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Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making

Sim-PROCESD 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. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems. The classes are designed to be extensible so the user can change their behavior to model more complex processes.In addition to modeling the behavior of existing systems, Sim-PROCESD is 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. Sim-PROCESD therefore provides tools to make simulation replication easy.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2023-06-28 00:00:00
Date Created: N/A
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Data Provided by:
discrete-event simulation
Dataset Owner: N/A

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Table representation of structured data
Title Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making
Description Sim-PROCESD 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. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems. The classes are designed to be extensible so the user can change their behavior to model more complex processes.In addition to modeling the behavior of existing systems, Sim-PROCESD is 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. Sim-PROCESD therefore provides tools to make simulation replication easy.
Modified 2023-06-28 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:[email protected]
Keywords discrete-event simulation , manufacturing , production , maintenance , python
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    "description": "Sim-PROCESD 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. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices\/components that can be configured by the user to model various real-world manufacturing systems. The classes are designed to be extensible so the user can change their behavior to model more complex processes.In addition to modeling the behavior of existing systems, Sim-PROCESD is 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. Sim-PROCESD therefore provides tools to make simulation replication easy.",
    "language": [
        "en"
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    "title": "Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making",
    "distribution": [
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            "title": "Sim-PROCESD"
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            "accessURL": "https:\/\/doi.org\/10.18434\/mds2-2733",
            "title": "DOI Access for Sim-PROCESD"
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            "description": "Python package index for SimPROCESD",
            "title": "simprocesd pip install"
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    "theme": [
        "Manufacturing:Factory operations planning and control",
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    ],
    "issued": "2022-08-17",
    "keyword": [
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        "manufacturing",
        "production",
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}

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