Integrating Variance Reduction Techniques and Parallel Computing in Construction Simulation Optimization

Abstract

Efficient planning of construction operations is deemed necessary to meet project objectives. Researchers have used simulation optimization to select the optimum amount of equipment and number of crews for construction operations. However, the current state of the practice suffers from the long computation time and the presence of inferior solutions in the final Pareto front. The objective of this paper is to develop and evaluate a robust simulation optimization framework. This framework is capable of reducing the computation time, improving the quality of optimal solutions, and increasing the confidence level in the optimality of the optimal solutions. This paper proposes the integration of common random numbers and parallel computing to achieve the stated objective. The parallel computing is performed on a single multicore processor. Based on the case study, the proposed framework was able to reduce the computation time by 90.5%, achieve a speedup of 2, improve the hypervolume indicator by 3.44%, and increase the confidence level by at least 100%. The values of improvement achieved will not necessarily be the same when different hardware, simulation models, simulation software, and optimization algorithms are used. The proposed framework allows project planners to obtain superior optimal solutions faster, which will make the use of stochastic simulation optimization more appealing.