Metaheuristic approaches to design and address multi-echelon sugarcane closed-loop supply chain network

The sugarcane industry is technologically pioneering in the area of food production. On the other side, this industry produces a huge amount of by-products. Proper handling of these by-products has remained a challenge. An efficient multi-echelon Sugarcane Supply Chain Network (SSCN) is designed and proposed in this paper to handle the by-products produced by the sugarcane industry that can be utilized further with little modification. It helps to reduce the overall working cost of the network. Usually, the supply chain problems are complex in nature, and complexity further increases with increasing problem instances. Metaheuristics techniques are, in general, applied to handle such NP-hard problems. This work proposes three hybrid metaheuristics algorithms, namely H-GASA, a hybrid of Genetic Algorithm with Simulated Annealing, H-KASA, a hybrid of Keshtel Algorithm with Simulated Annealing, and H-RDASA, a hybrid of Red Deer Algorithm with Simulated Annealing to handle the complexity of the problem. The algorithms’ performance is probed using the Taguchi experiments, and the best combinations of parameters are identified. This hybrid algorithms’ efficacy is compared with their basic version of the algorithms, i.e. GA, KA, RDA, and SA using different criteria. A set of test problems is generated to ensure the capability of the presented model. The obtained results suggest that H-KASA significantly outperforms in small-sized problems, while the H-RDASA significantly outperforms in medium- and large-sized problem instances. In addition, the sensitivity analysis confirms that by adopting this proposed multi-echelon SSCN, decision-makers can achieve a significant cost reduction of 8.3% in terms of the total cost.