Robust optimisation for ripple effect on reverse supply chain: an industrial case study

This study examines the ripple effect on the system performance of the reverse supply chain (RSC) network and introduces a robust optimisation model for designing strong RSC networks to cope with the uncertainties caused by the ripple effect. In this manner, to the best knowledge of authors, a robust optimisation model for RSC design against the ripple effect in the context of green principles is formulated for the first time. That being the case, the study aims to provide remarkable managerial insights thanks to the developed robust optimisation model by adopting a proactive strategy before a long-term disruption occurs in the network. To this end, the robust optimisation model is applied to an industrial case study from an enterprise disassembling the household appliance. The scope of the case study is limited to the enterprise's recycling activities in the northern region of Turkey which is a potential landslide site due to the heavy rainfall. Computational experiments are performed through a set of scenarios regarding the different weight uncertainty values to reveal the changes in objective function value and decision variables. Based on the results, whilst the computationally tractable robust solutions are obtained; the price of robustness is higher than expected to protect the constraints against violation when the probability of constraint violation equals 0.01.

Automated summary

This study explores the ripple effects on system performance in reverse supply chain networks and introduces a robust optimization model to address uncertainties caused by these ripple effects. By applying the model to an industrial case study, changes in objective functions and decision variables under different uncertainty values are revealed. While computationally tractable robust solutions are obtained, the cost of robustness may be higher than expected to protect constraints against violations.