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Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization

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  • Dolatshahi-Zand, Ali
  • Khalili-Damghani, Kaveh

Abstract

SCADA11Supervisory Control And Data Acquisition: SCADA. is an essential system to control critical facilities in big cities. SCADA is utilized in several sectors such as water resource management, power plants, electricity distribution centers, traffic control centers, and gas deputy. The failure of SCADA results in crisis. Hence, the design of SCADA system in order to serve a high reliability considering limited budget and other constraints is essential. In this paper, a bi-objective redundancy allocation problem (RAP) is proposed to design Tehran׳s SCADA water resource management control center. Reliability maximization and cost minimization are concurrently considered. Since the proposed RAP is a non-linear multi-objective mathematical programming so the exact methods cannot efficiently handle it. A multi-objective particle swarm optimization (MOPSO) algorithm is designed to solve it. Several features such as dynamic parameter tuning, efficient constraint handling and Pareto gridding are inserted in proposed MOPSO. The results of proposed MOPSO are compared with an efficient ε-constraint method. Several non-dominated designs of SCADA system are generated using both methods. Comparison metrics based on accuracy and diversity of Pareto front are calculated for both methods. The proposed MOPSO algorithm reports better performance. Finally, in order to choose the practical design, the TOPSIS algorithm is used to prune the Pareto front.

Suggested Citation

  • Dolatshahi-Zand, Ali & Khalili-Damghani, Kaveh, 2015. "Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 11-21.
  • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:11-21
    DOI: 10.1016/j.ress.2014.07.020
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    References listed on IDEAS

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    1. Zio, E. & Bazzo, R., 2011. "Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 569-580.
    2. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    3. Zio, E., 2009. "Reliability engineering: Old problems and new challenges," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 125-141.
    4. Gen, Mitsuo & Yun, YoungSu, 2006. "Soft computing approach for reliability optimization: State-of-the-art survey," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 1008-1026.
    5. Zio, E. & Bazzo, R., 2011. "A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 624-634, May.
    6. Maghsoud Amiri & Amir-Reza Abtahi & Kaveh Khalili-Damghani, 2013. "Solving a generalised precedence multi-objective multi-mode time-cost-quality trade-off project scheduling problem using a modified NSGA-II algorithm," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 14(3), pages 355-372.
    7. Li, Zhaojun & Liao, Haitao & Coit, David W., 2009. "A two-stage approach for multi-objective decision making with applications to system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1585-1592.
    8. Khalili-Damghani, Kaveh & Amiri, Maghsoud, 2012. "Solving binary-state multi-objective reliability redundancy allocation series-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 35-44.
    9. Khalili-Damghani, Kaveh & Abtahi, Amir-Reza & Tavana, Madjid, 2013. "A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 58-75.
    10. Taboada, Heidi A. & Baheranwala, Fatema & Coit, David W. & Wattanapongsakorn, Naruemon, 2007. "Practical solutions for multi-objective optimization: An application to system reliability design problems," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 314-322.
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    4. Saeideh Sheikhpour & Amin Kargar-Barzi & Ali Mahani, 2022. "A novel component mixing and mixed redundancy strategy for reliability optimization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 328-346, February.
    5. Alikar, Najmeh & Mousavi, Seyed Mohsen & Raja Ghazilla, Raja Ariffin & Tavana, Madjid & Olugu, Ezutah Udoncy, 2017. "Application of the NSGA-II algorithm to a multi-period inventory-redundancy allocation problem in a series-parallel system," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 1-10.
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    8. Attar, Ahmad & Raissi, Sadigh & Khalili-Damghani, Kaveh, 2017. "A simulation-based optimization approach for free distributed repairable multi-state availability-redundancy allocation problems," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 177-191.
    9. Muhuri, Pranab K. & Nath, Rahul, 2019. "A novel evolutionary algorithmic solution approach for bilevel reliability-redundancy allocation problem," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
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