Parallel Computing Platform for Multiobjective Simulation Optimization of Bridge Maintenance Planning

The maintenance planning of deteriorating bridges is to find a balance between obtained performance and incurred cost. Because the planning horizon spans tens of years, a certain amount of uncertainty is inherent in forecasting the deteriorating process and the costs and effects of maintenance actions. This paper proposes a multiobjective simulation optimization framework to establish the trade-off among the expected values of life-cycle maintenance cost and of the performance measures. The trade-off information, represented as the Pareto front, gives planners sufficient flexibility to respond to various needs. The optimization is performed by a multiobjective particle swarm optimization (MOPSO) algorithm, while Monte Carlo simulation is used to model the uncertainties. To alleviate the computational burden, the proposed framework is implemented in a parallel computing platform, where three programming paradigms (master-slave, island, and diffusion) are developed to distribute computation across processor...

[1]  Dan M. Frangopol,et al.  Lifetime cost optimization of structures by a combined condition–reliability approach , 2009 .

[2]  T.A.A. Victoire,et al.  Reserve constrained dynamic dispatch of units with valve-point effects , 2005, IEEE Transactions on Power Systems.

[3]  Jonathan E. Fieldsend,et al.  Using unconstrained elite archives for multiobjective optimization , 2003, IEEE Trans. Evol. Comput..

[4]  Thomas Hérault,et al.  Blocking vs. non-blocking coordinated checkpointing for large-scale fault tolerant MPI Protocols , 2008, Future Gener. Comput. Syst..

[5]  Shu-Kai S. Fan,et al.  A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search , 2006, Comput. Ind. Eng..

[6]  Dan M. Frangopol,et al.  Life-cycle performance of deteriorating structures : assessment, design, and management , 2003 .

[7]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[8]  Carlos A. Coello Coello,et al.  Advances in Multi-Objective Nature Inspired Computing , 2010, Advances in Multi-Objective Nature Inspired Computing.

[9]  Dan M. Frangopol,et al.  Probabilistic Performance Prediction of Deteriorating Structures Under Different Maintenance Strategies: Condition, Safety and Cost , 2003 .

[10]  Hojjat Adeli,et al.  Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms, and Parallel Computing , 2006 .

[11]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[12]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[13]  Luh-Maan Chang,et al.  BOT Financial Model: Taiwan High Speed Rail Case , 2001 .

[14]  Yoshito Itoh,et al.  Multiobjective Optimization of Bridge Deck Rehabilitation Using a Genetic Algorithm , 1997 .

[15]  Dan M. Frangopol,et al.  Lifetime-oriented multi-objective optimization of structural maintenance considering system reliability, redundancy and life-cycle cost using GA , 2009 .

[16]  Michael P. Enright,et al.  Condition Prediction of Deteriorating Concrete Bridges Using Bayesian Updating , 1999 .

[17]  Manjaree Pandit,et al.  Particle swarm optimization with crazy particles for nonconvex economic dispatch , 2009, Appl. Soft Comput..

[18]  Selim G. Akl.  Superlinear Performance in Real-Time Parallel Computation , 2004, The Journal of Supercomputing.

[19]  Dan M. Frangopol,et al.  Probabilistic Lifetime-Oriented Multiobjective Optimization of Bridge Maintenance: Combination of Maintenance Types , 2006 .

[20]  Johannes Bader,et al.  Hypervolume-based search for multiobjective optimization: Theory and methods , 2010 .

[21]  G. Amdhal,et al.  Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).

[22]  Dan M. Frangopol,et al.  Multiobjective Maintenance Planning Optimization for Deteriorating Bridges Considering Condition, Safety, and Life-Cycle Cost , 2005 .

[23]  Dan M. Frangopol,et al.  Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost* , 2007 .

[24]  I-Tung Yang,et al.  Multiobjective optimization for manpower assignment in consulting engineering firms , 2011, Appl. Soft Comput..

[25]  Dan M. Frangopol,et al.  RELIABILITY-BASED LIFE-CYCLE MANAGEMENT OF HIGHWAY BRIDGES , 2001 .

[26]  F. Cappello,et al.  Blocking vs. Non-Blocking Coordinated Checkpointing for Large-Scale Fault Tolerant MPI , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[27]  Bradley P. Carlin,et al.  Bayesian Methods for Data Analysis , 2008 .

[28]  Stephanie E. Chang,et al.  Life-Cycle Cost Analysis with Natural Hazard Risk , 1996 .

[29]  Ihsan Sabuncuoglu,et al.  Simulation optimization: A comprehensive review on theory and applications , 2004 .

[30]  I-Tung Yang,et al.  Using elitist particle swarm optimization to facilitate bicriterion time-cost trade-off analysis , 2007 .