Economic optimization of industrial safety measures using genetic algorithms

被引：29

作者：

Caputo, Antonio C. ^{[1
]}

Pelagagge, Pacifico M. ^{[2
]}

Palumbo, Mario ^{[2
]}

机构：

[1] Univ Roma Tre, Dept Mech & Ind Engn, I-00146 Rome, Italy

[2] Univ Aquila, Dept Mech Energy & Management Engn, I-67100 Laquila, Italy

来源：

JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES | 2011年 / 24卷 / 05期

关键词：

Risk reduction; Genetic algorithm; Cost minimization; Cost-benefits analysis; MULTIOBJECTIVE OPTIMIZATION; TECHNICAL SPECIFICATIONS; COST; RISK; SELECTION;

D O I：

10.1016/j.jlp.2011.01.001

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The paper presents a computer-aided methodology for economic optimization of industrial plants safety. The method is based on the minimization of total safety-related cost including investment, operating expenses of adopted safety measures, and expected monetary loss from accidents. The objective function minimization is pursued resorting to a genetic algorithm which selects the best mix of safety measures able to attain the optimal risk level at minimum cost by factoring in the cost and risk reduction potential of each candidate safety measure. After a detailed description of the optimization approach the paper discusses two numerical examples showing the method application to both easy and complex decision making scenarios. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：541 / 551

页数：11

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