Abstract
Off-site construction is a modern construction method that brings many sustainability merits to the built environment. However, the sub-optimal planning decisions (e.g., resource allocation, logistics and overtime planning decisions) of off-site construction projects can easily wipe away their sustainability merits. Therefore, simulation modelling—an efficient tool to consider the complexity and uncertainty of these projects—is integrated with metaheuristics, developing a simulation-optimization model to find the best possible planning decisions. Recent swarm intelligence metaheuristics have been used to solve various complex optimization problems. However, their potential for solving the simulation-optimization problems of construction projects has not been investigated. This research contributes by investigating the status-quo of simulation-optimization models in the construction field and comparing the performance of five recent swarm intelligence metaheuristics to solve the stochastic time–cost trade-off problem with the aid of parallel computing and a variance reduction technique to reduce the computation time. These five metaheuristics include the firefly algorithm, grey wolf optimization, the whale optimization algorithm, the salp swarm algorithm, and one improved version of the well-known bat algorithm. The literature analysis of the simulation-optimization models in the construction field shows that: (1) discrete-event simulation is the most-used simulation method in these models, (2) most studies applied genetic algorithms, and (3) very few studies used computation time reduction techniques, although the simulation-optimization models are computationally expensive. The five selected swarm intelligence metaheuristics were applied to a case study of a bridge deck construction project using the off-site construction method. The results further show that grey wolf optimization and the improved bat algorithm are superior to the firefly, whale optimization, and salp swarm algorithms in terms of the obtained solutions’ quality and convergence behaviour. Finally, the use of parallel computing and a variance reduction technique reduces the average computation time of the simulation-optimization models by about 87.0%. This study is a step towards the optimum planning of off-site construction projects in order to maintain their sustainability advantages.
Highlights
This study investigated, for the first time, recent Swarm Intelligence (SI) metaheuristics, namely: the firefly algorithm (FA), grey wolf optimization (GWO), the Novel Bat Algorithm (NBA), the whale optimization algorithm (WOA) and the salp swarm algorithm (SSA), for the integration of simulation and optimization, and applied them to infrastructure Off-site construction (OSC)
Based on the 95% confidence level, the statistical analysis proved that there is no statistically significant difference between the solution qualities obtained by GWO, the NBA and the WOA
The FA and SSA provide less optimal solutions. This comparative analysis proves that the NBA and GWO are very competitive for the SO of OSC projects
Summary
Off-site construction (OSC) is a sustainable construction method in which parts of the structure are produced off-site and transported to the construction site for erection [1,2]. OSC brings environmental improvements to the construction industry [6]. OSC could reduce greenhouse gas (GHG) emissions by 48%. OSC could reduce construction waste compared with traditional construction [8]. OSC is adopted in both building and infrastructure projects. OSC is an efficient way to narrow the gap between the supply and demand of both public and private buildings [9]. Many transportation agencies prefer OSC to deliver infrastructure projects to mitigate the traffic interruption resulting from the traditional cast-in-situ method [10]
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