Hai-jun Mao

Bio: Hai-jun Mao is an academic researcher from Southeast University. The author has contributed to research in topics: Parametric statistics & Vehicle routing problem. The author has an hindex of 2, co-authored 2 publications receiving 558 citations.

Vehicle Routing Problem: Simultaneous Deliveries and Pickups with Split Loads and Time Windows

Abstract: The vehicle routing problem with simultaneous deliveries and pickups (VRPSDP) has attracted much research interest because of the potential to provide cost savings to transportation and logistics operators. Several extensions of VRPSDP exist. Of these extensions, the simultaneous deliveries and pickups with split loads problem (SDPSLP) has been proposed to eliminate vehicle capacity constraints, as well as allow the deliveries or pickups for a customer to be split into multiple visits. Although delivery and pickup activities are often constrained by time windows, few studies have considered such constraints when SDPSLP has been addressed. To fill the gap, this paper formulates the vehicle routing problem of simultaneous deliveries and pickups with split loads and time windows (VRPSDPSLTW) as a mixed-integer programming problem. A hybrid heuristic algorithm was developed to solve this problem. Solomon data sets with minor modifications were applied to test the effectiveness of the solution algorithm. The r...

Location optimization of multiple distribution centers under fuzzy environment

Abstract: Locating distribution centers optimally is a crucial and systematic task for decision-makers. Optimally located distribution centers can significantly improve the logistics system’s efficiency and reduce its operational costs. However, it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center. With growing logistics demands, multiple distribution centers become necessary to meet customers’ requirements, but few studies have tackled the multiple distribution center locations (MDCLs) problem. This paper presents a comprehensive algorithm to address the MDCLs problem. Fuzzy integration and clustering approach using the improved axiomatic fuzzy set (AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria. Then, technique for order preference by similarity to ideal solution (TOPSIS) is applied for evaluating and selecting the best candidate for each cluster. Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure. Results from a case study in Guiyang, China, reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection. This new method may easily be extended to address location planning of other types of facilities, including hospitals, fire stations and schools.

An integrated optimisation framework for locating depots in shared autonomous vehicle systems

Abstract: This study presents an integrated optimisation framework for locating depots in a Shared autonomous vehicle (SAV) system under demand uncertainty. A two-stage stochastic mixed integer programming (MIP) model is formulated to optimise the number and locations of depots in a SAV system, where demand uncertainty is represented by multiple scenarios with occurrence probability. The dynamics of vehicle movements are further considered by forming a trip chain for each AV. An enhanced Benders decomposition-based algorithm with multiple Pareto-optimal cuts via multiple solutions is developed to solve the proposed model. The proposed modelling framework and the solution algorithm are tested using two different sizes of transportation networks. Computational analysis demonstrates that the proposed algorithm can handle large instances within acceptable computational cost, and be more efficient than the MIP solver. Meanwhile, insights regarding the optimal deployment of depots in SAV systems are also delivered under different parametric and demand pattern settings.

Coordinated Logistics with a Truck and a Drone

Abstract: We determine the efficiency of a delivery system in which an unmanned aerial vehicle UAV provides service to customers while making return trips to a truck that is itself moving. In other words, a UAV picks up a package from the truck which continues on its route, and after delivering the package, the UAV returns to the truck to pick up the next package. Although the hardware for such systems already exists, the extent to which such an approach can actually provide a significantly improved quality of service is not yet understood. By combining a theoretical analysis in the Euclidean plane with real-time numerical simulations on a road network, we demonstrate that the improvement in efficiency is proportional to the square root of the ratio of the speeds of the truck and the UAV. The online supplement is available at https://doi.org/10.1287/mnsc.2017.2824 . This paper was accepted by Vishal Gaur, operations management.