Saudi Cultural Missions Theses & Dissertations

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    The Humanitarian Vehicle Routing Problem with Non-Routineness of Trips
    (Purdue University, 2024-04-22) Alturki, Ibrahim; Lee, Seokcheon
    The escalating frequency and impact of natural disasters have necessitated the study of Humanitarian Logistics (HL) optimization to mitigate human and financial losses. This dissertation encompasses three pivotal studies that collectively seek to address some of the numerous gaps identified in the nascent literature of HL optimization, particularly in conflict-ridden and low-security environments. The first study conducts a comprehensive survey on the application of Multi-Criteria Decision Making (MCDM) methods in HL, identifying a significant gap between academic research and practical challenges, and highlighting underexplored areas within multicriteria optimization in HL. The second study introduces innovative deterministic and possibilistic models to improve the safety and security of humanitarian personnel by developing a vehicle routing model that minimizes the predictability of trips, a novel aspect in HL research. This includes the introduction of the Humanitarian Vehicle Routing Problem with Non-Routineness of Trips (HVRPNRT), creation of a unique index to measure trip routineness and the provision of an approximate closed-form solution for the aid allocation subproblem, and introduces a novel case study from the ongoing civil unrest in South Sudan. The third study presents a novel heuristic solution algorithm for the HVRPNRT, which is the first of its kind, and outperforms the commercial solver CPLEX on some instances. This algorithm offers near-optimal solutions with reduced computational times and maintains feasibility under stringent security conditions, thereby advancing the field of security-aware HL optimization. Collectively, these studies offer significant contributions to the field of HL optimization, providing a recent through survey of the field, novel practical models, methodologies, and an algorithm that address both operational efficiency and security challenges, in an effort to bridge the gap between theoretical research and real-world humanitarian needs.
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    Extensions of the Assembly Line Balancing Problem Towards a General Assembly System Design Problem
    (ProQuest, 2023) Alhomaidi, Esam; Askin, Ronald
    Assembly lines are low-cost production systems that manufacture similar finished units in large quantities. Manufacturers utilize mixed-model assembly lines to produce customized items that are not identical but share some general features in response to consumer needs. To maintain efficiency, the aim is to find the best feasible option to balance the lines efficiently; allocating each task to a workstation to satisfy all restrictions and fulfill all operational requirements in such a way that the line has the highest performance and maximum throughput. The work to be done at each workstation and line depends on the precise product configuration and is not constant across all models. This research seeks to enhance the subject of assembly line balancing by establishing a model for creating the most efficient assembly system. Several realistic characteristics are included into efficient optimization techniques and mathematical models to provide a more comprehensive model for building assembly systems. This involves analyzing the learning growth by task, employing parallel line designs, and configuring mixed models structure under particular constraints and criteria. This dissertation covers a gap in the literature by utilizing some exact and approximation modeling approaches. These methods are based on mathematical programming techniques, including integer and mixed integer models and heuristics. In this dissertation, heuristic approximations are employed to address problem-solving challenges caused by the problem's combinatorial complexity. This study proposes a model that considers learning curve effects and dynamic demand. This is exemplified in instances of a new assembly line, new employees, introducing new products or simply implementing engineering change orders. To achieve a cost-based optimal solution, an integer mathematical formulation is proposed to minimize the production line's total cost under the impact of learning and demand fulfillment. The research further creates approaches to obtain a comprehensive model in the case of single and mixed models for parallel lines systems. Optimization models and heuristics are developed under various aspects, such as cycle times by line and tooling considerations. Numerous extensions are explored effectively to analyze the cost impact under certain constraints and implications. The implementation results demonstrate that the proposed models and heuristics provide valuable insights.
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