The modeling of logistics systems is performed to seek the best possible system configuration to minimize costs or maximize operational performance, in order to meet or exceed customer expectations. Classically, analytic system analysis of this type has been performed using optimization, simulation, or heuristics. However, in the past two decades, a newer class of techniques, metaheuristics, has emerged as a capable method for quickly providing near-optimal solutions for problems that exact optimization cannot solve. This article outlines recent advances in metaheuristics development, and considers the ability of these advanced techniques to resolve various logistics and supply chain problem types. Specifically, the article discusses the ant colony optimization, genetic algorithm, simulated annealing, and tabu search metaheuristics. The capabilities of these metaheuristic techniques to examine supply chain risk and disruptions, intermodal operations, customer service trade-offs, backhaul strategies, and simultaneous facility location and vehicle route problems are proposed. The article concludes by describing how faculty can bring these techniques into the classroom to ensure their students enter the logistics and supply chain field with a current and relevant understanding of the state of the art in supply chain design techniques.