Production planning in 3D printing factories

Abstract

<p>Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure.</p>

Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing

“…Firstly, the chromosomes were decoded, and the task sets were divided into jobs by the lowest horizontal line method according to the sequence after decoding. After obtaining the jobs, the completion time of each job was calculated according to equation (12). Secondly, the device agent and the job agent are intelligently coordinated through the intelligent matching mechanism (IMM), and then the total processing time of the order was calculated using equation (11).…”

“…Zhou et al [ 11 ] established a task matching and scheduling model for distributed 3DP services in a cloud manufacturing environment and obtained the optimal solution by improving the genetic algorithm (GA). De Antón et al [ 12 ] used the combinatorial auction and heuristic algorithm to solve the allocation problem of 3DP parts. Rohaninejad et al [ 9 ] constructed a biobjective mathematical model targeting makespan and the total tardiness penalty and developed an efficient hybrid meta-heuristic algorithm to solve the production shop scheduling problem under heterogeneous 3D printing equipment.…”

“…Therefore, this study attempts to develop an improved genetic algorithm (IGA) to solve the 3D printing shop scheduling problem. From the perspective of optimization goals, existing studies have focused on the minimization of cost or the maximization of profit [ 4 , 8 , 10 , 12 , 19 ], minimization of maximum completion time [ 2 , 6 – 8 , 14 , 15 ], and minimization of total energy consumption [ 14 ].…”

A Multi-Part Production Planning Framework for Additive Manufacturing of Unrelated Parallel Fused Filament Fabrication 3D Printers

“…Research work has been mainly focused on nesting, 2D packing, and 3D packing problems with powder-based processes [20]. For this specific AM process, research efforts have attempted to gather multiple parts and to optimize their position for a given batch [21][22][23]. Li et al [12] have introduced two heuristics, namely, best fit and adaptive fit, for part-to-printer assignment to reduce the cost per volume by maximizing the number of parts in a single batch.…”

A Multi-part Production Planning Framework for Additive Manufacturing of Unrelated Parallel Fused Filament Fabrication 3D Printers

“…Research work has been mainly focused on nesting, 2D packing and 3D packing problems with powder-based processes [20]. For this specific AM process, research efforts have attempted to regroup multiple parts and to optimise their position for a given batch [21][22][23]. Li et al [12] have introduced two heuristics -namely best fit and adaptive fit -for part-to-printer assignment to reduce the cost per volume by maximising the number of parts in a single batch.…”