State estimation of a shop floor using improved resampling rules for particle filtering

Nurcin Celik, Young-Jun Son

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Operational inefficiencies in supply chains cost industries millions of dollars every year. Much of these inefficiencies arise due to the lack of a coherent planning and control mechanism, which requires accurate yet timely state estimation of these large-scale dynamic systems given their massive datasets. While Bayesian inferencing procedures based on particle filtering paradigm may meet these requirements in state estimation, they may end up in a situation called degeneracy, where a single particle abruptly possesses significant amount of normalized weights. Resampling rules for importance sampling prevent the sampling procedure from generating degenerated weights for particles. In this work, we propose two new resampling rules concerning minimized variance (VRR) and minimized bias (BRR). The proposed rules are derived theoretically and their performances are benchmarked against that of the minimized variance and half-width based resampling rules existing in the literature using a simulation of a semiconductor die manufacturing shop floor in terms of their resampling qualities (mean and variance of root mean square errors) and computational efficiencies, where we identify the circumstances that the proposed resampling rules become particularly useful.

Original languageEnglish (US)
Pages (from-to)224-237
Number of pages14
JournalInternational Journal of Production Economics
Volume134
Issue number1
DOIs
StatePublished - Nov 2011

Fingerprint

State estimation
Importance sampling
Computational efficiency
Mean square error
Supply chains
Dynamical systems
Semiconductor materials
Sampling
Planning
Costs
Industry
Resampling
Shopfloor
Inefficiency

Keywords

  • Importance sampling
  • Resampling rules
  • Sequential Monte Carlo methods
  • Shop floor state estimation
  • Simulation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Business, Management and Accounting(all)
  • Management Science and Operations Research
  • Economics and Econometrics

Cite this

State estimation of a shop floor using improved resampling rules for particle filtering. / Celik, Nurcin; Son, Young-Jun.

In: International Journal of Production Economics, Vol. 134, No. 1, 11.2011, p. 224-237.

Research output: Contribution to journalArticle

@article{a6ae1b83a3ec4b88abd2cb248589fd21,
title = "State estimation of a shop floor using improved resampling rules for particle filtering",
abstract = "Operational inefficiencies in supply chains cost industries millions of dollars every year. Much of these inefficiencies arise due to the lack of a coherent planning and control mechanism, which requires accurate yet timely state estimation of these large-scale dynamic systems given their massive datasets. While Bayesian inferencing procedures based on particle filtering paradigm may meet these requirements in state estimation, they may end up in a situation called degeneracy, where a single particle abruptly possesses significant amount of normalized weights. Resampling rules for importance sampling prevent the sampling procedure from generating degenerated weights for particles. In this work, we propose two new resampling rules concerning minimized variance (VRR) and minimized bias (BRR). The proposed rules are derived theoretically and their performances are benchmarked against that of the minimized variance and half-width based resampling rules existing in the literature using a simulation of a semiconductor die manufacturing shop floor in terms of their resampling qualities (mean and variance of root mean square errors) and computational efficiencies, where we identify the circumstances that the proposed resampling rules become particularly useful.",
keywords = "Importance sampling, Resampling rules, Sequential Monte Carlo methods, Shop floor state estimation, Simulation",
author = "Nurcin Celik and Young-Jun Son",
year = "2011",
month = "11",
doi = "10.1016/j.ijpe.2011.07.003",
language = "English (US)",
volume = "134",
pages = "224--237",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier",
number = "1",

}

TY - JOUR

T1 - State estimation of a shop floor using improved resampling rules for particle filtering

AU - Celik, Nurcin

AU - Son, Young-Jun

PY - 2011/11

Y1 - 2011/11

N2 - Operational inefficiencies in supply chains cost industries millions of dollars every year. Much of these inefficiencies arise due to the lack of a coherent planning and control mechanism, which requires accurate yet timely state estimation of these large-scale dynamic systems given their massive datasets. While Bayesian inferencing procedures based on particle filtering paradigm may meet these requirements in state estimation, they may end up in a situation called degeneracy, where a single particle abruptly possesses significant amount of normalized weights. Resampling rules for importance sampling prevent the sampling procedure from generating degenerated weights for particles. In this work, we propose two new resampling rules concerning minimized variance (VRR) and minimized bias (BRR). The proposed rules are derived theoretically and their performances are benchmarked against that of the minimized variance and half-width based resampling rules existing in the literature using a simulation of a semiconductor die manufacturing shop floor in terms of their resampling qualities (mean and variance of root mean square errors) and computational efficiencies, where we identify the circumstances that the proposed resampling rules become particularly useful.

AB - Operational inefficiencies in supply chains cost industries millions of dollars every year. Much of these inefficiencies arise due to the lack of a coherent planning and control mechanism, which requires accurate yet timely state estimation of these large-scale dynamic systems given their massive datasets. While Bayesian inferencing procedures based on particle filtering paradigm may meet these requirements in state estimation, they may end up in a situation called degeneracy, where a single particle abruptly possesses significant amount of normalized weights. Resampling rules for importance sampling prevent the sampling procedure from generating degenerated weights for particles. In this work, we propose two new resampling rules concerning minimized variance (VRR) and minimized bias (BRR). The proposed rules are derived theoretically and their performances are benchmarked against that of the minimized variance and half-width based resampling rules existing in the literature using a simulation of a semiconductor die manufacturing shop floor in terms of their resampling qualities (mean and variance of root mean square errors) and computational efficiencies, where we identify the circumstances that the proposed resampling rules become particularly useful.

KW - Importance sampling

KW - Resampling rules

KW - Sequential Monte Carlo methods

KW - Shop floor state estimation

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=80052355327&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80052355327&partnerID=8YFLogxK

U2 - 10.1016/j.ijpe.2011.07.003

DO - 10.1016/j.ijpe.2011.07.003

M3 - Article

AN - SCOPUS:80052355327

VL - 134

SP - 224

EP - 237

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

IS - 1

ER -