Interactive Web-Based Simulation for Supply Chain Education

Knowledge economies are based on the concept of knowledge transfer between education institutes and stakeholders. In response, the significant move away from traditional teaching methods to the emerging paradigm of eLearning has become increasingly in evidence across many academic disciplines. To assist in the transition from traditional learning to eLearning, more interactive and virtually orientated teaching aids are needed. This chapter introduces a simulation-based learning framework that integrates web-based simulation and a web content management hierarchy model. Using the complex subject of supply chain management as a case study, the new framework allows users to examine various real-life strategic management scenarios, encourages group work and has remote access capabilities for distance learning. Interactive learning is facilitated using the web-based simulation portal, enabling instructors to demonstrate the complexity of decisions in multiple criteria environment and also show the users the impact of strategies on performance. Supply chain simulation creates an animated experience and better understanding of system dynamics including risk. The framework assists in the knowledge and skills transfer between third-level education institutes and their stakeholders, primarily industry partners and the wider communit

Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments

Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems. However, existing efforts have focused on key generation for users in a specific environment where the training samples and test samples obey the same distribution, which is unrealistic for real world applications. This paper formulates the PKG problem in multiple environments as a learning-based problem by learning the knowledge such as data and models from known environments to generate keys quickly and efficiently in multiple new environments. Specifically, we propose deep transfer learning (DTL) and meta-learning-based channel feature mapping algorithms for key generation. The two algorithms use different training methods to pre-train the model in the known environments, and then quickly adapt and deploy the model to new environments. Simulation results show that compared with the methods without adaptation, the DTL and meta-learning algorithms both can improve the performance of generated keys. In addition, the complexity analysis shows that the meta-learning algorithm can achieve better performance than the DTL algorithm with less time, lower CPU and GPU resources

Group-Level Exploration and Exploitation: A Computer Simulation-Based Analysis

Organisational research has studied the tension between exploration and exploitation for years. In essence, this body of research agrees on the necessity of a balance between explora-tive and exploitative processes to prevent an organisation from falling into a learning trap. Thus, to enhance the active management of this balance in organisations, a deeper theoretical understanding of the factors that influence the development of exploration and exploitation has to be gained. One of the recently discussed factors is the interplay between exploration and exploitation on different organisational levels. This paper picks up this discussion. It pro-vides an in-depth, computer simulation-based analysis of the performance of organisational types with varying degrees of within-group and between-group exploration and exploitation in situations of different degrees of task complexity. The findings indicate that a high share of between-group processes as compared to within-group processes positively influences the organisational performance level and that dependent on task complexity the optimal share of exploration and exploitation varies.Organisational Learning, Experience-Based Learning, Exploration, Exploitation, Knowledge Management, Genetic Algorithms

A virtual environment to support the distributed design of large made-to-order products

An overview of a virtual design environment (virtual platform) developed as part of the European Commission funded VRShips-ROPAX (VRS) project is presented. The main objectives for the development of the virtual platform are described, followed by the discussion of the techniques chosen to address the objectives, and finally a description of a use-case for the platform. Whilst the focus of the VRS virtual platform was to facilitate the design of ROPAX (roll-on passengers and cargo) vessels, the components within the platform are entirely generic and may be applied to the distributed design of any type of vessel, or other complex made-to-order products

Active Learning: Effects of Core Training Design Elements on Self-Regulatory Processes, Learning, and Adaptability

This research describes a comprehensive examination of the cognitive, motivational, and emotional processes underlying active learning approaches, their effects on learning and transfer, and the core training design elements (exploration, training frame, emotion-control) and individual differences (cognitive ability, trait goal orientation, trait anxiety) that shape these processes. Participants (N = 350) were trained to operate a complex computer-based simulation. Exploratory learning and error-encouragement framing had a positive effect on adaptive transfer performance and interacted with cognitive ability and dispositional goal orientation to influence trainees’ metacognition and state goal orientation. Trainees who received the emotion-control strategy had lower levels of state anxiety. Implications for developing an integrated theory of active learning, learner-centered design, and research extensions are discussed

Knowledge transfer in a tourism destination: the effects of a network structure

Tourism destinations have a necessity to innovate to remain competitive in an increasingly global environment. A pre-requisite for innovation is the understanding of how destinations source, share and use knowledge. This conceptual paper examines the nature of networks and how their analysis can shed light upon the processes of knowledge sharing in destinations as they strive to innovate. The paper conceptualizes destinations as networks of connected organizations, both public and private, each of which can be considered as a destination stakeholder. In network theory they represent the nodes within the system. The paper shows how epidemic diffusion models can act as an analogy for knowledge communication and transfer within a destination network. These models can be combined with other approaches to network analysis to shed light on how destination networks operate, and how they can be optimized with policy intervention to deliver innovative and competitive destinations. The paper closes with a practical tourism example taken from the Italian destination of Elba. Using numerical simulations the case demonstrates how the Elba network can be optimized. Overall this paper demonstrates the considerable utility of network analysis for tourism in delivering destination competitiveness.Comment: 15 pages, 2 figures, 2 tables. Forthcoming in: The Service Industries Journal, vol. 30, n. 8, 2010. Special Issue on: Advances in service network analysis v2: addeded and corrected reference