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Hazardous materials facility siting optimization and ranking: A transportation risk mitigation framework.

Musharraf Ahmad Khan¹, Babak Mehran¹

¹Urban Mobility and Transportation Informatics Group (UMTIG), Department of Civil Engineering, University of Manitoba, Winnipeg, Manitoba, Canada.

|November 15, 2023

Summary

This study introduces a new method for ranking hazardous material facility locations using stochastic analysis. This approach improves risk assessment and site selection for safer transportation networks.

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Data Acquisition Protocol for Determining Embedded Sensitivity Functions

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Published on: April 20, 2016

Area of Science:

Operations Research
Environmental Science
Risk Management

Background:

Hazardous material transportation is well-studied, but facility location-routing problems are underexplored.
Optimal facility siting is crucial for mitigating transportation risks.
Existing deterministic risk analyses may overestimate risks.

Purpose of the Study:

To propose a methodological framework for evaluating and ranking hazardous material facility sites.
To improve risk functions and apply stochastic analysis for more realistic risk measurement.
To incorporate multi-objective optimization considering network preferences and population/water body risks.

Main Methods:

A hierarchical relationship utility-based framework for site evaluation.
Stochastic analysis to measure transportation and facility risks.
Multi-objective optimization considering decision-makers' preferences.
Ranking sites by probability of optimality and relationship utilities.

Main Results:

The proposed stochastic analysis provides a more flexible and realistic approach to risk assessment.
The method effectively ranks potential hazardous material facility sites.
It allows for better consideration of population and environmental risks.

Conclusions:

The developed framework enhances the evaluation and ranking of hazardous material facility sites.
Stochastic analysis offers advantages over deterministic methods in risk assessment.
This approach aids in selecting optimal sites to minimize potential transportation risks.