Robust supplier selection under uncertain costs and delivery delay times

We address the supplier selection problem under uncertainty, motivated by the current economic situation of global trade. The intense search among organisations for responsiveness in meeting market demands has directed efforts toward supply chain optimisation. Consequently, the decision regarding the best supplier choice has become vital for the success of organisations, requiring a high level of accuracy and assertiveness under complex and uncertain environments. To support decision-making in global sourcing environments, we propose a robust optimisation model that incorporates cost and time uncertainties that commonly arise in the context of worldwide raw materials supply. The model includes raw materials inventory management, preventing stockouts and violations of physical storage constraints, while considering deviations of the uncertain parameters. We analyse the behaviour of the proposed model using 324,000 scenarios generated by Monte Carlo simulations. The results show that the proposed model increases the level of robustness without significantly increasing the value of the objective function when uncertain costs and times attain their worst-case scenarios (highest deviation). On average, the objective function values increased only 3.58% in the worst case, considering 20 products, 40 periods, 60 suppliers, and an uncertainty level of 50%. [Received: 1 June 2022; Accepted: 10 May 2023]