Document Type

Article

Publication Date

3-20-2019

DOI

http://dx.doi.org/10.1016/j.jclepro.2019.01.010

Abstract

In recent years, several models have been proposed to inoculate Water Distribution Systems (WDS) against impacts of accidental and/or intentional compromised water quality through optimal deployment of online monitoring sensors in the network, which is referred to as Contamination Warning Systems (CWS). Translating such modeling efforts to real-world practice is, however, a challenge as different involved parties may pursue conflicting goals and modeling-based recommendations may not justify all stakeholders’ criteria. It is, hence, pivotal to develop conflict resolution methodologies to support engagement of different stakeholders in securing a safe water distribution. The decision making structure for CWS design is often of top-down nature, with the upper level decision maker concerned mainly about public safety and lower level stakeholders concerned about operational costs. In this study, a decision support framework based on Leader-Follower Game is proposed, given different power levels. Leader’s objectives are focused on the CWS robustness, while followers have conflicting interests that are in turn resolved via Nash Bargaining method. Lamerd WDS (Fars, Iran) is selected to assess the proposed model’s performance. The results show the proposed objective and parsimonious model provides a robust solution that complies with the leader’s criteria and maximizes the followers’ satisfaction. The proposed decision support system helps govern WDSs in a resilient and safe manner and warrants practical implementation of modeling-based security assurance policies to provide sustainable service to the society.

Copyright Statement

This is an author-produced, peer-reviewed version of this article. © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-No Derivatives 4.0 license. The final, definitive version of this document can be found online at Journal of Cleaner Production, doi: 10.1016/j.jclepro.2019.01.010

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Saturday, March 20, 2021

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