A multiobjective chance constrained programming model for supplier selection under uncertainty

Risk management is an inherent part of supplier selection. While companies are enjoying the benefits of outsourcing, risks brought by this practice should be taken into account in the process of decision making. This paper presents a multiobjective stochastic sequential supplier allocation model to help in supplier selection under uncertainty. Demand for products, capacities at suppliers as well as transportation and other variable costs are the main sources of uncertainty and are modeled using probability distributions. Disruptions are exogenous events and the model provides proactive mitigation strategies against disruptions by assigning backup suppliers who can be used in case of a default at a primary supplier. When there is no disruption, the model’s solution is an optimal supplier order assignment, considering operational risks.

Research highlights
► A stochastic multiobjective model to mitigate operational and disruption risks.
► New theoretical results for linearizing nonlinear chance constraints.
► Includes aspects of robust optimization to operational and disruption risk mitigation.
► Numerical examples to justify the use of stochastic programming in risk mitigation.