Data-driven globalized distributionally robust multi-period location-routing-scheduling model for waste-to-energy supply chain under emissions ambiguity

The intensification of global energy shortages and continuous expansion of municipal solid waste require effectively optimizing the waste-to-energy supply chain (WtESC). When the distribution information of uncertain parameters is partially known, WtESC often faces complex and ambiguous challenges. To address this, we construct data-driven inner and outer ambiguity sets based on real data and utilize globalized distributionally robust (GDR) optimization framework to handle uncertainty. Compared with classical distributionally robust optimization, it allows for controllable violations of constraints in the outer ambiguity set. A data-driven globalized distributionally robust WtESC (GDR-WtESC) model is developed, and transformed into an equivalent mixed integer linear programming model according to duality theory. The computational results of real case indicate that $\left(i\right)$ There is a conflict between economic and environmental objectives, and decision-makers can prioritize them based on their own preferences. $\left(ii\right)$ The tolerance level for constraint violation has a positive impact on the total cost. Specifically, the increase of tolerance level from 0.1 to 0.9 can reduce the optimal cost by 1.07%. $\left(iii\right)$ The optimal decision of GDR-WtESC model has strong stability and high quality. Compared with the sample average approximation (SAA) model, the variance of the objective value in out of sample experiments decreases by 88.28% on average, and the average cost decreases by 0.55%. The SAA method can address the uncertainty, but cannot handle constraint violations in realistic. Thus, for decision makers who are sensitive to distributional ambiguity, the GDR method is recommended for WtESC problem, because it enhances the robustness and reduces conservatism.