Green and sustainable tunnel maintenance activities scheduling under uncertainty

Abstract

The maintenance of tunnel infrastructure is fundamental to the reliability, safety, and efficiency of tunnel operations. However, environmental deterioration has dramatically changed maintenance scheduling of tunnel infrastructure. Therefore, the trade-off between maintenance cost and environmental impact is crucial when formulating maintenance schedules. This paper extends the preventive maintenance scheduling problem (PMSP) from three perspectives: social impact, environmental impact, and unexpected maintenance over the entire planning horizon. We then propose a stochastic bi-objective integer programming model to minimize the total cost and CO2 emissions of tunnel maintenance over the entire planning horizon. The model is applied to a case study developed for the Dalian Road Tunnel in Shanghai. A scenario-based method is adopted to account for uncertain failures. A hybrid algorithm using particle swarm optimization (PSO) and genetic algorithm (GA) is proposed to solve the model in realistic large-scale environments. Extensive numerical experiments are performed to verify the effectiveness of the proposed model and the efficiency of the proposed algorithm. Some meaningful management implications are revealed based on the experimental results.