With the growing interest in environmental protection and congestion, electric vehicles are increasingly becoming the important transportation means. However, electric vehicles currently face several adoption barriers including high purchasing price and limited travelling range, so the fleets where electric vehicles and conventional vehicles coexist are closer to the current fleet management status. Considering the impact of charging facilities and carbon emission, this paper proposes a vehicle routing problem with a mixed fleet of conventional and electric vehicles and soft time windows. A bi-objective programming model is established to minimize total operational cost and time penalty cost. Finally, the nondominated sorting genetic algorithm II (NSGA-II) is employed to deal with this problem. Furthermore, single-objective optimization is carried out for the two objectives, respectively, and the linear weighting method is also used to solve the problem. Through the contrast of these results and the NSGA-II results, the effectiveness of the algorithm in this paper is further verified. The results indicate that two objectives are contradictory to some extent and decision-makers need a trade-off between two objectives.

The optimization of ferry vehicle scheduling is the key factor to improve the punctuality of flights and passenger satisfaction at airports. Based on the airport reality, a bi-objective mixed integer linear programming model for airport ferry vehicle scheduling is proposed in this paper, in which the first objective is to minimize the number of vehicles used, and the second objective is to minimize the maximum number of flights per ferry vehicle serving under the constraint that the first objective takes the optimal value. For the optimization model of the second objective, this paper designs three heuristic algorithms: strict equalization algorithm, relaxed equalization algorithm and transplantation algorithm, and integrates them into a main algorithm. The actual flight data of Beijing Capital International Airport are used for numerical examples, and all the examples tested can obtain the exact solution or high-quality approximate solution using the designed algorithm, which verifies the effectiveness of the algorithm. This study can be used to inform decisions on the efficient and balanced use of airport ferry vehicles. Despite the system presented in the paper is designed for airport, it can be applied to solve similar vehicle scheduling problems.

The retail sector supplies the daily fresh products and increasingly plays a key role in the stability and livability of cities. However, public health events such as COVID-19 have caused frequent product shortages in recent years. The risk of fresh product shortages not only causes retailers to lose profits, but also affects the normal life of residents. In this paper, we address the problem of designing a resilient retail supply network for fresh products under the supply disruption risks and propose a bi-objective mixed-integer programming model. This model can help retailers to select suppliers, distribution centers and transportation routes under different scenarios and implement three resilience strategies, namely, priority supply, multiple sourcing and lateral transshipment. We use the ε -constraint method to transform the multi-objective problem into a single objective model and develop a Lagrangian relaxation algorithm to solve the different scale instances. This model is solved for a real-life case of a supermarket to obtain managerial insights. In the case study, this paper shows the set of Pareto fronts with different inventory periods, maximum allowed decay and decay rate. We calculate the expected total cost under targeted disruption scenarios and evaluate the effectiveness of these resilience strategies when implemented concurrently or separately. Our results identify the most critical suppliers and distribution centers that should be fortified. We elaborate that more resilience strategies are not always better and managers need to take appropriate resilience strategies according to their own problems.