Designing container shipping network under changing demand and freight rates

This paper focuses on the optimization of container shipping network and its operations under changing cargo demand and freight rates. The problem is formulated as a mixed integer non-linear programming problem (MINP) with an objective of maximizing the average unit ship-slot profit at three stages using analytical methodology. The issues such as empty container repositioning, ship-slot allocating, ship sizing, and container configuration are simultaneously considered based on a series of the matrices of demand for a year. To solve the model, a bi-level genetic algorithm based method is proposed. Finally, numerical experiments are provided to illustrate the validity of the proposed model and algorithms. The obtained results show that the suggested model can provide a more realistic solution to the issues on the basis of changing demand and freight rates and arrange a more effective approach to the optimization of container shipping network structures and operations than does the model based on the average demand. First published online: 27 Oct 201

A method integrating simulation and reinforcement learning for operation scheduling in container terminals

The objective of operation scheduling in container terminals is to determine a schedule that minimizes time for loading or unloading a given set of containers. This paper presents a method integrating reinforcement learning and simulation to optimize operation scheduling in container terminals. The introduced method uses a simulation model to construct the system environment while the Q-learning algorithm (reinforcement learning algorithm) is applied to learn optimal dispatching rules for different equipment (e.g. yard cranes, yard trailers). The optimal scheduling scheme is obtained by the interaction of the Q-learning algorithm and simulation environment. To evaluate the effectiveness of the proposed method, a lower bound is calculated considering the characteristics of the scheduling problem in container terminals. Finally, numerical experiments are provided to illustrate the validity of the proposed method

An integrating scheduling model for mixed cross-operation in container terminals

This paper focuses on the optimization of operation scheduling in container terminals based on mix cross-operation. Mix cross-operation is a scheduling method which allows yard trailers to be shared by different yard cranes in different berths to decrease yard trailers’ travel distance. An integrating scheduling model that optimizes the three key and interrelated issues, namely, berth assignment, equipment configuration and trailer routing are proposed. To solve the model, a bi-level genetic algorithm is designed. Numerical tests show that integrating scheduling method can reduce operation cost of container terminals significantly and mix cross-operation can decrease yard trailers’ empty travel distance to a great extent

A lexicographic optimization approach for berth schedule recovery problem in container terminals

In container terminals, the planned berth schedules often have to be revised because of disruptions caused by severe weather, equipment failures, technical problems and other unforeseen events. In this paper, the problem of berth schedule recovery is addressed to reduce the influences caused by disruptions. A multi-objective, multi-stage model is developed considering the characteristics of different customers and the trade-off of all parties involved. An approach based on the lexicographic optimization is designed to solve the model. Numerical experiments are provided to illustrate the validity of the proposed Model A and algorithms. Results indicate that the designed Model A and algorithm can tackle the berth plan recovery problem efficiently because the beneficial trade-off among all parties involved are considered. In addition, it is more flexible and feasible with the aspect of practical applications considering that the objective order can be adjusted by decision makers

Modeling the sailing risk of RoPax ships with Bayesian Network

In this paper, a methodology based on Bayesian Network (BN) was proposed to deal with the difficulty of risk analysis in RoPax transport. Based on data collection and expert survey, BN model for RoPax sailing risk analysis was constructed first. Then the Expectation Maximization (EM) algorithm for parameter learning and Evidence Prepropagation Importance Sampling (EPIS) algorithm for reasoning were designed. Finally, a sensitivity analysis was conducted. To validate the model algorithms, a case study on the RoPax system of Bohai gulf in China was provided. Results indicate that the BN model can effectively address the problem of data deficiency and mutual dependency of incidents in risk analysis. It can also model the development process of unexpected hazards and provide decision support for risk mitigation. First published online: 10 Sep 201

The stochastic container relocation problem with flexible service policies

This paper investigates the Stochastic Container Relocation Problem in which a flexible service policy is adopted in the import container retrieval process. The flexible policy allows the terminal operators to determine the container retrieval sequence to some extent, which provides more opportunity for reducing the number of relocations and the truck waiting times. A more general probabilistic model that captures customers’ arrival preference is presented to describe the randomness for external truck arrivals within their appointed time windows. Being a multi-stage stochastic sequential decision-making problem, it is first formulated into a stochastic dynamic programming (SDP) model to minimize the expected number of relocations. Then, the SDP model is extended considering a secondary objective representing the truck waiting times. Tree search-based algorithms are adapted to solve the two models to their optimality. Heuristic algorithms are designed to seek high-quality solutions efficiently for larger problems. A discrete-event simulation model is developed to evaluate the optimal solutions and the heuristic solutions respectively on two performance metrics. Extensive computational experiments are performed based on instances from literature to verify the effectiveness of the proposed models and algorithms

Economic forces shaping the evolution of integrated port systems - The case of the container port system of China's Pearl River Delta

International audienceWe investigate the evolution of the container port system of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), better known as Pearl River Delta (PRD). We analyze the economic drivers that over the years have shaped port development in one of the world's most dynamic regions, embracing three of the world's busiest container ports: Shenzhen, Hong Kong and Guangzhou. Three industry concentration methodologies are employed, each with its own distinct advantages: Concentration Ratios; the Herfindahl-Hirschman Index; and Dynamic Shift-Share Analysis (DSSA). Especially through the latter methodology, DSSA, -used here for the first time in the analysis of the evolution of ‘port systems’- we explain not only the shifts in market shares among the three ‘giants’, but also the underlying economic forces responsible for these shifts and for the relocation of economic activity in the hinterlands of those ports. We analyze the foreland and hinterland strategies of the ports, pursued as a result of rising inter-port competition and fuzzy, intertwined, hinterlands. The paper argues for the need of a more system-wide coordination and collaboration among ports, aiming to avoid overcapacity; duplication of scarce resources; low return on investment and, in general, wasteful competition. It is hoped that our analysis and ensuing recommendations will help other countries, port policymakers and stakeholders, to better understand, and thus exploit, the economic levers which shape the evolution of ports in proximity

A dynamic data-driven application simulation model for oil spill emergency decision in port water area

In order to solve the difficulty in simulation and prediction the evolution of oil spill incidents in port water area caused by system complexity and environment variety, a simulation model based on Dynamic Data-Driven Application System (DDDAS) was developed. By assimilating real-time data, simulation model can update its initial condition and solutions, and modify its parameters. Firstly, the framework for DDDAS-based simulation model was designed. Then the method for mapping of real-time data to simulation model, the method for recovery of initial data for DDDAS, and the algorithms to simulate the behavior of oil spill were studied. Finally, numerical tests were provided to illustrate the validity of the proposed model. Results indicate that the DDDAS-based simulation method can improve the prediction accuracy of oil spill incidents. First published online: 16 Oct 201

Optimization Model for Truck Appointment in Container Terminals

AbstractMany ports are facing heavy truck congestion in the terminal, which leads to longer truck waiting time and lower operation efficiency. To alleviate congestion and decrease truck turn time in the container terminal, an optimization model for truck appointment was proposed in this paper. In the model, the appointment quota of each period was optimized subject to the constraints of adjustment quota. And a BCMP queuing network was developed to describe the queuing process of trucks in the terminal. To solve the model, a method based on Genetic Algorithm (GA) and Point wise Stationary Fluid Flow Approximation (PSFFA) was designed. GA was used to search the optimal solution and PSFFA was designed to calculate the truck waiting time. Finally, numerical experiments were provided to illustrate the validity of the model and algorithm. The results indicate that the proposed PSFFA method can estimate the queue length accurately and the model can decrease the truck turn time efficiently

Models and algorithms for multi-crane oriented scheduling method in container terminals

"Multi-crane oriented" is a scheduling method that yard trailers can be shared by different quay cranes. In this paper, two models for this problem are developed. The first one is a model for an inter-ship-based sharing method. In this model, yard trailers can be shared by quay cranes of different ships. To solve the model, a two-phase Tabu search algorithm is designed. The second one is a model for a ship-based sharing method. In this model, yard trailers can only be shared by quay cranes of the same ship. Q-learning algorithm is designed to solve the model. Numerical tests show that the "multi-crane oriented" method can decrease the yard trailers' travel distance, reduce the disequilibrium of different working lines, and thus improve the operation efficiency in container terminals.Container terminals Multi-oriented scheduling method Yard trailer scheduling Q-learning algorithm