GRASP: an application to efficiently plan the low carbon emission distributed additive manufacturing

Abstract GRASP is a well-established metaheuristic algorithm that efficiently designs optimized solutions for complex problems. It has achieved notable results in scientific literature, particularly when addressing scenarios with many intricacies, where optimal solutions can be difficult to achieve in short computational times. This is often the case for challenging optimization problems aiming to foster sustainable practices. Our paper discusses the basic components of a GRASP and some of the most notable improvement strategies, while presenting an implementation that is specifically tailored to plan a sustainable framework for distributed additive manufacturing. The problem we address is planning a production schedule for a set of additively manufactured parts required by customers, followed by their subsequent shipment from the fabrication plants to the customers’ location. A comparison between GRASP and CPLEX showed that GRASP can obtain optimal or high-quality solutions while significantly reducing computational times.