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

5 Citations (Scopus)

Abstract

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
Title of host publicationProceedings - Winter Simulation Conference
Pages1998-2010
Number of pages13
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event2010 43rd Winter Simulation Conference, WSC'10 - Baltimore, MD, United States
Duration: Dec 5 2010Dec 8 2010

Other

Other2010 43rd Winter Simulation Conference, WSC'10
CountryUnited States
CityBaltimore, MD
Period12/5/1012/8/10

Fingerprint

Importance sampling
Particle Filtering
Benchmarking
State Estimation
State estimation
Resampling
Computational efficiency
Supply Chain
Mean square error
Supply chains
Sampling
Importance Sampling
Particle Filter
Computational Efficiency
Directly proportional
Roots
Resources
Simulation

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Cite this

Celik, N., & Son, Y. J. (2010). State estimation of a supply chain using improved resampling rules for particle filtering. In Proceedings - Winter Simulation Conference (pp. 1998-2010). [5678871] https://doi.org/10.1109/WSC.2010.5678871

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

Proceedings - Winter Simulation Conference. 2010. p. 1998-2010 5678871.

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

Celik, N & Son, YJ 2010, State estimation of a supply chain using improved resampling rules for particle filtering. in Proceedings - Winter Simulation Conference., 5678871, pp. 1998-2010, 2010 43rd Winter Simulation Conference, WSC'10, Baltimore, MD, United States, 12/5/10. https://doi.org/10.1109/WSC.2010.5678871
Celik, Nurcin ; Son, Young Jun. / State estimation of a supply chain using improved resampling rules for particle filtering. Proceedings - Winter Simulation Conference. 2010. pp. 1998-2010
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