Growing global trade brings an increasing challenge to intelligent maritime transportation, which is an important branch of the intelligent transportation system. Developing efficient technologies to improve the performance of intelligent maritime transportation is especially important. Most existing works for integrated berth allocation and quay crane assignment assume that all the equipment is available over the time horizon, however, there exists frequently time-consuming quay crane maintenance activities in the maritime port. It is recognized that maintenance activities can impact the loading/unloading activities. In this paper, we study a new bi-objective optimization model of integrated berth allocation and quay crane assignment with preventive quay crane maintenance activities. The two objectives are minimizing the total turnaround time of vessels and the total penalty cost of quay crane maintenance earliness and tardiness. For the considered problem, an appropriate integer linear programming model is formulated, and an ϵ-constraint-based two-phase iterative heuristic is designed based on the characteristics of our problem. Computational results on a case study and randomly generated instances show the efficiency of the proposed algorithm.

Maritime transportation is an important branch of intelligent transportation system (ITS). It is widely recognized that modern ITS technologies and advanced management methods, such as automated yard crane (YC) planning and scheduling, can significantly improve container terminal performance, and also impact the global performance of maritime transportation. In this paper, we investigate two YC scheduling with storage and retrieval tasks in a container block. The main contributions of this paper are: 1) container reshuffling operations and inter-crane interference constraint are both considered and 2) the dynamic processing times for retrieval containers are taken into consideration. These typical YC operation characteristics complicate the YC scheduling, and cause late delivery and economic loss. In this study, we focus on minimizing the maximum tardiness of container task and establishing an integer linear programming model. Regarding the NP-hardness nature of the problem, we develop a heuristic named dividing, sequencing, and comparing (DSC) and a genetic algorithm (GA) based on the characteristics of the problem. The computational results show the efficient performance of the developed algorithms, compared with the exact solutions via Cplex software for small size instances. The efficiency and effectiveness of DSC outperform those of GA for practical size instances.

Drone delivery has a great potential to change the traditional parcel delivery service in consideration of cost reduction, resource conservation, and environmental protection. This paper introduces a novel drone fleet deployment and planning problem with uncertain delivery demand, where the delivery routes are fixed and couriers work in collaboration with drones to deliver surplus parcels with a relatively higher labor cost. The problem involves the following two-stage decision process: (i) The first stage determines the drone fleet deployment (i.e., the numbers and types of drones) and the drone delivery service module (i.e., the time segment between two consecutive departures) on a tactical level, and (ii) the second stage decides the numbers of parcels delivered by drones and couriers on an operational level. The purpose is to minimize the total cost, including (i) drone deployment and operating cost and (ii) expected labor cost. For the problem, a two-stage stochastic programming formulation is proposed. A classic sample average approximation method is first applied. To achieve computational efficiency, a hybrid genetic algorithm is further developed. The computational results show the efficiency of the proposed approaches.