Architecture of a Knowledge-Based Education System for Logistics
Nowadays the technologies develop so fast that people cannot keep pace with the increasing logistics knowledge. In order to provide a professional platform of learning modern logistics, moreover, to shorten learning time and improve the learning efficiency, this paper introduces a knowledge-based education system which provides different user groups with well-formed learning contents. The architecture of the system as well as its four main modules is the focus of the paper. Additionally, the learning strategies and the mechanism of formulating learning plan are interpreted. It is a good foundation for the future knowledge-based education system in the logistics and supply chain management.
KeywordEducation system Logistics SCM Knowledge base Knowledge-based education system
Nowadays, the economics develops increasingly fast and the demand on goods exchange grows also quickly. In order to adapt to different application environment in practice, logistics systems with more advanced technologies are deployed, algorithms with higher complication are used for the planning of logistics systems and numerous logistic strategies emerge. Under this circumstance, the data and information as well as the knowledge and experiences in the area of logistics increases continuously day by day.
Whilst data and information are surely without effort to be stored, the knowledge and experience differing from people are hardly to keep. A DIKW Chain is mentioned in  to reveal the differences between data, information, knowledge and experiences. Thus, it is necessary to find a proper solution to maximize the utilization of knowledge and experiences. In such sense, the knowledge-based system (KBS), which can collect, store and manage organized knowledge, is appropriate to support the purpose. In , several relevant technologies for modeling, managing and representing knowledge in KBS are investigated and presented.
The application of KBS has many successful examples in the area of medicine, agriculture and manufacturing already. In this paper, however, it focuses more on applying KBS in education. Knowledge and experiences should not merely be kept and used. More important, they should be passed on to the next generation. In , architecture of an E-learning system in manufacturing industry is introduced. With manufacturing training materials, scenario case base and problem solving methods being provided, the system educate the employees to ensure their continuous competence in handling their task. In addition, an educational system based on semantic web technology is written in . This technology provides more adaptability, robustness and richer learning environments in building KBS. Moreover, a Web-based education system with the purpose of creating Web learning environment is proposed in . The integrated tools with Web Services have a characteristic of powerful adaptability for management, authoring, delivery and monitoring of learning content. Furthermore, KBS is also applied in logistics. A knowledge-based logistics strategy system referred in  is designed for supporting the logistics strategy development stage by retrieving and analyzing useful knowledge and solutions.
However, there exists little research on applying KBS as educational system for logistics. Combined with the idea of customizing learning contents and process, as described in [7, 8], we strive to put forward a kind of KBS which serves to educate people with personalized logistics knowledge. Firstly, we focus on depicting the architecture of the knowledge-based education system for logistics in Sect. 337.2. Then in Sect. 337.3, the application process of the system will be elaborated with an illustration. Afterwards, characteristics of this system will be portrayed. At last, results of applying KBS with a case will be presented, in order to verify the feasibility of the system.
337.2 Knowledge-Based Education System for Logistics
The purpose of building Knowledge-based Education System for Logistics (KESL) is to educate different user groups with logistic knowledge. Based on a Knowledge Base (KB), which contains full of educational materials of logistics, we strive to organize these materials strategically and then present the knowledge in a way that learners can assimilate them on their best performance.
337.2.1 Architecture of KESL
3184.108.40.206 Module 1 Entrance
Options and attributes for classifying different groups of people
Student, teacher, company personnel, manager, specialist, common people, …
<18, 18–24, 24–30, 30–50, > 50
Be engaged in
Economics, finance, journalism, engineering mechanics, material science, supply chain management, …
No foundation, common knowledge, broad vision, specialized in (), …
Purpose of learning
Interests, brief understanding, basic skills, additional knowledge for main major, practical knowledge for work, teaching, know-how, …
3220.127.116.11 Module 2 User Management
In this module, the Account Administration acts like a transit depot between users and message processing. On the one hand, it receives profile configurations from module 1 and obtains users’ feedbacks from module 4; on the other hand, it delivers feedbacks to Strategy Picker every time users add some feedback during learning. This operation repeats continuously and it forms a closed-loop feedback system, so that the Strategy Picker could be immediately updated and always ready to formulate an adapted learning plan. Moreover, to achieve effective knowledge storage, a multi-dimensional database (MDB) technology could be used, which is proposed in . It facilitates the knowledge representation, navigation and maintenance with a high performance level. Furthermore, the process of storing knowledge is also a process of establishing clusters. It enables data mining for potential usage.
The History Navigator in this module relates to record the learning process for every user. These recordings are stored in personal account. They will be utilized afterwards in the process of reviewing at the beginning of the next round of learning.
Additionally, the Template Database stores kinds of interface styles. Users can define their own style and it will be saved in personal account.
318.104.22.168 Module 3 Knowledge Center
The Knowledge Center is composed of Strategy Picker and Knowledge base. It receives different information from other modules as constraints and according to them then formulates the appropriate learning strategy and pick up the corresponding educational materials.
Layers of the knowledge base and contents in each layer
Terms about logistics and SCM, PPT and PDF files.
Explanation of logistics systems with picture, video, 3D-animation., planning procedure, Best practices
Subsystems and equipment
Container and pallets, rack system, forklift, conveyors, sorter etc.
Functions and principles
FIFO, LIFO, location management, route control, transport regulations and roles, layout plan principles
Algorithms and methods
Performance calculation, dimensioning, simulation etc.
Additionally, in order to make sure that all the knowledge objects can be picked out appropriately, they must be assigned several attributes. These attributes are previously given by the knowledge base designer and will be matched with constraints from users. They can describe the location of every educational material, reveal a fuzzy relationship between knowledge objects or indicate the abstraction level of a knowledge object and so on.
Expanding Learning: The learner learns the knowledge, which is distributed in the same layer, to broaden his horizon. The mechanism of searching learning material is to find the knowledge whose location label contains the same layer index.
Top-down Learning: The learner learns the knowledge in a specific domain from basis to proficiency. The mechanism of searching learning material is to find the knowledge whose location label contains the same domain index.
Knowledge-centered Learning: Having learned a specific knowledge, the learner start to study other related knowledge in order to strengthen the comprehension of the centered-knowledge. The mechanism of searching learning material is to find the knowledge which have a better fuzzy correlation with the focused knowledge and whose location label can be in any domain.
Applying a type of learning strategy indicates a definitive knowledge retrieval path. Based on the location label and fuzzy relationship, the knowledge retrieval process is performed by inductive indexing approach and the nearest neighbor algorithm along with the researching path. Since the search direction and connection between knowledge have been determined, a series of educational materials will be selected out after traversal.
322.214.171.124 Module 4 Learning Platform
The users can learn knowledge audiovisually from Learning Platform. The learning process consists of reviewing and learning.
In this article, we suggest that a reviewing process being introduced. According to Ebbinghaus forgetting curve, people forget more than 60 % of their learning things after 1 day. It is of little significance when people learn the knowledge only once. Therefore, we bring in an interactive process during learning activities, namely, the feedback from users about how difficult they feel about the content and how confident they feel they had understood the knowledge. The knowledge with higher difficulty or with lower score of understanding is prior to be reviewed.
- Passive learning
Receive knowledge material on screen
Go along with the instruction till to the end
- Positive learning
Knowledge Map: well-constructed knowledge links in a graph on which man can select whatever he wants to learn.
Search Engine: always ready to retrieve knowledge
- Interactive learning
Scoring: the user marks a knowledge unit with a score in certain area, so that the system response to modify its operation
Notebook: the user takes note for the learned knowledge and writes it on the board, in order to recall the memories by reviewing in the next round of learning. In this way, the efficiency of learning improves obviously.
337.3 Application Process
337.4 Result Analyses
At first, the users input their profile information into the system by Entrance, which is not illustrated in this paper. The options of user profile are presented in Table 337.1. For every user, the profile configuration can only load in the first time, this process is not necessary later. In this case, the name of the learner is Wang Jianguo, the age 29 years old and so on.
KESL works with several characteristics. Knowledge materials in KBS can be added, maintained and updated to adjust to changes. Meanwhile, the learning strategy is well calculated and can be optimized by users’ feedback continuously. Different from a traditional educational system, KESL has also a review process which could improve the efficiency and effectiveness of user’s learning. In addition, the capability of customizing the learning interface could provide the users with good learning experience and keep them from the impression of boring learning. Furthermore, a direct-viewing demonstration of learning outcome could deepen their understanding of their efforts.
KESL is a professional frame for the further development of the knowledge-based education system in the logistics and supply chain management. The new knowledge can be easily integrated. The next development steps could be the application for smart phones and smart TV.
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