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Latin hypercube sampling-based NSGA-III optimization model for multimode resource constrained time–cost–quality–safety trade-off in construction projects
101
Citations
35
References
2020
Year
Construction Project ManagementEngineeringProject SchedulingMultidisciplinary Design OptimizationOptimal System DesignOperations ResearchConstruction ProjectsGenetic AlgorithmSystems EngineeringModeling And SimulationPopulation InitializationNsga IiiDesignHyper-heuristicsLatin HypercubeConstruction OperationsInteger ProgrammingConstruction TechnologyOptimization ProblemCivil EngineeringTime–cost–quality–safety Trade-offConstruction ManagementMultimode ResourceSimulation OptimizationConstruction EngineeringResource Optimization
Many researchers have paid significant amount of attention for the development of time-cost, time-cost-quality and time–cost–safety trade-off optimization models. However, there is still a need to integrate the quality and safety together in time–cost trade-off optimization. To fill this gap, considering the multimode activities in project, this paper presents a multimode resource-constrained time–cost–quality–safety trade-off optimization model. Besides, this study considered the limited availability of resources for each execution mode of activities. A population-based meta-heuristics approach the nondominated sorting genetic algorithm III (NSGA III) is employed to develop the model. Also, Latin hypercube sampling for population initialization, analytical hierarchy process for quality determination and fuzzy logic for safety parameters determination are used. A case study of building construction project is used to demonstrate the applicability of proposed model. Whereas, the measure of various performance metrics and comparisons with existing trade-off optimization models demonstrate the effectiveness of proposed model in simultaneous optimization of four objectives. Moreover, a value path plot is prepared for the visualization of more than three objectives and a priori approach is presented to select one solution from obtained Pareto-optimal front.
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