Heuristic Solutions for Loading in Flexible Manufacturing Systems

Production planning in flexible manufacturing system deals with the efficient organization of the production resources in order to meet a given production schedule. It is a complex problem and typically leads to several hierarchical subproblems that need to be solved sequentially or simultaneously. Loading is one of the planning subproblems that has to addressed. It involves assigning the necessary operations and tools among the various machines in some optimal fashion to achieve the production of all selected part types. In this paper, we first formulate the loading problem as a 0-1 mixed integer program and then propose heuristic procedures based on Lagrangian relaxation and tabu search to solve the problem. Computational results are presented for all the algorithms and finally, conclusions drawn based on the results are discussed

Batching in Production Planning for Flexible Manufacturing Systems

Generally, production planning in flexible manufacturing systems is hierarchically grouped into two subproblems: batching and loading. These two subproblems can be solved either sequentially or simultaneously to generate a feasible production plan. This paper focuses on the batching problem which partitions the given production order of part types into batches that can be processed with the limited production resources such as the capacity of the tool magazines, pallets, fixtures and available machine time. A 0–1 integer program is formulated to address the batching problem and a simulated annealing algorithm is proposed for solving it. A systematic computational test is conducted to test the performance of the proposed algorithm. The results show that the simulated annealing algorithm can provide high-quality solutions in a reasonable amount of time for practical size problems

Service Innovation and Quality Assessment of Industry 4.0 Microservice through Data Modeling and System Simulation Evaluation Approaches

This study proposes a system construction approach under Industry 4.0 infrastructure that is validated by the proposed framework of microservice quality assessment with framework with data modeling and simulation methodology to achieve innovation and value cocreation goals. The framework, which combines a dynamic process flow with service-dominant logic design and reliability assessment using a multilayer perceptron (MLP) prediction model can assist decision makers in optimizing their service innovation and decision-making processes. The service innovation and evaluation approaches have implications for optimizing the corporation cooperation. The corporation can form a much more comprehensive manufacturing infrastructure or system by considering the requirements and assessment results of third parties. To help the corporation redefine its value proposition and system structure, we must examine the system interaction between different hierarchical layers within the Industry 4.0 system infrastructure. This study used a production dataset from the NASDAQ-listed electronics corporation and two top German and Japanese automobile firms. The proposed system framework had already been validated and introduced to improve 12% of service quality. The system integrated with anticipated functions will accelerate service innovation and optimization by combining MLP and Kaplan–Meier estimation methodologies by extracting the characteristics of realistic datasets

Application of Topic Map on Knowledge Organization

事實上，很多企業不是沒有知識庫或資料倉儲，而是知識庫太繁雜，以致在需要時無法適當地取得資料；再加上網際網路的興起，網路上龐大的、未經組織與分類的、及高重複性的資料特性使得資料擷取問題更加複雜。透過一般常用的搜尋引擎（如：google）會搜尋到上千筆的資料。對於使用者而言，瀏覽超過數百萬個網頁來尋找相關的資料是一項沉重的負擔，而目前已開發的搜尋系統並無法確切地滿足使用者的需求。資訊超載的情況，使得人們無法有效地進行資料搜尋，有必要利用資訊技術來尋找相關且高品質得資訊。然而，僅藉由搜尋引擎來尋找知識是不足的，因為即使目前大部份的搜尋引擎都有提供依相關性排序及本文摘要的功能。通常使用者還是得透過搜尋引擎尋找數次、瀏覽許多不必要的網頁之後才能找到所需的資料，而非一次就能完成。因此本研究的主要目的，在於介紹如何利用文字探勘來發現蘊藏在大量中文文件中的知識。本文也將深入探討此技術的各項主要元件。透過主題地圖的實證研究，我們將製作兩類的主題地圖，分別是顯性知識（臺灣證券暨期貨法令資料）及隱性知識（王永慶思想哲學）。藉由這兩個地圖的比較來探討顯性知識與隱性知識在主題地圖的呈現上所發現的問題。Knowledge management (KM) has received much attention from both academics and practitioners in the past few years. Following the KM trend, many organizations have built their own knowledge repositories or data warehouses. However, information or knowledge is still scattered everywhere without being properly managed. The rapid growth of the Internet accelerates the creation of unstructured and unclassified information and causes the explosion of information overload. The effort of browsing information through general-purpose search engines turns out to be tedious and painstaking. Hence, an effective technology to solve this information retrieval problem is much needed. The purpose of this research is to explore the application of text mining technique in organizing knowledge stored in unstructured natural language text documents. Major components of text mining techniques required for topic map in particular will be presented in detail. Two sets of unstructured documents are utilized to demonstrate the usage of SOM for topic categorization. The first set of documents is a collection of speeches given by Y.C. Wang, Chairman of the Taiwan Plastics Group, and the other is the collection of all laws and regulations related to securities and future markets in Taiwan. We also try to apply text mining to these two sets of documents to generate their respective topic maps, thus revealing the differences between organizing explicit and tacit knowledge as well as the difficulties associated with tacit knowledge

Application of Topic Map on Knowledge Organization

事實上，很多企業不是沒有知識庫或資料倉儲，而是知識庫太繁雜，以致在需要時無法適當地取得資料;再加上網際網路的興起，網路上龐大的、未經組織與分類的、及高重複性的資料特性使得資料擷取問題更加複雜。透過㆒般常用的搜尋引擎（如： google）會搜尋到上千筆的資料。對於使用者而言，瀏覽超過數百萬個網頁來尋找相關的資料是㆒項沉重的負擔，而目前已開發的搜尋系統並無法確切滿足使用者的需求。資訊超載的情況，使得人們無法有效地進行資料搜尋，有必要利用資訊技術來尋找相關且高品質的資訊。然而，僅藉由搜尋引擎來尋找知識是不足的，因為即使目前大部份的搜尋引擎都有提供依相關性排序及本文摘要的功能。通常使用者還是得透過搜尋引擎尋找數次、瀏覽許多不必要的網頁之後才能找到所需的資料，而非一次就能完成。因此本研究的主要目的，在於介紹如何利用文字探勘來發現蘊藏在大量中文文件中的知識。本文也將深入探討此技術的各項主要元件。透過主題地圖的實證研究，我們將製作兩類的主題地圖，分別是顯性知識（臺灣證券暨期貨法令資料）及隱性知識（王永慶思想哲學﹞。藉由這兩個地圖的比較來探討顯性知識與隱性知識在主題地圖的呈現上所發現的問題。Knowledge management (KM) has received much attention from both academics and practitioners in the past few years. Following the KM trend, many organizations have built their own knowledge repositories or data warehouses. However, information or knowledge is still scattered everywhere without being properly managed. The rapid growth of the Internet accelerates the creation of unstructured and unclassified information & causes the explosion of information overload. The effort of browsing information through general-purpose search engines turns out to be tedious and painstaking. Hence, an effective technology to solve this information retrieval problem is much needed. The purpose of this research is to explore the application of text mining technique in organizing knowledge stored in unstructured natural language text documents. Major components of text mining techniques required for topic map in particular will be presented in detail. Two sets of unstructured documents are utilized to demonstrate the usage of SOM for topic categorization. The first set of documents is a collection of speeches given by Y.C. Wang, Chairman of the Taiwan Plastics Group, & the other is the collection of all laws & regulations related to securities & future markets in Taiwan. We also try to apply text mining to these two sets of documents to generate their respective topic maps, thus revealing the differences between organizing explicit & tacit knowledge as well as the difficulties associated with tacit knowledge

Credit Rating Analysis With Support Vector Machines and Neural Networks: A Market Comparative Study

Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the United States and Taiwan markets. However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets

Evaluating the business value of RFID: Evidence from five case studies

This paper presents an in-depth analysis toward understanding the business value components an organization can derive from adopting radio frequency identification (RFID). Although this subject is currently a hot topic, many organizations are slow in warming up to the idea of using RFID to conduct more effective and efficient business processes. We propose a framework for evaluating the business value of RFID technology, hoping that a better understanding of the business value of RFID will encourage more organizations to implement it. Emphasis is on delivering business value through refining business processes and expanding the business model. We illustrate these concepts drawing on the experience of five early adopters from the Taiwan healthcare industry and formulate this framework as a set of propositions based on relevant literature, cases from pioneers in the field and our intuition. These propositions will need to be validated through empirical evidence.