Sustainable Optimization of Concrete Transportation Systems for Dam Construction Using a BCMP Closed-Loop Queuing Network

To address the challenges of relying on experience for vehicle allocation in concrete transportation for dam construction—as well as the difficulty in coordinating vehicle capacity, loading/unloading capabilities, and system queuing status—this study focuses on a closed-loop transportation system comprising “batching plant—transport route—pouring area” and develops a vehicle allocation model based on a BCMP (Baskett–Chandy–Muntz–Palacios) closed-loop queuing network. The loading process at the mixing plant and the unloading process at the silo yard are modeled as finite-service nodes, while the transportation of loaded vehicles and the return of empty vehicles are modeled as infinite-service nodes. The Buzen convolution algorithm is used to solve for the system’s steady-state performance. With the number of vehicles N and the silo yard diversion ratio p as joint decision variables, the feasible region for vehicle configuration and recommended solutions are determined subject to constraints on silo yard feed demand and node utilization. The results of the case study show that, using reference operating condition S0 as a benchmark, a decrease in the loading efficiency of the batching plant increased the recommended number of vehicles by 28.57%; a simultaneous increase in the unloading efficiency of the dual-bin system reduced the recommended number of vehicles by 42.86%; enhanced coordination between the loading and unloading systems reduced the recommended number of vehicles by 50.00% and decreased the expected queuing time per cycle by 97.26%; furthermore, the system bottleneck shifted as loading and unloading capacities changed. Further independent validation was conducted using discrete-event simulation (DES); the maximum relative error between BCMP and DES in terms of system throughput, expected queuing wait time per cycle, and maximum node utilization was less than 1.5%. Sensitivity analysis indicates that concrete pouring demand and unloading capacity at the bin area are key factors affecting vehicle allocation. When demand increases or unloading capacity decreases beyond the system’s capacity limits, simply adding more vehicles does not result in a feasible solution. The study demonstrates that the proposed method can quantitatively reveal the relationships among vehicle fleet size, traffic diversion at the bin area, loading and unloading capacity, and queueing conditions, thereby providing a decision-making basis for the coordinated allocation of vehicles and loading/unloading resources in concrete construction for dams.