Supply Chain Life Cycle Optimization under Uncertainty

Abstract

For most organizations, the majority of greenhouse gas emissions are Scope 3 emissions embedded in geographically dispersed supply chains. In such settings, environmental and economic impacts, as well as operating conditions, are uncertain, and decisions are sequential, meaning that early commitments constrain later operational choices. Yet current optimization practices do not integrate a full life cycle perspective in such conditions. In response, this thesis develops a stochastic Closed-Loop Supply Chain Life Cycle Optimization (SCLCO) framework that embeds the algebraic, matrix-based structure of life cycle assessment directly within a multistage stochastic mixed-integer program. Rather than assessing impacts ex post, the model minimizes expected environmental and economic impacts through sequential decisions while representing uncertainty in supplier-dependent life cycle inventory data and economic drivers, as well as stochastic demand and return processes. The framework is illustrated using a university campus circular furniture procurement case study. Applying the stochsatic SCLCO to a framework agreement yields decision-relevant insights for supplier selection under joint environmental–economic objectives. The resulting problem is solved using Sample Average Approximation. Trade-offs are explored via weighted objectives and summarized using a Pareto frontier; robustness is assessed through optimality-gap analysis and value-of-information metrics; and outcomes are examined using contribution, uncertainty, and sensitivity analyses. Results indicate that economic outcomes are driven primarily by demand uncertainty, whereas environmental outcomes are dominated by uncertainty in manufacturing-stage inventory data. Accordingly, this work offers a decision-support framework for decarbonization efforts throughout supply chains.