ENHANCING GENETIC ALGORITHM APPROACH FOR OPTIMIZING MUCK TRANSPORT SCHEDULING IN METRO CONSTRUCTION

Abstract

The rapid expansion of China's urban rail transit network in recent years, boasting a total operating mileage of 8,708 km in 2021 (2.4 times the 2015 figure), with subways comprising nearly 79% of this extensive network, has magnified the challenge of managing substantial volumes of sludge generated during construction. Limited storage capacities at construction stations necessitate the timely transport of muck, as delays disrupt subway construction progress.

To address this issue, most subway construction projects have established muck treatment plants. However, a lack of coordinated scheduling between construction teams at each station and these treatment plants can result in congestion and idle machinery, severely hampering treatment center efficiency. Investigative research revealed that congestion arises during the morning and evening peak hours due to traffic restrictions on urban main roads. Conversely, extended periods of inactivity occur during other hours. This insight prompted the development of a subway residue transportation scheduling model, optimizing vehicle waiting times. The accompanying algorithm and scheduling optimization scheme reduce the variability in muck truck arrival times at treatment plants, enhancing overall transportation and treatment efficiency