Dissertations/Theses - Department of Industrial and Production Engineering

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    Analyzing the challenges to mitigate traffic congestion in densely populated cities: implications for efficient urban planning and sustainability
    (Department of Industrial & Production Engineering,(IPE), BUET, 2024-03-24) Fahim Bin Alam, Md.; Bari, Dr. A. B. M Mainul
    Traffic congestion (TC) disrupts everyday life, elevating stress levels, extending commute durations, diminishing productivity, and reducing overall quality of life. Due to extended stress and pollutant exposure, TC causes environmental degradation through air pollution, fuel inefficiency, company losses, and increased health risks. Congestion exacerbates already-existing problems in densely populated places by taxing the little infrastructure, reducing productivity, and disrupting supply networks. Urban planning is made more difficult by the residents' lower accessibility and higher levels of distress. Reducing traffic is essential to creating sustainable, livable communities, which calls for creative ways to handle expanding populations while minimizing negative impacts on the environment, economy, and public health. Therefore, this study employs a combined method for multi-criteria decision-making (MCDM), integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique with the interval-valued Pythagorean Fuzzy (IVPF) theory to pinpoint, rank, and analyze the relationships between the challenges impacting the mitigation of TC, with an emphasis on densely populated urban areas like Dhaka. At the outset, 18 (eighteen) challenges were identified through a review of existing literature. Following expert validation, 16 (sixteen) challenges were chosen for analysis utilizing the IVPF DEMATEL method. The findings of the study show that the three major challenges to TC mitigation are “Lack of efficient coordination and management of traffic signals”, “Insufficient choices for public transportation”, and “Lack of integration of advanced data analytics and IoT-based technologies”. The anticipated impact of this study lies in its substantial contribution to future innovation and development in urban planning. By aiding policymakers, urban planners, and stakeholders in formulating long-term strategies through strategic decision-making, the study aims to alleviate severe TC, particularly in densely populated cities. and bring about positive changes in the urban planning sector.
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    Modeling humanitarian relief supply chain performance using bayesian network approach
    (Department of Industrial & Production Engineering (IPE), BUET, 2023-07-25) Nafisa Ahmed, Humaira; Ali, Dr. Syed Mithun
    The concept of humanitarian relief supply chain management has gained a lot of interest among academics and practitioners since the number of natural or human-made disasters have been increased drastically. These disasters frequently cause extensive destruction, loss of life, and collateral damage. Although these events cannot be prevented, appropriate measures can be taken to mitigate their negative effects on nations. Humanitarian organizations provide aid for disaster relief operations, which is known as the humanitarian relief supply chain. Nonetheless, it is essential to comprehend the efficacy of the humanitarian relief supply chain performance measurement model. A humanitarian organization can monitor and control its relief supply chain more efficiently and effectively by measuring performance. The purpose of this research is to develop a Bayesian belief network model for predicting the performance of the humanitarian relief supply chain in case of catastrophic events, such as natural disasters and man-made crises, in order to efficiently deliver assistance to affected regions. The study begins with the identification of performance metrics that have a direct or indirect effect on the overall performance of a humanitarian organization. Then, with the aid of a Bayesian belief network, a probabilistic graphical model capable of predicting any organization's relief supply chain based on performance metrics was developed. The model has been validated through numerical examples, extreme condition testing, scenario analysis, sensitivity analysis, and diagnostics analysis. The performance measurement model will assist organizations' decision-makers and policymakers in controlling, monitoring, and enhancing their relief supply chain.