Asia-Pacific Journal of Operational Research

Port state control (PSC) inspection contributes a lot to improving maritime safety and protecting the marine environment. After selecting the ships coming to a port for inspection, one critical challenge faced by the PSC authorities is deciding what deficiency items should be inspected and what the inspection sequence of these items is. To address this problem, two innovative and high-efficient PSC inspection schemes describing specific PSC inspection items and sequence are proposed for the inspectors’ reference when time and resources are limited, especially when there are difficulties in estimating the possible deficiencies in advance. Both schemes take the occurrence probability, inspection cost, and ignoring loss of each deficiency item into account. More specifically, the first inspection scheme is based on the occurrence probabilities of the deficiency items in the whole data set, while the second scheme further considers the correlations among the deficiency items extracted by association rules. The results of numerical experiments show that the efficiency of the two proposed inspection schemes is 1.5 times higher than that of the currently used inspection scheme. In addition, the second inspection scheme performs better than the first inspection scheme, especially with inspecting ships with no less than five deficiency items and limited inspection resources.

This paper considers the ship size optimization problem for a liner shipping company that provides feeder service between one hub port and one feeder port. In the maritime market with uncertainty, this problem becomes more challenging. This research first analyzes the decision behaviors of the shipping company. Then, a stochastic dynamic programming method is proposed to calculate the expected total volume of containers transported within the planning horizon. Using the calculated volumes as input parameters calculate the profit of each ship sizes and then determine the suitable ship size for the feeder route. Numerical experiments are performed to validate the effectiveness of the proposed method and the efficiency of the proposed algorithm.

Motivated by real needs of the industry, this paper studies a maritime pilot scheduling problem with working hour regulations. The existing traditional manual scheduling method of the targeted pilot station is actually a greedy-based approach which may lead to an extremely high operating cost solution and also some dissatisfaction of pilots. A mixed-integer programming (MIP)-based formulation is established and a variable neighborhood search (VNS) approach is proposed to solve the problem efficiently. Compared to the traditional scheduling method, the VNS approach significantly reduces the operating costs in different scales of realistic instances. At the same time, working time and working timespan of pilots are also prominently reduced, which hence directly reduce the workloads of pilots and improve their job satisfaction.

This paper studies the subject credit risk of Chinese port enterprises. Since the impact of cash flow ability on credit risk measurement will be increased under extreme case, ordinary logistic regression methods may lack explanatory power for port enterprise default under extreme cases. Considering the characteristics of cash flow in port industry, we introduce the constrained logistic regression method to establish a default probability model which can describe the credit risk level of the industry with higher accuracy in the extreme case where an enterprise’s quick ratio is lower than a cutoff point, For empirical study, we leverage the data of more than 900 companies in port and transportation industry in 2016–2018. The constrained logistic regression splits the data into two subspaces based on quick ratios with the cutoff of 1.8. Then logistic regression is built on the two subspaces, respectively. The recall ratios show that the constrained logistic regression method performs better than the ordinary logistic regression on the study of corporate default probability in port and transportation industry.

This paper proposes a blockchain-based framework to improve the efficiency of ship traffic in port. In the framework, ship agents, terminals, tug company, pilot station, and government share information and the information is stored in a blockchain. Based on the shared information, we discuss three categories of data-driven models that can improve the operations management of the above five parties. The first category is decisions made by a single party. The second category involves decisions of at least two ship agents. The third category relates to multi-party decision-making under uncertainty. This study hopes to stimulate maritime practitioners to embrace blockchain technology and data-driven approaches to enhance the competitiveness of the industry.

In recent years, the bottleneck of yard operation in container terminals has become more and more prominent. For resolving the departure delay problem of vessels, this paper proposes a mixed integer programming model considering the operation time requirements of vessels in a container terminal. It can optimize the yard template plan by reasonably arranging the number of loading clusters of each vessel in the yard, and meanwhile, enhance the efficiency of management in yard and lower the departure delay of vessels. Moreover, a case study is presented to illustrate the proposed model and sensitivity analysis is conducted in the numerical experiments to verify the effectiveness of the proposed model.

This paper studies a consolidation center selection and cargo transportation optimization problem of the Sino-Europe railway express. It takes account of demand uncertainty and multiple cost factors, such as transportation cost, time cost, waiting cost before consolidation, penalty cost of not full load after distribution, track changing cost, and customs clearance fee. A complicated mixed-integer programming (MIP) model is presented to describe the problem. Then a solution method based on binary particle swarm optimization (BPSO) is designed to solve the model of different scales of cases. Extensive numerical experiments generated from real-world data are conducted to validate the effectiveness of the proposed model. Moreover, we compare the efficiency of the solution method with a commercial solver and a heuristic algorithm based on variable neighborhood search (VNS). Results show that our solution method can optimally solve the problem in small-scale cases and get near-optimal solutions for large-scale problem instances. The proposed mathematical model and the calculation results may provide valuable insights for both business owners and government.

Strict limits on air emissions released by burning fuel of ships have recently been imposed in Emission Control Areas. Retrofitting ships to use Liquefied Natural Gas (LNG) as a ship fuel is one of the methods for liner companies to comply with global regulations. Since the LNG retrofitting investment is expensive, it is likely that liner companies need a more scientific methodology to evaluate the LNG for propulsion of ships. Liner companies are particularly interested in their profits determined by the pricing decision. Based on the evolutionary game theory, this paper develops a mathematical model for the pricing problem with the consideration of market competition, LNG investment, and price elasticity. We also consider several realistic factors, such as the demand sensitivity for LNG effort, to make the proposed methodology meet the realistic demands when more and more liner companies need a revolutionary transformation because of the stricter regulations. Numerical experiments, including a simulated-data case, are performed to validate the proposed methodology. Some managerial insights are concluded according to the computational experiments and sensitivity analysis.

This paper investigates the integrated planning problem of fleet deployment considering demand uncertainty for container shipping liners. In the daily operation of container shipping networks with multiple routes, the demand of container transportation may exceed the capacity on some routes due to demand uncertainty. Shipping liners can apply temporary chartering and transshipment strategies to get extra capacity so that the excessive demand can be met. To formulate the problem, this paper develops a mixed-integer linear programming model to determine the fleet deployment, service scheduling, container routing and temporary chartering considering demand uncertainty. An improved particle swarm optimization algorithm is designed for solving this problem in large-scale cases. Numerical experiments show that the algorithm can solve the problem with high efficiency. This paper provides shipping liners with an integrated decision support tool for fleet deployment.

With the economic growth and prosperity, ports and shipping play an increasingly important role in the national economy and international trade. Numerous studies related to the field of port and shipping were conducted to propose some managerial insights. This paper uses the bibliometric method to analyze the research papers published from 2000 to 2019 on port and shipping in the field of management science. The number of published papers, impact score and cite score are identified as evaluation criteria to analyze the research contributions of researchers, affiliated institutions and countries/regions in the port and shipping field. At the same time, the importance of each keyword can be obtained by the bibliometric method, which may help new researchers grasp research hotspots and trends in related fields.