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$\varepsilon $-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.

The air transport industry is an important branch of the intelligent transportation system (ITS). It is widely admitted that modern ITS technologies and advanced management methods, such as fleet assignment, aircraft maintenance routing, and crew scheduling, can significantly increase an airline's market share and profit, and also improve customer satisfaction. This paper studies a new airline stochastic fleet assignment problem with random passenger demands under risk aversion. The objective is to maximize the expected total profit at a certain level of risk avoidance (i.e., conditional value-at-risk). To solve this problem, we present a risk-averse two-stage stochastic mixed-integer programming model. The first stage mainly deals with tactic level decisions: assigning aircraft families (e.g., Airbus A380 family) to flight legs. The operational level decisions are made in the second stage to efficiently assign aircraft types (e.g., Airbus A380-800 or A380-800F) to flight legs while meeting the family assignment plan developed in the first stage. Then, a sample average approximation algorithm is proposed to solve the stochastic programming problem considering risk aversion. A realistic international airline's numerical experiment is conducted to illustrate the efficiency of the proposed two-stage stochastic programming model and algorithm.

The quay crane scheduling problem (QCSP) is one of the most important problems for the operations at container ports. The QCSP aims to decide a QC schedule for loading and unloading containers so as to minimize the vessel turnaround time. The QCSP is subject to various kinds of constraints, e.g., task precedence constraints and QC noninterference constraints. This paper extends the QCSP by taking into consideration the stability constraints, which are crucial for the safety reason but often omitted in the existing literature. We provide a mathematical model for the QCSP with stability constraints (QCSPSCs). A bicriteria evolutionary algorithm is proposed to solve the QCSPSC. The algorithm consists of a sliding-window heuristic to fix the schedule, which violates the stability constraints. Extensive experiments are conducted to demonstrate the effectiveness of the algorithm. The computational results of the traditional QCSP and the QCSPSC are also compared and analyzed.

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.

Time-of-use (TOU) pricing has been implemented by many electricity suppliers to alleviate the peak load of power grid, which provides a good opportunity for industrial consumers to reduce their energy bills. In industrial enterprises that involve batch processing machines, energy expenditure often accounts for large portion of the final product cost. Optimizing batch scheduling under TOU tariffs in these enterprises will be of great significance. This study investigates a single machine batch scheduling problem under TOU tariffs. The objective is to minimize the total electricity cost by optimally scheduling all jobs within a given planning horizon. Two mixed integer linear programming (MILP) models, which are respectively based on time-index formulation and time-interval formulation, are developed for the problem. The models are solved by CPLEX. Computational results on randomly generated instances demonstrate the effectiveness of the proposed approaches.