Multi-period green reverse logistics network design: An improved Benders-decomposition-based heuristic approach

There has been extensive academic research on the optimisation of reverse logistics (RL) and closed-loop supply chain (CLSC) network design. However, existing literature is lacking in several features practical relevance, simultaneous consideration dynamic characteristics, including multi-period setting, inventory factors, environmental footprints, scalability application. This shortcoming primarily due to challenges associated with computation complexity, mathematical formulation, need for a faster solution method solve such large-scale problems real-time. In this research, we address these investigate multi-facility green RL design problem, integrating carbon footprint vehicle selection, entailing allocation between facilities setting incorporate characteristics. We formulate mixed-integer linear programming (MILP) model minimise total cost, comprising emission cost transport production at facilities. also effects emissions choice fleet network's structure. The novelty our lies development application an exact method, namely “Improved Benders Decomposition (IBD)” algorithmic enhancements, strengthened master valid inequalities, heuristic, multi-stage strategy accelerate convergence decomposition method. By combining elements, proposed IBD solves MILP model, provides methodology improved bounds, addresses inherent intractability problem. apply heuristic set 12 problem configurations under distinct scenarios. show that outperforms traditional methods terms quality, computational time, robustness.