Effects of Non-Homogeneous Learning on the Performance of Serial Production Systems - A Simulation Study

Ranasinghe, Thilini; Senanayake, C.D.; Perera, K.

doi:10.1109/mercon.2018.8421995

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it does not become clear whether the simulation covers periods that allow to map the change of the age of workers and the models are combined in an unspecified way to determine the output. Ranasinghe et al (2018) examine the effects of non-homogeneous learning on the performance in serial production. The authors use the power learning curve model by Dar-El (2000) to calculate the processing time.…”

Section: Introductionmentioning

confidence: 99%

Extending Simulation-Based Assembly Planning to Include Human Learning and Previous Experience: A Simulation Study

Duisberg,

Kranz,

Nitsch

et al. 2023

New Trends in Computer Sciences

View full text Add to dashboard Cite

When using simulation-based assembly planning in the planning phase of designing modern assembly systems, the prospective system behavior should be predicted as reliably as possible by the simulation. For this purpose, personnel-related adjustment periods, such as those related to learning through task repetition should be considered in the simulation model, if employees are later to be involved in the assembly. The learning effect influences the overall performance of the system and can be described by learning curves. The aim of the approach presented in this paper is to increase the prediction quality of simulation models for assembly planning by taking into account the previous experience of the employees. For this purpose, a learning model is integrated into a discrete-event simulation and subsequently verified. The learning model includes the personnel-related learning curve as well as the previous experience of the employees as dynamic parameters. Simulation experiments with three forms of assembly organization were conducted to investigate the influence of learning and previous experience on the dynamic system behavior of an assembly system. The results indicate that assembly systems organized according to the One Piece Flow principle allow for broader, albeit slower, learning compared to row and group assembly.

show abstract

Section: Introductionmentioning

confidence: 99%

Extending Simulation-Based Assembly Planning to Include Human Learning and Previous Experience: A Simulation Study

Duisberg,

Kranz,

Nitsch

et al. 2023

New Trends in Computer Sciences

View full text Add to dashboard Cite

show abstract

“…Also, the flow line is studied under the learning and forgetting effect (Bai et al, 2018;Gao et al, 2018). The simulation is used to investigate the effect of non-homogeneous learning rate on the performance of serial production stages (Ranasinghe et al, 2018). The assembly flow shop line under learning effect is studied in order to minimize the makespan, total completion time and total quadratic completion time.…”

Section: Introductionmentioning

confidence: 99%

Flexible job-shop scheduling with learning and forgetting effect by Multi-Agent System

Renna¹

2019

10.5267/j.ijiec

View full text Add to dashboard Cite

The processing time of the machine is assumed fixed in several studies. In many real industrial applications, the processing time is affected by learning and forgetting effects. This research proposes a scheduling approach to support a manufacturing system under learning/forgetting effect. The approach is supported by a Multi-Agent System to perform the scheduling activities in a quasi-real-time and in general manufacturing systems. A simulation environment is developed to test the proposed approach and the results are compared with a benchmark model for evaluating several performance measures of the manufacturing system. The simulation results highlight how the proposed approach improves all the performance measures under different conditions of inter-arrival time, learning and forgetting rates. A complete Analysis of the Variance highlights the main effects on the performance measures to support the decision maker of the manufacturing system.

show abstract

Effects of Non-Homogeneous Learning on the Performance of Serial Production Systems - A Simulation Study

Cited by 2 publications

References 10 publications

Extending Simulation-Based Assembly Planning to Include Human Learning and Previous Experience: A Simulation Study

Extending Simulation-Based Assembly Planning to Include Human Learning and Previous Experience: A Simulation Study

Flexible job-shop scheduling with learning and forgetting effect by Multi-Agent System

Contact Info

Product

Resources

About