Data Publication

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

Serghei Drozdov , Michael Sharp Author's orcid, Mehdi Dadfarnia Author's orcid
Contact: Mehdi Dadfarnia..
Identifier: doi:10.18434/mds2-2733
Version: 1.0... First Released: 2022-08-17 Revised: 2022-08-17


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.
Research Topics: Manufacturing: Factory operations planning and control, Manufacturing: Manufacturing systems design and analysis    
Subject Keywords: discrete-event simulation, manufacturing, production, maintenance, python    

Data Access

These data are public.
Data and related material can be found at the following locations:
Simulated-Production Resource for Operations & Conditions Evaluations to Support Decision-making
  simprocesd pip install
Python package index for SimPROCESD

About This Dataset

Version: 1.0... First Released: 2022-08-17 Revised: 2022-08-17
Cite this dataset
Drozdov , Serghei , Sharp, Michael, Dadfarnia, Mehdi (2023), Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making, National Institute of Standards and Technology, (Accessed 2024-06-18)
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