April  2015, 11(2): 595-617. doi: 10.3934/jimo.2015.11.595

Bilevel multi-objective construction site security planning with twofold random phenomenon

1. 

Business School, Sichuan University, Chengdu 610064, China

2. 

State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064

3. 

Decision Sciences Department, LeBow College of Business, Drexel University, Philadelphia, PA 19104, United States, United States

4. 

China Three Gorges Corporation, Yichang 443001, China

Received  November 2012 Revised  May 2014 Published  September 2014

The lack of early security arrangements during the construction period can increase the vulnerability of construction project. To support current construction security standards, this paper proposes a bilevel multi-objective model for construction site security problem (CSSP). In contrast to prior studies of CSSP, the bilevel relationship and twofold random phenomenon are considered. Specifically, the upper level programming denotes that project security officer must first decide which facilities to be secured under limited funds whilst maximizing the efficiency of the construction facilities system and minimizing the countermeasure cost and economic loss. The lower level programming denotes that the attacker will destroy a subset of the facilities to inflict maximum loss of efficiency in the construction facilities system. To deal with the uncertainties, expected value method and chance constraint method are introduced to transform the uncertain model into a calculable one. Thereafter, a stochastic simulation based constraint checking procedure is designed. Plant Growth Simulation Algorithm (PGSA) is applied to solve this model. Finally, the approach is carried out in the Longtan hydropower construction project to illustrate the efficiency of the proposed model and algorithm.
Citation: Zongmin Li, Jiuping Xu, Wenjing Shen, Benjamin Lev, Xiao Lei. Bilevel multi-objective construction site security planning with twofold random phenomenon. Journal of Industrial & Management Optimization, 2015, 11 (2) : 595-617. doi: 10.3934/jimo.2015.11.595
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show all references

References:
[1]

Implementation resource BMM-3, Benchmarking and Metrics, CII, University of Texas at Austin, USA, 2005. Google Scholar

[2]

Report No DOT/FAA/AR-00/52, U.S. Dept. of Transportation, Washington, D.C, 2001. Google Scholar

[3]

manual 12 FAM 360, Foreign Affairs Manual, Chapter 12- Diplomatic Security, U.S. Department of State, 2002. Google Scholar

[4]

Federal Register, 58 (1993), 1-6. Google Scholar

[5]

Journal of Industrial and Management Optimization, 10 (2014), 543-556. doi: 10.3934/jimo.2014.10.543.  Google Scholar

[6]

In Systems and Information Engineering Design Symposium, SIEDS'09., 2009 (2009), 171-176. Google Scholar

[7]

Institute of Electrical and Electronics Engineers (IEEE), Monterey, CA, USA, 2007. Google Scholar

[8]

University of Gavle, 2009, Master's Thesis in Energy System. Google Scholar

[9]

Journal of Industrial and Management Optimization, 1 (2011), 549-561. Google Scholar

[10]

Management Science, 6 (1959), 73-79. doi: 10.1287/mnsc.6.1.73.  Google Scholar

[11]

Annals of the Association of American Geographers, 94 (2004), 491-502. Google Scholar

[12]

Journal of Construction Engineering and Management, 135 (2009), 1316-1323. Google Scholar

[13]

Journal of Construction Engineering and Management, 134 (2008), 40-48. Google Scholar

[14]

in Proceedings of International Conference of Management Science and Engineering, (2005), 13-15. Google Scholar

[15]

Journal of Industrial and Management Optimization, 7 (2011), 365-383. doi: 10.3934/jimo.2011.7.365.  Google Scholar

[16]

Computers & Operations Research, 38 (2011), 357-366. doi: 10.1016/j.cor.2010.06.002.  Google Scholar

[17]

Springer Verlag, 2009. Google Scholar

[18]

Journal of Industrial and Management Optimization, 1 (2005), 305-314. doi: 10.3934/jimo.2005.1.305.  Google Scholar

[19]

Journal of Management in Engineering, 22 (2006), 196-202. Google Scholar

[20]

Natural Hazards and Earth System Science, 4 (2004), 153-163. Google Scholar

[21]

European Journal of Operational Research, 209 (2011), 23-36. doi: 10.1016/j.ejor.2010.08.030.  Google Scholar

[22]

Computers & Industrial Engineering, 53 (2007), 433-453. Google Scholar

[23]

Electrical Power and Energy Systems, 33 (2011), 1133-1139. Google Scholar

[24]

J. A. Rice, Mathematical Statistics and Data Analysis,, 2007, ().   Google Scholar

[25]

Automation in Construction, 19 (2010), 221-234. Google Scholar

[26]

International Journal of Advances in Science and Technology, 30 (2011), 43-54. Google Scholar

[27]

Computers & Operations Research, 35 (2008), 1905-1923. Google Scholar

[28]

Journal of Optimization Theory and Applications, 11 (1973), 533-555. doi: 10.1007/BF00935665.  Google Scholar

[29]

presentation in: 4th Annual International Airfield Operations Area Expo & Conference, Airport Consultants Council (AAC), Milwaukee, WI, USA, 2008. Google Scholar

[30]

in Security Technology 28th International Carnahan conference, Institute of Electrical and Electronics Engineers (IEEE), Atlanta, GA, USA, (1992), 164-168. Google Scholar

[31]

Journal of Construction Engineering and Management, 128 (2002), 203-210. Google Scholar

[32]

European Journal of Operational Research, 188 (2008), 76-84. doi: 10.1016/j.ejor.2007.04.003.  Google Scholar

[33]

Journal of Global Optimization, 5 (1994), 291-306. doi: 10.1007/BF01096458.  Google Scholar

[34]

presentation in: 28th IRMI Construction Risk Conference, IRMI, Las Vegas, VA, USA, 2008. Google Scholar

[35]

European Transactions on Electrical Power, 19 (2009), 291-301. Google Scholar

[36]

International Journal of Production Economics, 131 (2011), 709-720. Google Scholar

[37]

Mathematics and Computers in Simulation, 85 (2012), 11-33. doi: 10.1016/j.matcom.2012.09.010.  Google Scholar

[38]

Mathematical Problems in Engineering, 2012 (2012), Article ID 463976, 40 pages. doi: 10.1155/2012/463976.  Google Scholar

[39]

Journal of Industrial and Management Optimization, 9 (2013), 31-56. doi: 10.3934/jimo.2013.9.31.  Google Scholar

[40]

J. P. Xu and L. M. Yao, Random-Like Multiple Objective Decision Making,, 2011, ().   Google Scholar

[41]

Journal of Industrial and Management Optimization, 6 (2010), 751-760. doi: 10.3934/jimo.2010.6.751.  Google Scholar

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