State estimation of a supply chain using improved resampling rules for particle filtering

Nurcin Celik, Young Jun Son

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Resampling rules for importance sampling play a critical role in achieving good performance of the particle filters by preventing the sampling procedure from generating degenerated weights for particles, where a single particle abruptly possesses significant amount of normalized weights, and from wasting computational resources by replicating particles proportional to these weights. In this work, we propose two new resampling rules concerning minimized variance and minimized bias, respectively. Then, we revisit a half-with based resampling rule for benchmarking purposes. The proposed rules are derived theoretically and their performances are compared with that of the minimized variance and half width-based resampling rules existing in the literature using a supply chain simulation 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)
Title of host publicationProceedings of the 2010 Winter Simulation Conference, WSC'10
Number of pages13
StatePublished - 2010
Event2010 43rd Winter Simulation Conference, WSC'10 - Baltimore, MD, United States
Duration: Dec 5 2010Dec 8 2010

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2010 43rd Winter Simulation Conference, WSC'10
Country/TerritoryUnited States
CityBaltimore, MD

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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