Evaluation of Methods Used to Detect Warm-Up Period in Steady State Simulation

Mahajan, Prasad S.; Ingalls, Ricki G.

doi:10.1109/wsc.2004.1371374

Cited by 40 publications

(31 citation statements)

References 8 publications

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“…We also plan to automate the warm-up period detection based on various approaches mentioned in [18]. We also plan to leverage the current prototype for HP's 3D printing efforts.…”

Section: Discussionmentioning

confidence: 99%

Simulation as a cloud service for short-run high throughput industrial print production using a service broker architecture

Kothari

Peck

Zeng

et al. 2015

Simulation Modelling Practice and Theory

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“…We also plan to automate the warm-up period detection based on various approaches mentioned in [18]. We also plan to leverage the current prototype for HP's 3D printing efforts.…”

Section: Discussionmentioning

confidence: 99%

Simulation as a cloud service for short-run high throughput industrial print production using a service broker architecture

Kothari

Peck

Zeng

et al. 2015

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

“…Kimbler's Double Exponential Smoothing Method Kimbler and Knight (1987) Euclidean Distance (ED) Method Lee et al (1997) Neural Networks (NN) Method Lee et al (1997) Statistical Goodness-Of-Fit Test Pawlikowski (1990) Algorithm for a Static Dataset (ASD) Bause and Eickhoff (2003) Algorithm for a Dynamic Dataset (ADD) Bause and Eickhoff (2003) Kelton and Law Regression Method Kelton and Law (1983), Law (1983), Kimbler and Knight (1987), Pawlikowski (1990), Roth and Josephy (1993), Roth (1994), Gallagher et al (1996), Law and Kelton (2000), Linton and Harmonosky (2002) Glynn & Iglehart Bias Deletion Rule Glynn and Iglehart (1987) Wavelet-Based Spectral Method (WASSP) Lada et al (2003), Lada et al (2004), Lada and Wilson (2006) Queueing Approximations Method (MSEASVT) Rossetti and Delaney (1995) Chaos Theory Methods (methods M1 and M2) Lee and Oh (1994) Kalman Filter Method Gallagher et al (1996), Law and Kelton (2000) Randomisation Tests for Initialisation Bias Yucesan (1993), Mahajan and Ingalls (2004) Initialisation bias tests Schruben's Maximum Test (STS) Schruben (1982), Law (1983), Schruben et al (1983), Yucesan (1993), Ockerman and Goldsman (1999), Law andKelton (2000) Schruben's Modified Test Schruben (1982), , Law (1983), White et al(2000), Law and Kelton (2000) Optimal Test (Brownian bridge process) …”

Section: Methods Type Methodsmentioning

confidence: 99%

Automating warm-up length estimation

Hoad

Robinson

Davies

2010

Journal of the Operational Research Society

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There are two key issues in assuring the accuracy of estimates of performance obtained from a simulation model. The first is the removal of any initialisation bias, the second is ensuring that enough output data is produced to obtain an accurate estimate of performance. This paper is concerned with the first issue, and more specifically warm-up estimation. Our aim is to produce an automated procedure, for inclusion into commercial simulation software, for estimating the length of warm-up and hence removing initialisation bias from simulation output data. This paper describes the extensive literature search that was carried out in order to find and assess the various existing warm-up methods, the process of short-listing and testing of candidate methods. In particular it details the extensive testing of the warm-up MSER-5 method.

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“…To this aim, an extensive number of tests were made in different scenarios, obtained through the combination of the following parameters: radius r ࢠ {5, 15}, workstations per subline m ࢠ {5, 10}, number of picking trucks p ࢠ {1, 2}. Data were gathered performing simulation trials of 50 repetitions; in each simulation run data were gathered for 10,000 minutes, after a warm-up period of 1000 minutes, determined with the statistical process control (SPC) approach proposed by Mahajan and Ingalls (2004). Figures 8a and b display a selection of the obtained results of the throughput rate, for increasing values of the truck's capacity.…”

Section: Additional Numerical Experimentsmentioning

confidence: 99%

Just‐in‐time parts feeding policies for paced assembly lines: possible solutions for highly constrained layouts

Zammori

Braglia

Castellano

2015

Int Trans Operational Res

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This paper deals with the dimensioning of a just‐in‐time (JIT) parts feeding system, for a paced assembly line. Specifically, we consider the quite frequent case of an old‐fashioned, push‐oriented warehouse arrangement, characterized by materials and subassemblies that are stocked in different and peripheral areas of the manufacturing plant. In these circumstance, layout changes are almost impossible and warehouses cannot be substituted by supermarkets. Nevertheless, this does not preclude the possibility to rationalize material flow and to reduce work in process (WIP) using a JIT parts feeding system. To this aim, we propose two alternative approaches, differentiating in terms of the order with which warehouses and workstations are visited. In order to dimension both systems and to estimate their expected performances, a mathematical model is also presented. At first, the model is presented in a basic formulation, which is very effective and easy to use. A more sophisticated formulation, based on Bayesian probability, is introduced next; this is not so much for practical purposes, but rather to demonstrate the potential of the proposed JIT system in very constrained operating settings.

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Evaluation of Methods Used to Detect Warm-Up Period in Steady State Simulation

Cited by 40 publications

References 8 publications

Simulation as a cloud service for short-run high throughput industrial print production using a service broker architecture

Simulation as a cloud service for short-run high throughput industrial print production using a service broker architecture

Automating warm-up length estimation

Just‐in‐time parts feeding policies for paced assembly lines: possible solutions for highly constrained layouts

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