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An example of simulation optimisation using a neural network metamodel: finding the optimum number of kanbans in a manufacturing system
3 references·10 citing·39 second hop
An example of simulation optimisation using a neural network metamodel: finding the optimum number of kanbans in a manufacturing system (1997)An example of simulation optimisation usi…Response Surface Methodology (1996)Response Surface MethodologyMetamodels in manufacturing: a review (1994)Metamodels in manufacturing: …Just-in-time manufacture (1987)Just-in-time manufactureExploring the behavior space of agent-based simulation models using random forest metamodels and sequential sampling (2018)Exploring the behavior space …The optimal control of just-in-time-based production and distribution systems and performance comparisons with optimized pull systems (2011)The optimal control of just-i…Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system (2004)Knowledge-based improvement: …A survey of recent advances in discrete input parameter discrete-event simulation optimization (2004)A survey of recent advances i…Optimization of buffer sizes in assembly systems using intelligent techniques (2003)Optimization of buffer sizes …An integrated multidimensional process improvement methodology for manufacturing systems (2003)An integrated multidimensiona…Simulation metamodelling with neural networks: An experimental investigation (2002)Simulation metamodelling with…Process optimization via neural network metamodeling (2002)Process optimization via neur…Simulation optimization using tabu search (2000)Simulation optimization using…A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels (1999)A comparison of factorial and…International Journal of Production Research (1962)The construction and implementation of metamodels (1975)A methodology for fitting and validating metamodels in simulation1Two anonymous referees' comments on the first draft lead to an improved organization of our paper.1 (2000)Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach (2006)Ordinal optimization of DEDS (1992)Design and Analysis of Experiments for Statistical Selection, Screening and Multiple Comparisons (1996)PROCEEDINGS OF THE 1997 WINTER SIMULATION CONFERENCE (1997)Improving model understanding using statistical screening (2009)The Purpose of Mathematical Programming is Insight, Not Numbers (1976)Detecting Initialization Bias in Simulation Output (1982)Computer simulation of a PCB production line: metamodeling approach (2000)Simulation metamodel development using uniform design and neural networks for automated material handling systems in semiconductor wafer fabrication (2007)Time Series Analysis: Forecasting and Control (1977)Introduction to Statistical Quality Control (1992)Reengineering the corporation: A manifesto for business revolution (1993)Una revisión de los modelos de mejoramiento de procesos con enfoque en el rediseño (2012)The Queueing Network Analyzer (1983)Introduction to Queueing Networks (1987)Stochastic Modelling and Analysis: A Computational Approach. (1987)Designing work breakdown structures using modular neural networks (2007)An integrated artificial neural network-computer simulation for optimization of complex tandem queue systems (2011)Work breakdown structure (WBS) development for underground construction (2015)Desiǵn and analysis of Pull System, a method of multi-staǵe production control (1981)Toyota Production System (1993)Simulation optimisation of pull control policies for serial manufacturing lines and assembly manufacturing systems using genetic algorithms (2009)Simulation Optimization: A Review on Theory and Applications (2013)Optimization via simulation: A review (1994)Simulation optimization (1997)A survey of simulation optimization techniques and procedures (2002)Transient behaviour of unbalanced lines (2011)Dynamic models of segregation† (1971)Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Response Surfaces: Designs and Analyses (1997)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Response surface methodology (2010)Less pain but what gain?: a comparison of the effectiveness and effects of Japanese and non-Japanese car assemblers' buyer-supplier relations in the UK automotive industry (1997)Produktionsplanung und -steuerung (1999)JIT SYSTEM: CONCEPTS, BENEFITS AND MOTIVATION IN INDIAN INDUSTRIES (2011)
this work references cited by second hop

References

  • 1996Response Surface Methodology3,819 cited
  • 1994Metamodels in manufacturing: a review72 cited
  • 1987Just-in-time manufacture48 cited

Cited by

  • 2004A survey of recent advances in discrete input parameter discrete-event simulation optimization65 cited
  • 2003Optimization of buffer sizes in assembly systems using intelligent techniques63 cited
  • 2002Process optimization via neural network metamodeling61 cited
  • 2002Simulation metamodelling with neural networks: An experimental investigation58 cited
  • 2003An integrated multidimensional process improvement methodology for manufacturing systems56 cited
  • 2000Simulation optimization using tabu search46 cited
  • 2018Exploring the behavior space of agent-based simulation models using random forest metamodels and sequential sampling43 cited
  • 2004Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system41 cited
  • 1999A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels41 cited
  • 2011The optimal control of just-in-time-based production and distribution systems and performance comparisons with optimized pull systems22 cited

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