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

Nurcin Celik, Young Jun Son

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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
Issue number1
StatePublished - Nov 2011


  • 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


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