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This paper aims to conduct a review on cruise shipping and the cruise shipping industry. The current trends are analyzed for the industry, showing that this industry is still young and has great potential to boom. Meanwhile, the Asia market is the fastest growth region among the global, to which increasing cruise ships are repositioned by major cruise lines. However, for such a promising industry, the literature review on some state-of-the-art research works suggests that the research works that have been conducted for the cruise shipping are quite limited, and the majority of the works belongs to empirical and descriptive studies, which does not provide optimization-based quantitative analysis on some operation planning problems.

This paper conducts a review on cruise shipping and the cruise shipping industry.

In reality, there are some important operation planning problems faced by cruise lines or cruise ships, and four of them are addressed in this paper in response to the fast growing trend. The addressed operation planning problems are cruise fleet management, cruise ship deployment, cruise itinerary design and cruise service planning.

The importance and the attention for the research on the problems are also elaborated in the paper.

Corporate travel represents a significant source of revenue for the tourism industry. Therefore, the quality of service is essential for maintaining and expanding corporate cliental bases. Despite the importance, the extant literature has yet sufficiently examined corporate travel service quality (SQ) and its impact. To make up for the drawback, this study aims to differentiate the impact of SQ perceptions on customer satisfaction between the online and off-line contexts through an empirical investigation in one of the top five corporate travel agencies in North America.

The well-established SERVQUAL measurement is applied in differentiating the impact of SQ dimensions between the online and off-line context. To empirically test the proposed corporate travel agency (CTA) SQ conceptual model, a set of survey data of “Welcome Back Survey” from HRG (a top five CTA in North America) was examined.

The study finds that for online services, assurance, responsiveness and empathy affect perceived SQ, whereas for off-line services, assurance, empathy and tangible are the three dimensions of perceived SQ.

By relying on the existing survey, the off-line context has one less dimension than the online context. Yet as an early effort in differentiating the differences in the impact of SQ between two service contexts, the study offers insightful findings.

The findings will be helpful for business managers of CTAs to identify the factors that influence SQ in both online booking and off-line booking context. In particular, assurance and empathy are two dimensions that exert a significant impact on customer satisfaction.

This paper is the first to compare the differences of the SQ of online and off-line corporate travel.

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

The purpose of this paper is to investigate the forces driving organizational innovation, particularly CEO transformational leadership as it affects external and internal social capital in top management teams.

Survey questionnaires were administered to 90 Chinese top management teams. Structural equation modeling was used to test the hypothesized relationships.

Both internal and external social capital mediated the relationship between transformational leadership and organizational innovation.

Organizations should strengthen internal and external capital of top management teams to reap maximal innovation outcomes from transformational leadership.

The findings contribute to the transformational leadership, social capital, and innovation literature first by showing how leadership influences innovation through largely neglected mechanisms – internal and external social capital. Second, a social capital focus challenges the tacit assumption that transformational leadership has only internal influences by showing that it potentially spills over to the external domain.

The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost.

A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search.

Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space.

This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured.

The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases.

The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.