Methods for job allocation in a multi machine additive manufacturing environment

Date

2016-12-01

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Abstract

According to recent studies, the importance of additive manufacturing technologies will be growing tremendously over the next years. It is estimated that the number of additive manufacturing machines used for commercial applications will rise further. The general concept of additive manufacturing is neither new nor completely undiscovered in any way. The field has been constantly developed by researchers over the last two decades. However, as additive manufacturing operations becoming a substantial part of efficient supply chains, the problem of job allocation and scheduling is likely to arise. Quite in contrast to the large amount of available research with respect to the technology itself, only little work has been conducted on surrounding factors such as production planning and scheduling. As much as additive manufacturing is a widely definable term, most technologies summarized by the term, have in common that they have inherent characteristics that make them quite different to traditional manufacturing operations. The question inevitably arises, what are those differences with respect to production planning and scheduling and how can these differences be addressed in a formal way. This research answers this question. Furthermore, it presents a scheduling and job allocation approach specifically designed for the needs of most additive manufacturing techniques. As part of this work, a scheduling process based on candidate orientations is newly developed and applied with a genetic algorithm that improves relevant performance parameters. The effectiveness is proven through a simulation study.

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Keywords

Additive manufacturing, Scheduling, Multi machine scheduling, Job allocation

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