Robotic-cell scheduling with pick-up constraints and uncertain processing times

Tonke, Daniel; Grunow, Martin; Akkerman, Renzo

doi:10.1080/24725854.2018.1555727

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…Other researches with some similarities with the one under study are the scheduling of robotic cells and the cyclic robot scheduling problem (Buzacott and Shanthikumar, 1993). The first group of works (Dawande et al, 2005;Geismar and Pinedo, 2010;Tonke et al, 2018) study the robotic flow shop systems in which one or more stages are served by one or more robots, very similar to the one under analysis in this paper. These studies focus on the identification of the bottleneck of the process and use it as the decision point for scheduling the operations.…”

Section: State Of Artmentioning

confidence: 99%

A predictive–reactive approach for the sequencing of assembly operations in an automated assembly line

Manzini

Demeulemeester

Urgo

2022

Robotics and Computer-Integrated Manufacturing

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Section: State Of Artmentioning

confidence: 99%

A predictive–reactive approach for the sequencing of assembly operations in an automated assembly line

Manzini

Demeulemeester

Urgo

2022

Robotics and Computer-Integrated Manufacturing

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“…Equation ( 9) represents the collection of optional processing equipment for a process; Equation (10) represents the optional processing time of the process on different machines; Equation (11) represents the dynamic bottleneck state of the manufacturing system; Equation (12) is the bottleneck degree calculation formula.…”

Section: Symbol Definitionmentioning

confidence: 99%

“…Berkhout et al [9] proposed a short-term production scheduling method based on shifting production bottlenecks. Tonke et al [10] proposed a round-robin scheduling strategy to solve the resource configuration problem in multi-bottleneck systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Configuration Method of Flexible Workshop Resources Based on IICA-NS Algorithm

Zhang

Chen

et al. 2022

Processes

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The optimal configuration of flexible workshop resources is critical to production efficiency, while disturbances pose significant challenges to the effectiveness of the configuration. Therefore, this paper proposes a hybrid-driven resource dynamic configuration model and an improved Imperialist Competitive Algorithm hybrid Neighborhood Search (IICA-NS) that incorporates domain knowledge to allocate resources in flexible workshops. First, a hybrid-driven configuration framework is proposed to optimize resource configuration strategies. Then, in the revolutionary step of the Imperialist Competitive Algorithm (ICA), the bottleneck heuristic neighborhood structure is adopted to retain the excellent genes in the imperial so that the updated imperial is closer to the optimal solution; And a population invasion strategy is proposed further to improve the searchability of the ICA algorithm. Finally, the simulation experiments are carried out through production examples on flexible workshop production cases, and the proposed algorithm is applied. Compared with traditional ICA, genetic algorithm (GA), particle swarm optimization algorithm (PSO), moth-flame optimization (MFO) and sparrow search algorithm (SSA), the proposed method and algorithm effectively solve flexible workshops’ resource dynamic configuration problems.

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A review of optimization models and applications in robotic manufacturing systems: Industry 4.0 and beyond

Vaisi

2022

Decision Analytics Journal

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Robotic-cell scheduling with pick-up constraints and uncertain processing times

Cited by 8 publications

References 37 publications

A predictive–reactive approach for the sequencing of assembly operations in an automated assembly line

A predictive–reactive approach for the sequencing of assembly operations in an automated assembly line

Dynamic Configuration Method of Flexible Workshop Resources Based on IICA-NS Algorithm

A review of optimization models and applications in robotic manufacturing systems: Industry 4.0 and beyond

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