithm. Moreover, two hybrid models are also included, i.e. genetic algorithm combined with simulated annealing and genetic algorithm combined with particle swarm optimization. The latter provides the best result with a constraint satisfaction rate equal to 0.9968.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
