Feedback-Based Deterministic Optimization Is a Robust Approach for Supply Chain Management under Demand Uncertainty

Abstract

Optimization-based inventory and supply chain management (SCM) under uncertainty can provide organizations a significant competitive advantage. Implementing optimization under uncertainty using stochastic or robust formulations requires accurate characterization of uncertain parameters (e.g., customer demand, production and transportation lead times) and can be computationally demanding. On the other hand, online deterministic optimization addresses uncertainty via a periodic recalculation of SCM decisions based on feedback of updated system information. In this work, we carry out an extensive comparative numerical study to analyze and contrast the performance of online and offline algorithms for several deterministic and uncertainty-aware inventory production and distribution planning formulations under uncertain customer demand in the context of multiechelon, multiperiod supply chains. We demonstrate empirically that using online deterministic optimization rooted in feedback control has significant computational and economic performance advantages over stochastic and robust optimization formulations.

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