Modelling and simulation of discrete manufacturing industry

Neeraj, R.; Nithin, R Pranav; Niranjhan, P.; Sumesh, A.; Thenarasu, M.

doi:10.1016/j.matpr.2018.10.298

Cited by 22 publications

(11 citation statements)

References 6 publications

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“…Within a system of discrete events, one or more phenomena of interest change their value in discrete points in time [22]. Discrete event simulation considers the evolution of the system, but the states are modified only at discrete moments of time, and they are caused by the occurrence of some event.…”

Section: Discrete Event Simulationmentioning

confidence: 99%

Optimization of the Heap Leaching Process through Changes in Modes of Operation and Discrete Event Simulation

et al. 2019

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The importance of mine planning is often underestimated. Nonetheless, it is essential in achieving high performance by identifying the potential value of mineral resources and providing an optimal, practical, and realistic strategy for extraction, which considers the greatest quantity of options, materials, and scenarios. Conventional mine planning is based on a mostly deterministic approach, ignoring part of the uncertainty presented in the input data, such as the mineralogical composition of the feed. This work develops a methodology to optimize the mineral recovery of the heap leaching phase by addressing the mineralogical variation of the feed, by alternating the mode of operation depending on the type of ore in the feed. The operational changes considered in the analysis include the leaching of oxide ores by adding only sulfuric acid (H2SO4) as reagent and adding chloride in the case of sulfide ores (secondary sulfides). The incorporation of uncertainty allows the creation of models that maximize the productivity, while confronting the geological uncertainty, as the extraction program progresses. The model seeks to increase the expected recovery from leaching, considering a set of equiprobable geological scenarios. The modeling and simulation of this productive phase is developed through a discrete event simulation (DES) framework. The results of the simulation indicate the potential to address the dynamics of feed variation through the implementation of alternating modes of operation.

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Section: Discrete Event Simulationmentioning

confidence: 99%

Optimization of the Heap Leaching Process through Changes in Modes of Operation and Discrete Event Simulation

et al. 2019

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“…DES gives an indication of the best one before actual implementation which enables lean improvements to be systematically designed. Neeraj et al, (2018) used DES to improve performance in an aluminum brake brackets factory. Bottleneck identification and optimization were used in the model which showed improvements in productivity and workforce requirements.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Performance in an Aluminum Extrusion Plant Using Discrete Event Simulation: A Case Study

Al-Hawari

Naimi

Zurikat

et al. 2019

mijst

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Simulation has been used in many industrial applications for performance improvement. It excels over other system analysis methods in its high flexibility and ability to model system details with high accuracy. In this study, Discrete Event Simulation (DES) is used to improve the performance of an aluminum extrusion plant. A case study is presented in a local factory in which problems are identified, and their effects on efficiency are monitored. The main problem noticed was high production rates with respect to demand rates which resulted in large amounts of work-in-process (WIP) inventory. It was found that the current base system is unstable and suggestions were made to lower production rates in order to stabilize it. Average WIP was reduced by 324% once the system was stabilized with only 1.77% difference in weekly throughput which improved the system

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“…The above studies mainly focused on the throughput estimation of the manufacturing systems with one blocking type, which does not apply to manufacturing systems with mixed blocking types. To solve this problem, simulation methods [13][14][15] were proposed. However, compared to analytical methods, simulation methods usually require longer modeling and simulation time [16].…”

Section: Introductionmentioning

confidence: 99%

Throughput Estimation of Manufacturing Systems with Mixed Blocking Types

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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This study proposed a decomposed method to calculate the throughput of manufacturing systems with mixed blocking types, including blocking before service and blocking after service. In particular, a manufacturing system is decomposed into sub-models, which decreases the analysis complexity of the steadystate probability of the manufacturing system. The iterative calculation formulations for blocking before service and blocking after service are proposed to deal with mixed blocking types and approximate the steadystate probability of the manufacturing system by iteratively calculating the state probabilities of the sub-models. If the state probability of the sub-model with output converges after several iterations, the throughput of the manufacturing system can be obtained based on the steady-state probability of the sub-model. To verify the effectiveness of the proposed decomposition method, it was compared to a simulator in a numerical example. The results demonstrated the effectiveness of the proposed method.

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Modelling and simulation of discrete manufacturing industry

Cited by 22 publications

References 6 publications

Optimization of the Heap Leaching Process through Changes in Modes of Operation and Discrete Event Simulation

Optimization of the Heap Leaching Process through Changes in Modes of Operation and Discrete Event Simulation

Improving Performance in an Aluminum Extrusion Plant Using Discrete Event Simulation: A Case Study

Throughput Estimation of Manufacturing Systems with Mixed Blocking Types

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