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