3-D printing task scheduling for idle time minimization: An optimization algorithm using knapsack problem and artificial intelligence

In the world of evolving technologies, 3-D printing or additive manufacturing is widely recognized and used across many manufacturing industries. However, because of a lack of central management software that can work with different types of printers, managing multiple 3-D printers is challenging and inefficient. As a result, industries usually have to buy more 3-D printers in order to meet their needs. Industrial 3-D printers have a higher footprint that require more space and their cost is higher compared to personal 3-D printers. This presents a significant difficulty for major industrial businesses that have several 3-D printers spread across different departments, especially if those departments are geographically separated. For small industries, this is an even bigger challenge due to budget limitations. Since the printers are not effectively used, their idle time is higher, which requires more printers to complete the jobs on time and means manufacturing industries are not able to use the full potential of 3-D printers. This research creates a novel solution for managing multiple 3-D printers which can be scattered across different departments. The proposed program is based on Particle Swarm Optimization algorithm and Knapsack Problem. In addition, the program is user-friendly even for operators with very little experience. The operator can adjust the priority of different tasks in order of urgency based on the daily workload schedule. The program sorts 3-D printer tasks based on the order of the importance (decided by the operator) to reduce the idle time of each 3-D printer. The result shows that the proposed algorithm can solve the problem of task scheduling for different 3-D printer in reasonable time