A multi-objective stochastic model for designing a resilient supply chain network in complex product systems considering disruption risks

Abstract

This research introduces a novel mathematical model for designing a resilient supply chain network in complex product systems under uncertainty. Unlike the previous studies, the current study has considered complex product systems, supply chain network design, uncertainty, limited fortification budget, shortage, disruption risk, backup suppliers, multi-tier supplier networks, and outsourcing in an integrated mathematical model. The model applies the Chance-Constrained Programming (CCP) approach to deal with stochastic lead times. The efficiency of the model is validated by solving 36 experimental problems of different sizes. The effects of facility fortification budget, outsourcing, and the number of materials, components, and planning periods on supply chain performance are studied and some managerial implications are explored. Based on the results, when outsourcing activities are included, the total cost decreases (1.21%). Outsourcing also decreases the resilience level leading to an increase in the lead-time deviation. According to the findings, managers should prioritise the economic objective and cost-saving measures when the number of materials and components increases. By extending periods, they have to focus on more computationally efficient models. When lower cost and criticality are vital, they need to outsource more projects, but when planners seek on-time deliveries, they should produce items by internal centres.