<p>In the dynamic landscape of contemporary business, the imperative to optimize supply chain operations has become increasingly pronounced. The Closed-Loop Supply Chain (CLSC) framework stands out as a formidable approach for achieving operational excellence and generating economic impact. This paper addresses the pivotal role of transportation within supply chain network design, emphasizing cost reduction as a paramount objective. Presented herein is a pioneering CLSC model that incorporates the pivotal element of fixed-cost solid transportation. By integrating this transfer type into the network, the model offers a more authentic representation of the intricacies inherent in supply chain operations. To grapple with the inherent complexity of the problem, a fusion of basic and hybrid metaheuristic algorithms is meticulously developed. The Taguchi method is enlisted to discern optimal operators and parameter values for each algorithm, facilitating a comparative evaluation of their performance. Findings underscore the Particle Swarm Optimization (PSO) algorithm’s superiority in effectively addressing the NP-Hard problem associated with the CLSC model incorporating fixed-cost solid transportation. This research makes a substantial contribution to the optimization landscape of CLSC, particularly in the domain of transportation. By illuminating the efficacy of metaheuristic algorithms, organizations can glean invaluable insights to enhance their supply chain performance. The study’s exclusive focus on CLSC optimization, specifically in the realm of transportation, offers actionable insights for organizations striving to elevate their supply chain efficiency.</p>