Robot task sequencing for a flexible assembly system with 3D printers

Kim, Hyun-Jung; Lee, Jun-Ho

doi:10.1109/codit.2017.8102557

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Along with the integration of transportation, another aspect representing a problem extension is the joint scheduling of the Material Handling Systems to load and unload the AM resources. The papers by Kim and Lee (2017) and Kim and Lee (2021) address this problem in order to minimize the cycle time using Petri nets.…”

Section: Problem Extensionsmentioning

confidence: 99%

An overview on the use of operations research in additive manufacturing

Framiñán

Fernández-Viagas

2022

Ann Oper Res

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Additive Manufacturing (AM) is a disruptive production technology that challenges many well-established, conventional practices in Operations and Supply Chain Management decisions. Consequently, new context-specific decision problems have appeared in this area, while existing decision problems must be adapted or extended to this context. In this overview, these decision problems in AM are highlighted and classified, describing the different decision in an unified manner and citing the underlying OR techniques that have been applied to solve them. The aim of the paper is that, by presenting an overview of the AM-related problems in a systematic, OR- or Decision-Making-centred (rather than technology-centred) manner, the OR community can become more aware of this stream of research and thus be more active and contribute with some high-quality work. Open research challenges, as well as avenues for future research are also discussed.

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Section: Problem Extensionsmentioning

confidence: 99%

An overview on the use of operations research in additive manufacturing

Framiñán

Fernández-Viagas

2022

Ann Oper Res

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“…To demonstrate the position-based crossover operating, an example of schematic diagram is given in Figure 1. As shown in the figure, in order to generate offspring P o1 and P o2 from the parent frogs P p1 and P p2 , firstly, the genes of [g2, g4, g5] in P p1 are selected randomly with their positions [2,4,5], and so the unselected genes in P p1 are [g1, g3, g6] with their positions [1,3,6]. According to the selected [g2, g4, g5] in P p1 , we find the same [g2, g4, g5] with their positions [2,3,5] in P p2 , and then the unselected positions are [1,4,6] with genes [g3, g6, g1] in P p2 ; we crossover the corresponding genes in unselected [g1, g3, g6] of P p1 and unselected [g3, g6, g1] of P p2 , and then we obtain the offspring P o1 and P o2 .…”

Section: Position-based Crossovermentioning

confidence: 99%

“…With the advancement of Industry 4.0, the demographicdividend is gradually replaced by the technology-dividend [1,2]. In the flowshop, designing an intelligent scheduling algorithm can effectively improve manufacturing systems and production efficiency of enterprises.…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

Yang

Chen

et al. 2020

Complexity

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Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution. To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step. The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed. Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively.

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Cyclic robot scheduling for 3D printer-based flexible assembly systems

Kim

Lee

2018

Ann Oper Res

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Robot task sequencing for a flexible assembly system with 3D printers

Cited by 5 publications

References 26 publications

An overview on the use of operations research in additive manufacturing

An overview on the use of operations research in additive manufacturing

An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

Cyclic robot scheduling for 3D printer-based flexible assembly systems

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