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The purpose of this paper is to study the impacts of time segment modeling approach for berth allocation and quay crane (QC) assignment on container terminal operations efficiency.

The authors model the small time segment modeling approach, based on minutes, which can be a minute, 15 min, etc. Moreover, the authors divided the problem into three sub-problems and proposed a novel three-level genetic algorithm (3LGA) with QC shifting heuristics to deal with the problem. The objective function here is to minimize the total service time by using different time segments for comparison and analysis.

First, the study shows that by reducing the time segment, the complexity of the problem increases dramatically. Traditional meta-heuristic, such as genetic algorithm, simulated annealing, etc., becomes not very promising. Second, the proposed 3LGA with QC shifting heuristics outperforms the traditional ones. In addition, by using a smaller time segment, the idling time of berth and QC can be reduced significantly. This greatly benefits the container terminal operations efficiency, and customer service level.

Nowadays, transshipment becomes the main business to many container terminals, especially in Southeast Asia (e.g. Hong Kong and Singapore). In these terminals, vessel arrivals are usually very frequent with small handling volume and very short staying time, e.g. 1.5 h. Therefore, a traditional hourly based modeling approach may cause significant berth and QC idling, and consequently cannot meet their practical needs. In this connection, a small time segment modeling approach is requested by industrial practitioners.

In the existing literature, berth allocation and QC assignment are usually in an hourly based approach. However, such modeling induces much idling time and consequently causes low utilization and poor service quality level. Therefore, a novel small time segment modeling approach is proposed with a novel optimization algorithm.

Many e-commerce companies adopt a product customization platform offering various choices for customers to configure products to better satisfy their needs. However, a method to effectively measure customer satisfaction is lacking. This paper aims to investigate customers' perception of time in the online configuration process of customized products and seeks to propose time perception as the measurement of the effectiveness of online product customization.

An online laptop customization system was used in an empirical experiment to collect respondents' answers in a set of research questions. Regression and correlation analysis were conducted to investigate the factors affecting customers' satisfaction as well as the relationships with time perception.

The experimental results reveal several factors in customers' perception of time during the online product customization process. First, customers tend to overestimate the amount of time spent in a short-duration task but underestimate the amount of time spent in a long-duration task. Second, customers' perceptions of time are significantly correlated with their satisfaction with the configured products, and perceived time is moderately correlated with their satisfaction with the configuration process. Third, the difficulty of customization tasks and customers' motivation to process information also significantly affect customers' perceptions of time.

This paper advances the research on time perception by developing a new relative segmentation-based method to estimate the subjective perception of time. This study also makes several contributions to product customization research: the authors fill a research gap in the field of product customization by incorporating customers' perceptions of time into the measurement of customer satisfaction and by identifying the significant relationships among customers' perception of time, the ease of task selection, the customers' motivation to process information, and customers' satisfaction with customized products. These results aid in the design of online product customization systems.

Risk management is crucial for all organizations, especially those in the global supply chain network. Failure may result in huge economic loses and damage to company reputation. Risk assessment usually involves quantitative and qualitative decisions. The purpose of this paper is to apply fuzzy logic to capture and inference qualitative decisions made in the House of Risk (HOR) assessment method.

In the existing HOR model, aggregate risk potential (ARP) is calculated by the risk event times the risk agent value and its occurrence. However, these values are usually obtained from interviews, which may involve subjective decisions. To overcome this shortcoming, a fuzzy-based approach is proposed to calculate ARP instead of the current deterministic approach.

Risk analyses are conducted in five major categories of risk sources: internal, global environment, supplier, customer and third-party logistics provider. Moreover, each category is further divided into different sub-categories. The results indicate that the fuzzy-based HOR successfully inferences the inputs of the risk event, risk agents and its occurrence, and can prioritize the risk agents in order to take proactive decisions.

The proposed fuzzy-based HOR model can be used practically by manufacturers in the global supply chain. It provides a framework for decision makers to systematically analyze the potential risks in different categories.

The proposed fuzzy-based HOR approach improves the traditional approach by more precise modeling of the qualitative decision-making process. It contributes to a more accurate reflection of the real situation that manufacturers are facing.

This paper aims to build a novel model and approach that assist an aircraft MRO procurement and overhaul management problems from the perspective of aircraft maintenance service provider, in order to ensure its smoothness maintenance activities implementation. The mathematical model utilizes the data related to warehouse inventory management, incoming customer service planning as well as risk forecast and control management at the decision-making stage, which facilitates to alleviate the negative impact of the uncertain maintenance demands on the MRO spare parts inventory management operations.

A stochastic model is proposed to formulate the problem to minimize the impact of uncertain maintenance demands, which provides flexible procurement and overhaul strategies. A Benders decomposition algorithm is proposed to solve large-scale problem instances given the structure of the mathematical model.

Compared with the default branch-and-bound algorithm, the computational results suggest that the proposed Benders decomposition algorithm increases convergence speed.

The results among the same group of problem instances suggest the robustness of Benders decomposition in tackling instances with different number of stochastic scenarios involved.

Extending the proposed model and algorithm to a decision support system is possible, which utilizes the databases from enterprise's service planning and management information systems.

A novel decision-making model for the integrated rotable and expendable MRO spare parts planning problem under uncertain environment is developed, which is formulated as a two-stage stochastic programming model.