LAUSR

Abstract Under the background of the development of construction waste recycling in China, optimizing the site of construction waste recycling and disposal plant is important, considering not only the cost of construction waste recycling but also the impact on the surrounding environment. This study aims to minimize the cost and negative environmental effects. In order to find the best method to solve the problem of multiobjective function optimization, we propose a multiobjective location model which combines genetic algorithm with probabilistic robust optimization. The model first uses genetic algorithm to get preliminary result and then it uses probabilistic robust optimization to find the optimal solution. The preliminary results show that 1, 3, 5 of the candidate sites more cost-effective and environmentally friendly than other. The fitness value converges at a stable value of 1.55 × 10−5, and the Pareto optimal frontier presents considerable clustering characteristics, which prove the rationality and operability of the site selection optimization model. Meanwhile, the robust model analysis under the given uncertain environment achieves the purpose of further optimization of the site. The research results can provide the government with a theoretical basis for the site selection of construction and demolition waste recycling plants.