A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling

Fera, M.; Fruggiero, F.; Lambiase, A.; Macchiaroli, R.; Todisco, V.

Growing Science » International Journal of Industrial Engineering Computations » A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling

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International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print) Quarterly Publication Volume 9 Issue 4 pp. 423-438 , 2018

A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling Pages 423-438 Download PDF

Authors: M. Fera, F. Fruggiero, A. Lambiase, R. Macchiaroli, V. Todisco

DOI: 10.5267/j.ijiec.2018.1.001

Keywords: Additive Manufacturing, Scheduling, Time, Cost, Metaheuristics, Production Planning

Abstract: Additive Manufacturing (AM) is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®), is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

How to cite this paper

 Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R & Todisco, V. (2018). A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling.International Journal of Industrial Engineering Computations , 9(4), 423-438.

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