Journal of Management and Engineering Integration, v.13 no.2

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    Journal of Management & Engineering Integration, v.13, no.2 (Winter)
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-12) Association for Industry, Engineering and Management Systems (AIEMS)
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    Enhancing decision making in power system planning using observable Markov Models and multi-objective optimization
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-12) Anyama, Tettey; Kim, Hensley
    In Power System planning, we seek to identify all current problems as well as potential issues in the electrical network and prioritize such cases accordingly for further action by management. Traditionally, load flow analysis is a network modeling and simulation approach that uses peak load as input data and generates feeder characteristics such as losses and voltages. The terminal voltages and losses obtained from such analysis are functions of the loading and circuit impedance. Since circuit impedance is fixed for an existing feeder, the peak loading becomes the main decision variable for System Planners. The use of peak loading alone for load flows therefore becomes an issue in Decision Making when an expectation of the peak is not quantified. In this research work, we apply Markov modeling to model the randomness in electric load behavior based on temperature. Instead of using just one historical peak loading, an expectation of the peak loading in different temperature states is modeled and used as the input for load flow studies. Based on the Decision Maker's (DM's) preferences and objectives for characteristics of the feeder in different states, the dominance test is used to rank feeders and their states. The approach sets the stage for Decision Making under uncertainty in power system planning.
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    Performance characterization of X group control charts
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-12) Abdulaziz, Abdulaziz; Weheba, Gamal
    Group control charts are used for the statistical control of multiple stream processes. Recently, a modified control chart based on the residuals from each stream has been proposed as an alternative charting scheme. This research examines the performance of these charts under varying combinations of shift magnitudes and number of streams affected. Several simulated scenarios were generated and used to evaluate the long-term performance of the two schemes in terms of the average run length. Statistical analysis of the results indicated that the modified chart has an advantage over the traditional group chart in terms of the rate of false alarms. However, both charting schemes appear to have similar shift detection capabilities.
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    Progress in autonomous building inspection drone development for scanning exterior damage of buildings
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-12) Hur, Byul; Ryoo, Boong Yeol; Zhan, Wei; Bustos, Carmelo; Consuelo, Gabriel; Orozco, Luis; Vazquez, Ramon
    Structures over time are prone to having damages and faults on exterior surfaces. Current methods for analyzing buildings for defects are time consuming and potentially dangerous for the inspector. At higher altitudes, additional safety measures must be taken. In order to streamline the exterior surface inspection process, a drone with manual and autonomous flight capabilities is being developed by multidisciplinary faculty members and undergraduate students at Texas A&M University through a Capstone project. The drone will record video and sensor data during operation. In this first phase of the development, the team has been creating a building infrastructure research drone which can monitor the exterior of the buildings for a vertical scanning task. The details of the building analysis drone development effort are presented in this paper.
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    Blockchain technology key to veracity in supply chain transaction data
    (Association for Industry, Engineering and Management Systems (AIEMS), 2020-12) Clasby, Dustin; Wollega, Ebisa
    Data are the foundation of the supply chain. However, the modern data stream in a supply chain is based on trust of the actors therein. Blockchain, a form of distributed ledger technology, eliminates the trust requirement of sharing supply chain data and allows each user to maintain an accurate, current copy of the ledger, without fear of data corruption or manipulation by bad actors. This paper reviews the application of blockchain to the management of transaction data in the supply chain. An overview of current blockchain technology will be discussed as well as advantages and disadvantages of the technology when applied to the supply chain. Current uses and research of the blockchain in supply chains will be discussed. There are three basic types of materials that are being explored in supply chain management using blockchain technology. The first is information-based materials and products. The most natural starting place for the blockchain is to replace the documentation and financial instruments. The second is fungible or commodity type materials. These goods are those delivered and consumed automatically, such as energy. The last are luxury, or crucial materials. These are items that are expensive or important enough that the costs of using advanced tracking technology makes economic sense.