This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of makespan, waiting time, and degree of deviation from a predetermined priority schedule. These objectives represent the interests of both port and ship operators. Unlike most existing approaches in the literature which are single-objective-based, a multiobjective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with three primary features which are specifically designed to target the optimization of the three objectives. The features include a local search heuristic, a hybrid solution decoding scheme, and an optimal berth insertion procedure. The effects that each of these features has on the quality of berth schedules are studied.

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Context-aware applications are required to be aware of user context and ambient intelligent to support nonintrusive humancomputer interaction. However, the uncertain real-world environments make it difficult for a system to perceive enough environmental contexts and achieve user's goal. Therefore, this study proposes a framework for developing an NFC-enabled intelligent agent, which combines the NFC technique with context-acquisition, ontology-knowledgebase, and semantic-adaptation modules to be aware of location, time, device, and activity contexts with respect to personal and social profiles. To cope with the uncertain environment, a credit-based incentive scheme is also proposed to encourage social cooperation and thereby enlarge the value of personal perceptions. By developing a complete ontology knowledgebase, the proposed framework can incorporate with social-cooperation schemes to recommend relevant services for supporting reactive action, proactive achievement, and social cooperation. The resultant social-advertising system shows that this framework can support a wide-range of different functionalities and is indispensable to an NFC-based intelligent agent for social Internet of things.

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Multicast routing is an effective way to transmit messages to multiple hosts in a network. However, it is vulnerable to intermittent connectivity property in mobile ad hoc network (MANET) especially for multimedia applications, which have some quality of service (QoS) requirements. The goal of QoS provisioning is to well organize network resources to satisfy the QoS requirement and achieve good network delivery services. However, there remains a challenge to provide QoS solutions and maintain end-to-end QoS with user mobility. In this paper, a novel penalty adjustment method based on the rough set theory is proposed to deal with path-delay constraints for multicast routing problems in MANETs. We formulate the problem as a constrained optimization problem, where the objective function is to minimize the total cost of the multicast tree subject to QoS constraints. The RPGA is evaluated on three multicast scenarios and compared with two state-of-the-art methods in terms of cost, success rate, and time complexity. The performance analyses show that this approach is a self-adaptive method for penalty adjustment. Remarkably, the method can address a variety of constrained multicast routing problems even though the initial routes do not satisfy all QoS requirements.

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Generation Z students have their learning preferences. They like to learn independently, advocate for what they believe in, and work hard to achieve their goals. However, there are significant gaps between Generation Z students’ expectations for learning and prior experiences, especially for three domains of motivation in online learning environments: relatability, affirmation, and opportunity. This study aims at exploring the effectiveness of a progressive teaching method designed for Generation Z students in computer networking courses. This study proposes a progressive three-stage teaching method that gradually implements traditional lecture, individual flipped learning, and cooperative flipped learning methods over a semester. The design principle of this study differs from most existing studies that focus on the effectiveness of specific teaching methods. This study encourages each student to learn sequentially through three teaching stages. The purpose of this study is to investigate the changes in students’ learning experiences, particularly in terms of learning comprehension and learning motivation. The research results show that the proposed progressive teaching method can improve students’ understanding of computer networking courses and enhance their learning motivation. Participants agreed that the proposed progressive pedagogy can improve their teamwork skills and provide a different learning experience in the computer networking courses.