Department of Industrial Engineering

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Work by the faculty and students of the Department of Industrial Engineering


Recent Submissions

  • Publication
    Numerical and sensitivity analyses of various design parameters to maximize performance of a Vortex Tube
    (Elsevier, 2022) Al Saghir, Ahmad Mohammad; Hamdan, Mohammad; Orhan, Mehmet Fatih; Awad, Mahmoud
    In this study, a series of numerical simulations are performed to investigate the performance of a vortex tube (VT) with various design parameters. The study focuses on six design parameters, mainly, inlet pressure, VT length, VT diameter, number of inlet nozzles, nozzle diameters, and hot outlet pressure. The results indicate that VT performance has a non-monotonic relation with all design parameters and, hence optimum performance can be achieved by conducting response surface methodology (RSM) study. The optimum VT performance is achieved with specific design parameters; for instance, the maximum cooling is achieved with VT length of 194 mm, VT diameter of 14.6 mm, 4 inlet nozzles, nozzle diameter of 1.8 mm, and hot outlet pressure of 60,303 Pa. The results show that energy separation increases at higher inlet pressures until inlet nozzles reach choking condition. Beyond that, the improvement in energy separation is small and VT performance starts deteriorating once shock waves start formulating outside the nozzle.
  • Publication
    Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain
    (MDPI, 2021) Al Sadawi, Alia; Shamayleh, Abdulrahim; Ndiaye, Malick
    The financial data supply chain is vital to the economy, especially for banks. It affects their customer service level, therefore, it is crucial to manage the scheduling of the financial data supply chain to elevate the efficiency of banking sectors’ performance. The primary tool used in the data supply chain is data batch processing which requires efficient scheduling. This work investigates the problem of scheduling the processing of tasks with non-identical sizes and different priorities on a set of parallel processors. An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. The objective is to minimize different cost types while satisfying constraints such as resources availability, customer service level, and tasks dependency relation. The algorithm proved its effectiveness by allocating tasks with higher priority and weight while taking into consideration customers’ Service Level Agreement, time, and different types of costs, which led to a lower total cost of the batching process. The developed algorithm proved effective by testing it on an illustrative network. Also, a sensitivity analysis is conducted by varying the model parameters for networks with different sizes and complexities to study their impact on the total cost and the problem under study.
  • Publication
    Transient high thyroid stimulating hormone and hypothyroidism incidence during follow up of subclinical hypothyroidism
    (Sciendo, 2021) Abu-Helalah, Munir; Alshraideh, Hussam; Al-Sarayreh, Sameeh Abdulkareem; Al-Hader, AbdelFattah
    Objectives. Given the high prevalence of subclinical hypothyroidism (SCH), defined as high thyroid stimulating hormone (TSH) and normal free thyroxine (FT4), and uncertainty on treatment, one of the major challenges in clinical practice is whether to initiate the treatment for SCH or to keep the patients under surveillance. There is no published study that has identified predictors of short-term changes in thyroid status amongst patients with mild elevation of TSH (4.5-10 mIU/L). Subjects and Results. A cohort study was conducted on patients with SCH detected through a general population screening program, who were followed for six months. This project identified factors predicting progression to hypothyroid status, persistent SCH and transient cases. A total of 656 participants joined the study (431 controls and 225 were patients with SCH). A part of participants (12.2%) developed biochemical hypothyroidism during the follow-up, while 73.8% of the subjects became euthyroid and the remained ones (13.4%) stayed in the SCH status. The incidence of overt hypothyroidism for participants with TSH above 6.9 mIU/L was 36.7%, with incidence of 42.3% for females. Anti-thyroid peroxidase antibodies (TPO) positivity is an important predictor of development of hypothyroidism; however, it could be also positive due to transient thyroiditis. Conclusions. It can be concluded that females with TSH above 6.9 mIU/L, particularly those with free triiodothyronine (FT3) and FT4 in the lower half of the reference range, are more likely to develop biochemical hypothyroidism. Therefore, it is recommended to give them a trial of levothyroxine replacement. It is also recommended to repeat TSH after six months for male subjects and participants with baseline TSH equal or less than 6.9 mIU/L.
  • Publication
    A Framework for Assessing Commitment Indicators in Sustainable Development Decisions
    (MDPI, 2021) Al-Tekreeti, Mustafa Sahban; Beheiry, Salwa; Ahmed, Vian
    Numerous decision support systems have been developed to address the decision-making process in organizations. However, there are no developed mechanisms to track commitment down the line to the decisions made by corporate leaders. This paper is a portion of a study that establishes a framework for a comprehensive metric system to assess commitment to Sustainable Development (SD) decisions down the line in capital projects, and sets the groundwork for further development of performance indicators for SD outcomes. This ultimately leads to investigating the relationship between commitment to corporate decisions and better project performance in SD parameters. Hence, this study explores the literature to extract relevant parameters that reflect the degree of the project participants’ commitment to SD decisions and to develop commitment indicators. The study created then validated an index to track this commitment along the project stages: the Sustainable Development Commitment Tracking Tool (SDCTT). The SDCTT was tested on an infrastructure project case study. In this paper, techniques relevant to the first stage of projects (planning and definition) are presented. The SDCTT is the groundwork for the future development of performance indicators for SD outcomes, and within the postulated model should ultimately contribute towards reducing project waste, energy use, and carbon emissions.
  • Publication
    A Comparative Study of Energy Performance in Educational Buildings in the UAE
    (UTS ePRESS, 2021) Ahmed, Vian; Saboor, Sara; Almarzooqi, Fatima Ahmed; Alshamsi, Hessa Ahmed; Alketbi, Mariam Abdalla; Al marei, Fatema Ahmed
    Sustainability has gained popularity and importance around the globe due to the ever-increasing effects of climate change and global warming on Earth. As of the 21st century, human endeavour has caused an enormous amount of damage to the environmental ecological system. Among which, one of the major contributors to the increase in the environmental issues and CO2 emissions are the conventional sources of energy, especially in the built environment. Globally, the built environment accounts for 12 percent of the world’s drinkable water, 40 percent of energy wastage and 35 percent of scarce natural resources, which in turn produces 40 percent of the total global carbon emission. Among which are educational buildings which tend to be a major contributor (as most of these facilities are old and conventionally built in the mid 1900’s) Thus, with the education sector being an essential part of society, it becomes important to determine the energy performance and carbon footprint of these buildings. The United Arab Emirates (UAE) vision 2021 highlights the country’s approach to the importance of providing the best education and adopting sustainable environmental infrastructure. Therefore, this study adopts a methodological approach based on semi-structured interviews and surveys, in order to compare the energy performance of three educational buildings within Higher Education establishments in the UAE as a case study. The study also evaluates the end user’s awareness of the importance of sustainable practices in the buildings and their preference of these buildings. The findings of this study conclude that Net Zero Energy Buildings (NZEBs) are the most efficient buildings in terms of energy performance, carbon consumption and heat generated. Therefore, it is important that the integration of these types of buildings are considered in educational establishments.
  • Publication
    Targeting Breast Cancer Using Hyaluronic Acid-Conjugated Liposomes Triggered with Ultrasound
    (American Science Publishers, 2021) Ben-Daya, Mohamed; Paul, Vinod; Awad, Nahid S.; AlSawaftah, Nour Majdi; Al-Sayah, Mohammad; Husseini, Ghaleb
    The successful targeting of tumors can be achieved by conjugating targeting moieties to nanoparticles. These modifications allow nannocarriers to achieve greater targeting specificity through binding to specific receptors overexpressed on the surface of the tumor cells. In this study, pegylated liposomes encapsulating the model drug/dye calcein and conjugated to hyaluronic acid (HA) molecules were successfully synthesized, and their ability to target HA receptors overexpressed on a breast cancer cell line was investigated in vitro. Low-frequency ultrasound (LFUS), applied at three different power densities (6.2, 9, and 10 mW/cm2) were used to trigger the release of the entrapped calcein. Both the control and HA-conjugated liposomes showed similar release profiles. HA conjugation to the liposomes resulted in a significant increase in calcein uptake by the breast cancer cell line MDA-MB-231 known for its CD44 (HA receptor) overexpression, while such an effect was not recorded with NIH-3T3, an embryonic mouse fibroblast, with low levels of CD44 expression. The application of low LFUS showed a significant enhancement of calcein uptake by MDA-MB-231 cells from our liposome compared to calcein uptake without cell exposure to ultrasound. These findings suggest that combining HA-conjugated liposomes with ultrasound is a promising drug delivery platform in breast cancer treatment.
  • Publication
    Economic Allocation of Reliability Growth Testing Using Weibull Distributions
    (Elsevier, 2016-08) Awad, Mahmoud
    Reliability growth testing (RGT) has been widely used for assessing the reliability of complex systems in many industries such as automotive, aerospace, and oil and gas industry. The traditional common and practiced approach of RGT is to assess the initial reliability of the system by building and testing few prototypes for a period of time that extends from few months to years. Then, based on the initial reliability, initial testing time, and reliability target; the total testing time is determined using power law based models such as Duane and AMSAA/Crow models. In this paper, a new method is proposed to allocate RGT testing time for both subsystems and system level in order to minimize system failure rate under limited cost and time resources. Unlike existing methods, intensity failure rate is assumed to be dynamic and modeled using Weibull distribution. Modeling using Weibull is more realistic and increases the applicability of the proposed method in real life applications. The proposed method is motivated by real life examples and its effectiveness is demonstrated by real-life examples.
  • Publication
    Reliability Centered Maintenance Actions Prioritization Using Fuzzy Inference Systems
    (Emeraldinsight, 2016) Awad, Mahmoud; As'ad, Rami Afif
    Reliability centered maintenance (RCM) is a systematic maintenance philosophy/approach used to analyze system's performance in terms of the impact of a potential failure and select the most efficient maintenance tasks along with their timings in order to mitigate failures risks. In this paper, a comprehensive RCM actions prioritization method is proposed using four criteria: severity, benefit to cost ratio, customer satisfaction, and easiness of action implementation. The method utilizes fuzzy inference system (FIS) to incorporate subject matter experts feedback into the decision making process. The output of the FIS, which takes the form of a numerical weight that assesses the relative importance of each criterion, is then fed into a binary integer program (BIP) that selects the optimal maintenance actions out of a set of possible actions. A real life example of a hydraulic brake system is also provided to illustrate the proposed methodology.
  • Publication
    Fault Detection of Fuel Systems Using Polynomial Regression Profile Monitoring
    (Wiley Online Library, 2016-08-09) Awad, Mahmoud
    Anomaly detection is the characterization of a normal behavior of a system or process and identification of any deviation from such normal behavior. Anomaly detection of critical systems provides an important financial and client competitive advantage since it gives the decision-maker lead-time and flexibility to manage the health of the system. Fuel systems are complex and mission critical systems that require high operational availability because of the high costs associated with the services they provide. In complex systems, it is not uncommon to monitor a quality-related response which relies on the functional form between several variables using a non-linear relationship. We present in this paper a new monitoring framework for smart fuel systems utilizing outlying observations detection and monitoring using ccharts. The traditional control charts based on the Hotelling's T2 statistic were deficient in detecting SFS anomalies and a new approach was necessary to isolate faulty profiles. The proposed methodology requires a simple quality performance test that can be performed once assembly is completed to assure readiness for client use or completion of a job. The results were tested and validated using scaled data that mimic an actual system. The methodology presented in this paper is scalable and can be applied to a wide range of systems to assess their health from an inspection check to anticipate and avoid failures.
  • Publication
    Analyzing Sensitivity Measures Using Moment-Matching Technique
    (Elsevier, 2017-03) Awad, Mahmoud
    Sensitivity indices are used to rank the importance of input design variables or components by estimating the degree of uncertainty of output variable influenced by the uncertainty generated from input variables or components. With the advent of highly complex engineering simulation models that describe the relationship between input variables and output response, the need for an efficient and effective sensitivity analysis is more demanding. Traditional importance measures either requires extensive random number generations or unable to measure variables interaction effects. In this article, a generalized approach that can provide efficient and accurate global sensitivity indices is developed. The approach consists of two steps; running an orthogonal array based experiment using moment-matched levels of the input variables followed by a variance contribution analysis. The benefits of the approach are demonstrated through different real life examples.