Title: A fire management decision support systems to minimise economic losses: a case study in a petrochemical complex

Authors: Khaled Alutaibi; Abdullah Alsubaie; José Martí

Addresses: Department of Electrical Computer Engineering, University of British Columbia, Vancouver, Canada ' Water and Energy Research Institute, King Abdulaziz City for Science and Technology, Riyadh, Saudi Arabia ' Department of Electrical Computer Engineering, University of British Columbia, Vancouver, Canada

Abstract: Fires are very expensive to fight and may result in devastating human, economic, and environmental effects. Due to limited fire management resources and budget constraints, fire management becomes increasingly challenging. The increased interdependencies among existing infrastructure systems make economic losses induced by fires very severe and difficult to predict. Despite recent advances in fire management decision support systems (FMDSSs), economic analysis capabilities have not received enough attention in these systems. Efficient FMDSS incorporates economic considerations to determine optimal fire fighting tactics and strategies. This paper proposes an FMDSS for developing optimal fire management plans. The proposed system adopts the cost-plus-net-value change (C + NVC) concept to evaluate the economic efficiency of the plans. In order to capture the net value change of goods and services due to fires, an infrastructure interdependency simulator (i2Sim) is used to incorporate the interaction among infrastructure systems. The proposed FMDSS is capable of developing long-term (strategic) plans and short-term (operational) plans. The applicability of the proposed system is demonstrated using a case study involving multiple fire incidents in a large petrochemical complex.

Keywords: economic efficiency; decision support systems; fire management; critical infrastructures; interdependencies; firefighting; industrial fires; damage function; infrastructure interdependency simulator; i2Sim; cost-plus-net-value change; C+NVC; machine learning; petrochemical industry.

DOI: 10.1504/IJCIS.2018.091933

International Journal of Critical Infrastructures, 2018 Vol.14 No.2, pp.120 - 139

Received: 08 Apr 2017
Accepted: 09 Oct 2017

Published online: 21 May 2018 *

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