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

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  • Tiwari, M.K.
  • Raghavendra, N.
  • Agrawal, Shubham
  • Goyal, S.K.

Abstract

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.

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  • Tiwari, M.K. & Raghavendra, N. & Agrawal, Shubham & Goyal, S.K., 2010. "A Hybrid Taguchi-Immune approach to optimize an integrated supply chain design problem with multiple shipping," European Journal of Operational Research, Elsevier, vol. 203(1), pages 95-106, May.
    • Handle: RePEc:eee:ejores:v:203:y:2010:i:1:p:95-106
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    2. Nurezayana Zainal & Azlan Mohd Zain & Nor Haizan Mohamed Radzi & Muhamad Razib Othman, 2016. "Glowworm swarm optimization (GSO) for optimization of machining parameters," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 797-804, August.
    3. Besseris, George J., 2012. "Profiling effects in industrial data mining by non-parametric DOE methods: An application on screening checkweighing systems in packaging operations," European Journal of Operational Research, Elsevier, vol. 220(1), pages 147-161.
    4. Farnaz Barzinpour & Peyman Taki, 2018. "A dual-channel network design model in a green supply chain considering pricing and transportation mode choice," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1465-1483, October.
    5. R. Jamshidi & Mir Seyyed Esfahani, 2014. "Human resources scheduling to improve the product quality according to exhaustion limit," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 1028-1041, October.

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