A resource allocation approach for managing critical network-based infrastructure systems

In recent years, many resource allocation models have been developed to protect critical infrastructure by maximizing system resiliency or minimizing its vulnerability to disasters or disruptions. However, these are often computationally intensive and require simplifying assumptions and approximatio...

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Bibliographic Details
Published in:IIE transactions 2016-09, Vol.48 (9), p.826-837
Main Authors: Dehghani, Mohammad Saied, Sherali, Hanif D.
Format: Article
Language:English
Subjects:
Computation
Disruption
Energy efficiency
Infrastructure
Maintenance management
Mathematical models
Network flow problem
Network topologies
Networks
Nonlinearity
Optimization
Resource allocation
resource allocation
maintenance
critical infrastructure
risk
resiliency
Software
Studies
Transportation models
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Summary:In recent years, many resource allocation models have been developed to protect critical infrastructure by maximizing system resiliency or minimizing its vulnerability to disasters or disruptions. However, these are often computationally intensive and require simplifying assumptions and approximations. In this study, we develop a robust and representative, yet tractable, model for optimizing maintenance planning of generic network-structured systems (transportation, water, power, communication). The proposed modeling framework examines models that consider both linear and nonlinear objective functions and enhances their structure through suitable manipulations. Moreover, the designed models inherently capture the network topography and the stochastic nature of disruptions and can be applied to network-structured systems where performance is assessed based on network flow efficiency and mobility. The developed models are applied to the Istanbul highway system in order to assess their relative computational effectiveness and robustness using several test cases that consider single- and multiple-treatment types, and the problems are solved on the NEOS server using different available software. The results demonstrate that our models are capable of obtaining optimal solutions within a very short time. Furthermore, the linear model is shown to yield a good approximation to the nonlinear model (it determined solutions within 0.3% of optimality, on average). Managerial insights are provided in regard to the optimal policies obtained, which generally appear to favor selecting fewer links and applying a higher quality treatment to them.
ISSN:0740-817X
2472-5854
1545-8830
2472-5862
DOI:10.1080/0740817X.2016.1147662