Supply Chain Planning Using Robust Optimization

Abstract

This thesis investigates robust and distributionally robust optimization methodologies to address the dynamic decision-making challenges in supply chain networks under demand uncertainty. The primary objective is to optimize system-wide performance by centralizing decisions across all entities in the supply chain. Departing from traditional objectives such as cost minimization, this work introduces target-attainment decision criteria into robust centralized supply chain planning. This innovation equips organizations with greater flexibility to balance operational targets and resilience to demand fluctuations, thereby enhancing adaptability in uncertain environments. To explore the operation-finance interface within robust supply chain planning, this thesis incorporates a cash pooling strategy that synchronizes product and financial flows through a unified cash pool. By consolidating financial resources across the supply chain, the proposed model enhances liquidity management, reduces borrowing costs, and strengthens the overall profitability of supply chain operations. To offer a practical, data-driven decision support tool, a novel predict-then-optimize framework is proposed. This framework eliminates the need for precise support information by integrating data-driven prediction techniques with robust decision-making. Leveraging advanced statistical learning methods, the framework improves practical applicability while retaining critical theoretical guarantees, including computational tractability and asymptotic optimality. An extensive series of numerical experiments was conducted to evaluate the proposed methodologies against traditional approaches documented in the literature, including myopic policies and sample average approximation. These experiments were meticulously designed to assess cost efficiency, robustness, scalability, and adaptability to dynamic demand environments. By simulating complex supply chain networks under diverse demand distributions, varying configurations, and a wide range of parameter settings, the results provide compelling evidence of the practical efficacy and theoretical soundness of the proposed methodologies. This thesis thus advances the field of robust supply chain planning, offering both theoretical insights and practical solutions to address uncertainty in dynamic and complex supply chain environments