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Product return processes are managed by reverse logistics (RL), which is a systematic way of putting recovery activities into action successfully. One of the research topics in the RL field is “outsourcing,” which has a strategic role in achievement of profitability and compatibility. It mainly includes three decisions: (1) make all operations by the firm itself, (2) all reverse logistics operations are outsourced by the firm, and (3) operations are partially outsourced. This paper proposes a multi-phased new approach that combines cloud-based design optimization (CBDO) with analytical hierarchy process (AHP) in order to select the best outsourcing alternative under uncertainties. As a novel approach, CBDO is good at dealing with high uncertainty in a computationally satisfactory way. Because the perceptions and ideas of managers can change across time, traditional multi-criteria decision-making (MCDM) techniques such as AHP are strengthened by a robust technique such as CBDO in the study. In order to verify the performance of the proposed model, an illustrative study is conducted.

A Robust Multi-Critia Decision-Making Procedure for Outsourcing Decisions in Reverse Logistics