MILP models to minimise makespan in additive manufacturing machine scheduling problems

Küçükkoç, İbrahim

doi:10.1016/j.cor.2019.01.006

Cited by 89 publications

(34 citation statements)

References 24 publications

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“…Li et al [21] dealt with minimizing production costs for machines with different specifications and parts with different areas, heights, and volumes. Kucukkoc [22] derived MILPs to minimize makespan. They assumed that the processing time is proportional to volume and height.…”

Section: Literature Reviewmentioning

confidence: 99%

Part-grouping and build-scheduling with sequence-dependent setup time to minimize the makespan for non-identical parallel additive manufacturing machines

Kim

2021

Int J Adv Manuf Technol

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This paper addresses a part-grouping and build-scheduling problem for an additive manufacturing process with non-identical parallel additive manufacturing machines. Parts with the same material can be simultaneously manufactured as a build in the machine. The total areas of parts in the build cannot exceed the area capacity of the machine. Each machine has different heights in a build area. The maximum height of parts in the build is less than or equal to the height of each machine in the build area. The processing time of each build is the maximum processing time of parts belonging to the build. When the material is changed in each machine, the setup time occurs. The objective function is minimizing the makespan. To find optimal and near-optimal solutions for the problem, a mixed integer linear programming model and three meta-heuristic algorithms are presented. For small problem instances, the performance of three meta-heuristic algorithms is compared with the optimal value obtained mixed integer linear programming model. For large problem instances, the performance of three meta-heuristic algorithms is compared to each other.

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Section: Literature Reviewmentioning

confidence: 99%

Part-grouping and build-scheduling with sequence-dependent setup time to minimize the makespan for non-identical parallel additive manufacturing machines

Kim

2021

Int J Adv Manuf Technol

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“…Besides, the customer is favored by receiving the product at a lower price. Li et al (2017) and Kucukkoc, (2019) tackled the chance of integrating a comprehensive nesting procedure valid to be integrated into the LONJA3D model. The allocation of parts was solved considering their exact dimensions on the horizontal and vertical axes, rather than accounting the production area as a whole.…”

Section: Production Scheduling and Nesting Problemsmentioning

confidence: 99%

Production planning in 3D printing factories

Antón

Senovilla

González

et al. 2020

Int. J. Prod. Manag. Eng.

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<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>

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“…The authors employed the mixed-integer linear programming (MILP) model to solve their scheduling and nesting problems. Moreover, Kucukkoc et al [44] explored the same nesting and scheduling problems. The authors employed MILP models to solve the scheduling problems of single and multiple either identical or non-identical AM machines while considering minimizing the makespan.…”

Section: Related Workmentioning

confidence: 99%

Towards Reinforcing Healthcare 4.0: A Green Real-Time IIoT Scheduling and Nesting Architecture for COVID-19 Large-Scale 3D Printing Tasks

2020

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With declaring the highly transmissible COVID-19 as a pandemic, an unprecedented strain on healthcare infrastructures worldwide occurred. An enormous shortage in the personal protective equipment (PPE) and the spare parts (SP) for the mechanical ventilators ensued as a consequence of the failure of the centralized global supply chains. Additive manufacturing and Industrial Internet of Things (IIoT), as the pillars of Industry 4.0, arose as the robust noncentralized alternatives. When gathered and properly managed in the IIoT, 3D Printers (3DPs) can complement and support Healthcare 4.0 to face the current and future pandemics. Thus, this paper proposes a real-time green allocation and scheduling architecture designed and dedicated particularly for the large-scale distributed 3D printing tasks (3DPTs) of both PPE and SPs. Our proposed architecture comprises; a broker (B) and a cluster manager (CM). Dynamic status check for the 3DPs and admission control for 3DPTs are among the interconnected roles of CM. CM also performs task allocation and scheduling according to our proposed Online Ascending Load-Balancing Modified Best-Fit (OALMBF) allocation algorithm and Green Real-time Nesting Priority-Based Adaptive (GRNPA) scheduling algorithm. The performance of the proposed architecture was investigated under extremely highload environments which resulted in a success ratio and a response rate of 99.9667% and 10.9665 seconds, respectively, for the 3000 3DPTs trial. These results proved the robustness and the scalability of our architecture that surpasses its state-of-the-art counterparts. Besides respecting the real-time requirements of the 3DPTs, the proposed architecture improves the utilization of the 3DPs and guarantees an even workload distribution.

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MILP models to minimise makespan in additive manufacturing machine scheduling problems

Cited by 89 publications

References 24 publications

Part-grouping and build-scheduling with sequence-dependent setup time to minimize the makespan for non-identical parallel additive manufacturing machines

Part-grouping and build-scheduling with sequence-dependent setup time to minimize the makespan for non-identical parallel additive manufacturing machines

Production planning in 3D printing factories

Towards Reinforcing Healthcare 4.0: A Green Real-Time IIoT Scheduling and Nesting Architecture for COVID-19 Large-Scale 3D Printing Tasks

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