Development of a reliable and flexible supply chain network design model: a genetic algorithm based approach

• Published in: International journal of production research Vol. 59; no. 20; pp. 6185 - 6209
• Main Authors: Vishnu, C. R., Das, Sangeeth P., Sridharan, R., Ram Kumar, P. N., Narahari, N. S.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 18.10.2021; Taylor & Francis LLC
• Subjects: Case studies; Flexibility; fuzzy TOPSIS; genetic algorithm; Genetic algorithms; Mathematical models; Network design; Reliability; Risk management; Simulated annealing; supply chain network design; Supply chain risk management; Supply chains
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2020.1808256

Enhancing the proactive strategic capabilities to withstand the most unfavourable circumstances is always appreciated as a long-term policy rather than incident-based responses. The present research is positioned on this fundamental notion of supply chain risk management with a particular focus on strategic capabilities like reliability and flexibility that often conflict with cost. Accordingly, the authors propose a multi-objective mathematical model for designing a four-echelon supply chain that optimises cost, reliability, and volume flexibility. Interestingly, this research is the maiden effort to optimise the supply chain with these trifold objectives and herein lies the novelty as well as the challenges. Consequently, a genetic algorithm based approach is utilised as the solution methodology. To demonstrate the effectiveness of the proposed method, the small problem instances and the four-echelon problems have also been validated through exact methods and simulated annealing algorithm, respectively. A case study on a footwear supply chain involving three echelons is also presented to showcase the industrial applicability and adaptability of the proposed model. A fuzzy TOPSIS method has been adopted in the case study to incorporate the expert opinion for assigning priorities to the objectives. Supply chain professionals can leverage this methodology to establish a risk resistant supply chain.