| dc.description.abstract | Additive manufacturing (AM), also known as 3D printing, is a rapidly growing
technology with the potential to revolutionize the manufacturing industry. AM produces
lightweight, highly customized products in small quantities, making them suitable for
various applications. In addition, AM is viewed as a potential future technology that could
facilitate space colonization. Despite the numerous benefits of AM, obstacles must still be
addressed. The scheduling of AM machines is one of these difficulties. The scheduling
problem involves determining the order in which tasks will be processed on AM machines
to minimize a particular performance metric, such as the makespan (total processing
time). This paper addresses the scheduling problem of identical parallel machines to
minimize the makespan. After reviewing and analyzing the factors of many calculation
methods, we propose two mathematical models for optimization: a MILP model and a GA
model. The MILP model is implemented in IBM ILOG CPLEX Optimisation Studio,
whereas the genetic algorithm is implemented in MATLAB. We conduct an exhaustive
computational analysis to assess the proposed models' efficacy. The outcomes
demonstrate that the MILP model can identify the optimal solution for most problems
within the time constraint (1,800 seconds). The genetic algorithm finds high-quality
solutions in a reasonable amount of time. The proposed models and algorithms provide a
valuable contribution to the field of AM scheduling. They can be used to improve AM
production efficiency and reduce the cost of AM parts. | en_US |
