Production planning in 3DPrinting factories

TL;DR

This paper introduces a heuristic-based method for scheduling and nesting in 3D printing factories, aiming to optimize production capacity and profitability in a managed 3D printing market.

Contribution

It presents a novel approach using combinatorial auction-inspired heuristics to improve batch creation and scheduling in 3D printing production planning.

Findings

The proposed method effectively identifies high-profit batches.

Experiments validate the approach as a promising first step.

The approach enhances production efficiency in 3D printing factories.

Abstract

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