Heuristic Techniques for Real-Time Order Acceptance and Scheduling in Metal Additive Manufacturing
- 52 Downloads
In this research, we consider a real-time order acceptance and scheduling (OAS) problem in metal additive manufacturing (MAM) production environment, where the manufacturer with multiple machines makes decisions on the acceptance and scheduling of dynamic arriving part orders simultaneously. The objective is to maximize profit per unit time within the planning horizon. An MAM machine is a kind of batch processing machine (BPM) in which a batch of non-identical parts can be processed simultaneously as a production job according to its capacity, and the process time of the job is a function of the properties of all parts assigned to this job as well as the specifications of the MAM machine to conduct this job. This is the first time that a real-time OAS problem is considered in MAM production environment with capacity and due date constraints. We define the problem and propose a mathematical formulation. As this problem is shown to be strongly NP-hard, meta-heuristic procedures based on various selection rules are proposed for the generation of feasible schedule results. The difference of bad schedule results from those good ones is investigated first according to the results obtained with the stochastic selection. Afterwards, the performance of non-random selection rules is evaluated by comparing with the best and the worst results from the stochastic selection. Experimental tests indicate that the proposed non-random selection rules are able to provide promising schedule results without iteration.
The third author (I.K.) acknowledges the financial support received from Balikesir University—Scientific Research Projects Department under grant number BAP-2018-131.
- 3.L.E. Murr, S.M. Gaytan, D.A. Ramirez, E. Martinez, J. Hernandez, K.N. Amato, P.W. Shindo, F.R. Medina, R.B. Wicker, Metal fabrication by additive manufacturing using laser and electron beam melting technologies. J. Mater. Sci. Technol. 28, 1–14 (2012). https://doi.org/10.1016/S1005-0302(12)60016-4 CrossRefGoogle Scholar
- 8.A. Noroozi, M.M. Mazdeh, M. Heydari, M. Rasti-Barzoki, Coordinating order acceptance and integrated production-distribution scheduling with batch delivery considering Third Party Logistics distribution. J. Manuf. Syst. 46, 29–45 (2018). https://doi.org/10.1016/j.jmsy.2017.11.001 CrossRefGoogle Scholar
- 12.I. Kucukkoc, Q. Li, D.Z. Zhang, Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times, in Nineteenth International Working Seminar on Production Economics, Innsbruck, Austria, vol. 3 (2016), pp. 195–201Google Scholar
- 13.I. Kucukkoc, Q. Li, N. He, D. Zhang, Scheduling of multiple additive manufacturing and 3D printing machines to minimise maximum lateness, in: Twentieth International Working Seminar on Production Economics, Innsbruck, Austria, vol. 1 (2018), pp. 237–247Google Scholar
- 19.Q. Li, I. Kucukkoc, N. He, D. Zhang, S. Wang, Order acceptance and scheduling in metal additive manufacturing: an optimal foraging approach, in Twentieth International Working Seminar on Production Enconomics, Innsbruck, Austria, vol. 1 (2018), pp. 225–235Google Scholar
- 20.P. Jacobs, 2D Rectangle bin packing in Python (2016). Online material. https://github.com/pellejacobs/2d-rectangle-bin-packing (Accessed: 19.12.2019)