Data availability in [14] for our experiments.

Integrating Supply Chain Optimization (SCO) with Life Cycle Assessment (LCA) is essential for creating supply chains that are both economically efficient and environmentally sustainable. While SCO focuses on optimizing network structures and decisions related to product and service delivery, LCA systematically assesses the environmental impacts across the entire supply chain. The existing literature treats SCO and LCA as separate, sequential steps, often leading to inconsistencies in scope and challenges in data transfer and rescaling. Our research presents a novel Supply Chain Life Cycle Optimization (SCLCO) model that integrates SCO and LCA. Our SCLCO model is based on LCA data structures, incorporates multi-time period, closed-loop SCO decisions (e.g. reverse chain management, inventory control, network design), and is capable of considering the three pillars of sustainability: environmental, economic, and social. It includes harmonizing principles, terminology, and notation, thereby bridging the gap between the SCO and LCA communities through a generalized formulation. Computational experiments on a selected SCO model from Operations Research literature validate the SCLCO and demonstrate its effectiveness in providing valuable insights to both SCO and LCA practitioners and researchers. The results emphasize that the simultaneous execution of SCO and LCA in SCLCO minimizes the risk of overlooking decision impacts and facilitates data transfer from existing LCA databases.