IEMS 2020

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Now showing 1 - 5 of 22
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    Drivers and barriers of advanced manufacturing technology implementation in Saudi Arabia
    (Industry, Engineering & Conference Management Systems Conference, 2020-03) Al Fatais, Abdullah; Almuflih, Ali
    The purpose of this paper is to present a systematic literature review to identify opportunities and challenges that face Advanced Manufacturing Technology (AMT) adoption in Saudi industrial sector. It also highlights the critical factors behind those opportunities and challenges, which need to be taken into consideration when it comes to AMT implementation. This study uses a systematic review of the literature contained in the two databases ProQuest, and Compendex and on the search engine Google Scholar. Moreover, the study highlights a gap in the research efforts for identifying the need for effective integration and interaction between the eight different categories mentioned previously for both developed and developing economies. For that reason, it is recommended that researchers adopt a broader view that includes the role of integration and interaction between critical factors in each category and their impact on AMT adoption. The systematic literature review in this study used to review all vital elements of adopting AMT and identifies new research avenues and different approaches to implementing AMT, focusing on the integration between the different categories that can be used for AMT adoption in Saudi Arabia.
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    Effects of verbal versus graphical weather information on a pilot's decision-making during preflight
    (Industry, Engineering & Conference Management Systems Conference, 2020-03) Pittorie, Warren; Carstens, Deborah S.; Carroll, Meredith
    This study focused on the older technology of a verbal preflight weather briefing compared with the newer and emerging technology of digital textual and graphical weather pertinent to the flight route the pilot has chosen. The target population for this study was aviation students and instructors at Part 61 and 141 flight schools across the country. The accessible population for this study was flight students at a Florida university that had at least a private pilot's license through being employed as flight instructors. The 36 participants were selected from the accessible population based upon their availability and willingness to participate in the study. Institutional Review Board (IRB) approval was obtained from the university before conducting trials with human subjects. Two weather scenarios were selected for the trials. Participants were assigned to one of four groups based on the order of the two formats, verbal or visual (graphical/textual), for the two different scenarios. Four open-ended questions for the two weather scenarios were given to participants, which resulted in a total of eight open-ended questions per participant. The open-ended questions designed and included in the instrument captured the "why" behind pilots' decision to "go" or "no-go." The qualitative analysis software, Nvivo®, was used to analyze the four open-ended questions for each of the two weather scenarios. To visualize this data, a diagram was formed. The study results, discussion, and future research are presented in the paper.
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    Universidad Autónoma de Santo Domingo energy forecast
    (Industry, Engineering & Conference Management Systems Conference, 2020-03) Llaugel, Felipe; Ridley, Dennis; Cespedes, Amparo
    Energy time series data for Universidad Autónoma de Santo Domingo is analyzed and forecast by the moving window spectral method to capture cyclical components and effects. The historical record is 217 months from March 2001 to March 2019. A model is fitted to 205 historical observations from March 2001 to March 2018. The forecast period is 12 months from April 2018 to March 2019. The two overlapping 12 actual and forecast months are compared.
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    Electric vehicles routing problem with variable speed and time windows
    (Industry, Engineering & Conference Management Systems Conference, 2020-03) Abdallah, Khaled S.; Adel, Yasmin
    Vehicle routing is a major concern for a distribution channel of any supply chain. It plays a crucial role in attaining a competitive advantage for a company by being cost efficient or responsive. Transportation as a key logistics activity represents a relevant component (generally from 10% to 20%) of the final cost of goods, and one third to two thirds of the cost of logistics. The literature recently shifted towards the use of more energy efficient vehicles. Electric vehicles are characterized by being energy efficient, and do not produce polluting gas emissions such as carbon dioxide. However, the electric vehicles suffer from the limited capacity of the battery and the large charging time. In this paper, the dispatching and routing of battery-operated electric vehicles is considered. The vehicles can move at variable speeds when moving from a customer to another. When the speed is fast, the charge is depleted fast and small number of customers are served in a route. While when the speed is slow, the charge is depleted slowly, and more customers can be accommodated in a route. A genetic algorithm is developed to solve the problem. A piece linear range function based on finite speeds is proposed, as the average speed is used in planning for a given route in real life. A proposed genetic algorithm is proposed and applied on many cases from the literature. The results show that the model is able to optimize the performance and that the model behavior is consistent.
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    Developing a decision support tool for quantification and reduction of construction material waste
    (Industry, Engineering & Conference Management Systems Conference, 2020-03) Moynihan, Gary P.; Lyu, Qingchen; Nnaji, Chuma
    The construction industry has a major impact on the environment, both in terms of resource consumption and waste production. Given the high rate of raw materials wastage and ineffective waste management frequently conducted on construction sites, waste minimization strategies have become an important area of concern in the construction industry. However, the knowledge of origins and distribution of construction waste is very limited. This gap in knowledge limits the effectiveness of decisions made to reduce construction waste at the design and planning phase. The University of Alabama Construction Administration department (UACA) provides management and support for construction projects on campus. In this paper, the application of decision support system (DSS) technology is investigated to support UACA interest in construction material waste management. DSS are software systems that utilize sophisticated algorithmic approaches to solve problems. The utilization of technologies, such as Building Information Modeling (BIM), in construction has allowed for more efficient, better designed structures that limit the waste of resources, optimize energy use, and promote passive design strategies. The objectives of this research are to quantify construction waste, determine its causes, and lead to methods to minimize this waste. A prototype DSS was designed utilizing historical data at the University of Alabama. The design proposes a framework that integrates BIM with advanced analytical methods, within the DSS, to reduce the impact of construction waste materials. The current status of development of the prototype DSS, and future path forward, are discussed.