Data Publication

Simantha: Simulation for Manufacturing

Michael Hoffman, Mehdi Dadfarnia Author's orcid, Serghei Drozdov, Michael Sharp Author's orcid
Contact: Mehdi Dadfarnia..
Identifier: doi:10.18434/mds2-2530
Version: 1.0... First Released: 2022-02-07 Revised: 2022-02-07
Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems. The object classes are also designed to be extensible so that they can be used to model more complex processes.

In addition to modeling the behavior of existing systems, Simantha is also intended for use with simulation-based optimization and planning applications. For instance, users may be interested in evaluating alternative maintenance policies for a particular system. Estimating the expected system performance under each candidate policy will require a large number of simulation replications when the system is subject to a high degree of stochasticity. Simantha therefore supports parallel simulation replications to make this procedure more efficient.

Github repository: https://github.com/usnistgov/simantha
Research Areas
NIST R&D: Manufacturing: Manufacturing systems design and analysisManufacturing: Factory operations planning and control
Keywords: discrete-event simulationmanufacturingproductionmaintenancepython
These data are public.
Data and related material can be found at the following locations:
  Simantha
Github repository
Version: 1.0... First Released: 2022-02-07 Revised: 2022-02-07
Cite this dataset
Michael Hoffman, Mehdi Dadfarnia, Serghei Drozdov, Michael Sharp (2022), Simantha: Simulation for Manufacturing , National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2530 (Accessed 2024-10-12)
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