Abstract
<jats:p>This chapter analyzes the use of Multi-Objective Optimization (MOO) methods in designing and controlling smart and sustainable supply chains. Contemporary supply chains are under more pressure than ever before to balance cost cutting, service performance, and environmental and societal effects. MOO is a strong tool for coping with the trade-offs between such disparate objectives. The research utilizes advanced techniques such as NSGA-II, Particle Swarm Optimization (PSO), and Differential Evolution (DE) to demonstrate how decision-makers can achieve optimal solutions in multiple areas simultaneously, such as lowering costs, reducing carbon footprints, shortening delivery times, and improving service reliability. Realistic examples from city logistics and circular production illustrate how these methods function in dynamic and uncertain settings. The study also stresses the need to connect optimization results with ESG reporting practices and the United Nations' Sustainable Development Goals (SDGs).</jats:p>