औद्योगिक इंजीनियरिंग एवं प्रबंधन

As human beings living in the so-called “blue planet,” our main responsibility lies in protecting the earth and its resources. The exhaustion of petrochemical energy and the greenhouse effect have caused huge impact on our lives. With these in mind, specifically their damage to the environment and the economy, this study discusses the possible strategies used in dealing with the industrial sustainability issues from a national viewpoint. A Dynamic Industrial System (DIS) is proposed in which the integration of the input-output analysis with the time series analysis is developed. Scenario analysis for energy conservation and carbon reduction is conducted to facilitate the strategy development of the industrial sectors. The case of the Taiwan economy is adopted to illustrate the proposed system. The proposed DIS is applied by classifying all domestic industries into nine sectors. Then, the vector time series analysis is carried out to predict the related input and output factors up to the year 2025. By observing the development trend of each focal industry sector included in this work with the analysis of the degree of industrial influence and sensitivity, the scenario analysis regarding the energy saving and carbon emission reduction are carried out to observe the changes in the industrial input to the impact and development of the output as consequence. Thus, the proper industrial strategies are developed to support a government on approaching a win-win frontier of both the environment and the economy.

This study presents two enhanced learning algorithms used for discovering probabilistic graphical models based on the Bayesian Network (BN) structure. The two heuristic structure learning algorithms, namely Tabu Search (TS) and Simulated Annealing (SA), were empirically evaluated and compared regarding efficiency. These algorithms were applied to real-life data sets for the vertebral column. A data set containing values for six biomechanical features was used to classify patients into three categories, namely, Disk Hernia (DH), Spondylolisthesis (SL), and Normal (NO), and two categories, namely, Abnormal (AB) and NO. The results indicated that SA is a more effective algorithm than TS. However, the empirical results obtained using TS indicated that the TS algorithm is promising because of its relatively simple network structure.