Global Value Chain Reconfiguration and Circular Economy Transitions: A Mixed-Integer Linear Programming Model

Global value chains (GVCs) generate rising volumes of electronic waste (e-waste), of which only 22.3% is formally collected and recycled, and operationalizing circular economy principles within GVCs requires reverse logistics networks that existing optimization models only partially capture. This paper develops a multi-echelon mixed-integer linear programming (MILP) model for integrated forward–reverse e-waste network design that jointly optimizes facility locations, material flows, hybrid distribution–collection co-location, and the collection price offered to consumers. Returns follow uniformly distributed consumer reservation prices, and the resulting price-dependent return mechanism is linearized exactly through a discrete price menu, yielding a fully linear formulation without big-M constants; recyclable fractions re-enter manufacturing as secondary inputs, closing the material loop. The model is evaluated on thirty randomly generated instances of three sizes, with parameter ranges anchored to the literature, solved with the open-source HiGHS solver; the largest instances solve to within 0.1% of optimality in under two minutes. Endogenizing the collection incentive raises total profit by 4.5 to 20.1% over an exogenous-return baseline and lifts material recovery from roughly 25% to 36 to 49%, while co-location adds modest, scale-dependent value and the two mechanisms show a directionally consistent but not statistically significant tendency toward substitutability (Wilcoxon signed-rank test, p > 0.05 across all size classes). These figures characterize the calibrated synthetic instances studied here and should not be read as generalizable empirical estimates. Sensitivity analyses identify consumer responsiveness to incentives, rather than waste stream quality, as the binding determinant of achievable recovery.