BRT-lane-sharing, which allows regular buses to operate on Bus Rapid Transit (BRT) lanes without disrupting scheduled BRT operations, has gained prominence. It is helpful to increase the utilization of BRT lanes and enhance the efficiency of the bus transit system. However, the current approaches for designing bus transit networks and setting frequencies do not incorporate BRT-lane-sharing, thereby neglecting its potential benefits, including improved speeds, easier transfers, and cost savings. This paper proposes a pioneering study for the Bus Transit Network Design and Frequency Setting (BTNDFS) problem incorporating BRT-lane-sharing. A novel road network description is presented, specifically tailored to accommodate BRT-lane-sharing through the introduction of BRT nodes and BRT-lane arcs. Furthermore, a bi-level model is built for the BTNDFS problem incorporating BRT-lane-sharing. To solve this model, a Priority-Based Genetic Algorithm (PBGA) is proposed, in which a priority-based chromosome is defined, whilst priority-based crossover and mutation operators are devised. Experimental results on the standard Mandl’s benchmark instances indicate that the PBGA outperforms other metaheuristic approaches, with outcomes closely approximating optimal solutions. Further experiments are carried out on a real-world network featuring BRT-lane-sharing in the city of Linyi. The results show that the proposed model and the PBGA can reduce costs for passengers and operators, while simultaneously increasing the utilization of BRT lanes.

Vehicle scheduling plays a profound role in public transportation. Especially, stochastic vehicle scheduling may lead to more robust schedules. To solve the stochastic vehicle scheduling problem (SVSP), a discrete artificial bee colony algorithm (DABC) is proposed. Due to the discreteness of SVSP, in DABC, a new encoding and decoding scheme with small dimensions is designed, whilst an initialization rule and three neighborhood search schemes (i.e., discrete scheme, heuristic scheme, and learnable scheme) are devised individually. A series of experiments demonstrate that the proposed DABC with any neighborhood search scheme is able to produce better schedules than the benchmark results and DABC with the heuristic scheme performs the best among the three proposed search schemes.

Vehicle routing problem with time windows (VRPTW) is a core combinatorial optimization problem in distribution tasks. The electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the VRPTW. Although some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles, the literature on the integration of uncertain demand and energy consumption of electric vehicles is still scarce. However, practically, it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles (EVs) in actual operation. Hence, we propose the robust optimization model based on a route-related uncertain set to tackle this problem. Moreover, adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the problem. The effectiveness of the method is verified by experiments, and the influence of uncertain demand and uncertain parameters on the solution is further explored.