A Hybrid Taguchi–Immune approach to optimize an integrated supply chain design problem with multiple shipping

Supply chain network design is considered a strategic decision level problem that provides an optimal platform for the effective and efficient supply chain management. In this research, we have mathematically modeled an integrated supply chain design. To ensure high customer service levels, we propose the inclusion of multiple shipping/transportation options and distributed customer demands with fixed lead times into the supply chain distribution framework and formulated an integer-programming model for the five-tier supply chain design problem considered. The problem has been made additionally complex by including realistic assumptions of nonlinear transportation and inventory holding costs and the presence of economies of scale. In the light of aforementioned facts, this research proposes a novel solution methodology that amalgamates the features of Taguchi technique with Artificial Immune System (AIS) for the optimum or near optimum resolution of the problem at hand. The performance of the proposed solution methodology has been benchmarked against a set of test instances and the obtained results are compared against those obtained by Genetic Algorithm (GA), Hybrid Taguchi–Genetic Algorithm (HTGA) and AIS. Simulation results indicate that the proposed approach can not only search for optimal/near optimal solutions in large search spaces but also has good repeatability and convergence characteristics, thereby proving its superiority.