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Title: Chemical supply chain modeling for analysis of homeland security events

Abstract

The potential impacts of man-made and natural disasters on chemical plants, complexes, and supply chains are of great importance to homeland security. To be able to estimate these impacts, we developed an agent-based chemical supply chain model that includes: chemical plants with enterprise operations such as purchasing, production scheduling, and inventories; merchant chemical markets, and multi-modal chemical shipments. Large-scale simulations of chemical-plant activities and supply chain interactions, running on desktop computers, are used to estimate the scope and duration of disruptive-event impacts, and overall system resilience, based on the extent to which individual chemical plants can adjust their internal operations (e.g., production mixes and levels) versus their external interactions (market sales and purchases, and transportation routes and modes). As a result, to illustrate how the model estimates the impacts of a hurricane disruption, a simple example model centered on 1,4-butanediol is presented.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1333836
Report Number(s):
SAND-2012-2956J
Journal ID: ISSN 0098-1354; PII: S0098135413002342
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Computers and Chemical Engineering
Additional Journal Information:
Journal Volume: 60; Journal Issue: C; Journal ID: ISSN 0098-1354
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; agent-based modeling; supply chains; linear programming; economic markets; transportation networks; supply chain resilience

Citation Formats

Ehlen, Mark A., Sun, Amy C., Pepple, Mark A., Eidson, Eric D., and Jones, Brian S. Chemical supply chain modeling for analysis of homeland security events. United States: N. p., 2013. Web. doi:10.1016/j.compchemeng.2013.07.014.
Ehlen, Mark A., Sun, Amy C., Pepple, Mark A., Eidson, Eric D., & Jones, Brian S. Chemical supply chain modeling for analysis of homeland security events. United States. https://doi.org/10.1016/j.compchemeng.2013.07.014
Ehlen, Mark A., Sun, Amy C., Pepple, Mark A., Eidson, Eric D., and Jones, Brian S. Fri . "Chemical supply chain modeling for analysis of homeland security events". United States. https://doi.org/10.1016/j.compchemeng.2013.07.014. https://www.osti.gov/servlets/purl/1333836.
@article{osti_1333836,
title = {Chemical supply chain modeling for analysis of homeland security events},
author = {Ehlen, Mark A. and Sun, Amy C. and Pepple, Mark A. and Eidson, Eric D. and Jones, Brian S.},
abstractNote = {The potential impacts of man-made and natural disasters on chemical plants, complexes, and supply chains are of great importance to homeland security. To be able to estimate these impacts, we developed an agent-based chemical supply chain model that includes: chemical plants with enterprise operations such as purchasing, production scheduling, and inventories; merchant chemical markets, and multi-modal chemical shipments. Large-scale simulations of chemical-plant activities and supply chain interactions, running on desktop computers, are used to estimate the scope and duration of disruptive-event impacts, and overall system resilience, based on the extent to which individual chemical plants can adjust their internal operations (e.g., production mixes and levels) versus their external interactions (market sales and purchases, and transportation routes and modes). As a result, to illustrate how the model estimates the impacts of a hurricane disruption, a simple example model centered on 1,4-butanediol is presented.},
doi = {10.1016/j.compchemeng.2013.07.014},
journal = {Computers and Chemical Engineering},
number = C,
volume = 60,
place = {United States},
year = {2013},
month = {9}
}

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