Integrated production and transportation scheduling in distributed 3D printing

With the maturation of emerging information technologies (Internet of Things, cloud computing, and big data), distributed manufacturing has emerged as an important model for future manufacturing. 3D printing, with its integrated molding and design freedom, is a powerful catalyst for distributed manufacturing. This paper investigates the integrated production and transportation scheduling problem in distributed 3D printing. To solve this problem, we decompose the original problem into three sub-problems and design a multilevel optimization algorithm. We employ a genetic algorithm in the outer-level optimization to determine the optimal allocation of parts to machines. In the inner-level optimization, we utilize a simulated annealing algorithm to tackle the vehicle routing problem during the transportation stage followed by a local search algorithm to address the scheduling problem encountered during the production stage. Our algorithm is validated using real data from a 3D printing company, and the results show that our algorithm can obtain solutions that are the same as or better than those of Gurobi in a reasonable time for small-sized instances. Additionally, three types of initial methods are tested on large-sized instances to verify the efficiency of the proposed algorithm, and some interesting insights are also revealed and discussed.