Industrial and Manufacturing Systems Engineering electronic theses and dissertations (MU)
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The items in this collection are the theses and dissertations written by students of the Department of Industrial and Manufacturing Systems Engineering. Some items may be viewed only by members of the University of Missouri System and/or University of Missouri-Columbia. Click on one of the browse buttons above for a complete listing of the works.
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Item Evaluating cognitive workload in an AR environment using pupil area responses(University of Missouri--Columbia, 2025) Mohanty, Siddarth; Kim, Jung Hyup[EMBARGOED UNTIL 12/01/2026] This thesis investigates how augmented-reality (AR) instruction affects learners' cognitive workload, using pupil-area pupillometry synchronized with established subjective measures. Participants engaged with biomechanics-focused AR modules while wearing eye-tracking glasses, enabling continuous recording of their pupil responses during both learning and problem-solving phases. Introducing brief verbal nudges further modulates these dilation trajectories, suggesting that timely spoken prompts can help regulate mental effort. Together, the work delivers (i) a validated, non-intrusive protocol for assessing workload in head-worn AR; (ii) clear evidence that pupil dynamics differ distinctly between learning and problem-solving, underscoring the importance of phase-sensitive adaptation; (iii) proof that targeted verbal cues can influence cognitive engagement. These contributions deepen our understanding of cognitive processing in mixed-reality learning and lay the groundwork for workload-aware AR educational tools.Item Optimization frameworks for collaborative truck-drone logistics under environmental and operational considerations(University of Missouri--Columbia, 2025) Alizadeh, Arash; Srinivas, Sharan[EMBARGOED UNTIL 12/01/2026] The integration of unmanned vehicles (UVs) into last-mile logistics enables costefficient, rapid, and environmentally sustainable deliveries. Yet, most prior work treats operational and environmental factors separately and simplifies key dependencies such as speed regulation, roadway grade, and coordination among heterogeneous vehicles. This dissertation develops a unified suite of optimization frameworks that address these gaps through three progressively enriched variants, each supported by mixed-integer programming (MIP) and advanced metaheuristics. The first variant introduces the Truck Multi-Drone Pollution Routing Problem with Pickup and Delivery under Variable Truck Speeds (TMD-PRP-PDVS). The formulation integrates multi-drone coordination, segment-specific truck speed control, and weight-dependent fuel and energy models. A MIP minimizes truck fuel, drone energy, and labor costs, while an adaptive large neighborhood search (ALNS) efficiently solves larger instances. Experiments show that collaboration with drones reduces total cost by about 40% relative to truck-only delivery, and speed optimization yields an additional 7–13% reduction across contrasting cost regimes. The second variant extends the framework to the Gradient-Aware Truck–Drone Pollution Routing Problem with Adaptive Drone Deployment Sites and Soft Time Windows (GA-TDPRP-AS-STW), explicitly modeling roadway slopes, variable truck speeds, and flexible drone deployment sites under penalty-based time windows. Both a MIP and an enhanced ALNS are developed. On small benchmarks, the MIP proves optimality and the ALNS attains near-optimal solutions; on larger sets (up to 100 customers), the ALNS scales effectively. Collaboration delivers 33–40% savings over truck-only, and speed optimization contributes a further 6–12%, reaching up to 62% total savings. Elevation data and adaptive sites expand synchronization options and lower fuel consumption even when route length increases modestly. Lastly, the third variant, A Synergistic Truck-Drone-Robot System for Last-Mile Delivery with Hand-Off Points and Microstations for Dynamic Repositioning, unifies tri-vehicle operations with a network of micro-/docking stations that enable parallel service, recovery without truck waiting, and dynamic drone and ADR repositioning. For this variant, an ALNS developed to coordinate facility usage, routing, and allocation decisions. Computational studies indicate 20–38% reductions in completion time versus truck-only baselines, with additional gains when hybrid trucks carry UVs between stations. Across variants, extensive experiments demonstrate that integrating environmental characteristics, speed selection, adaptive deployment sites, and facility-aided repositioning materially improves both cost and completion time performance. The resulting models and algorithms provide actionable guidance for designing sustainable, multi-agent last-mile networks.Item Robust facility location selection frameworks for service systems(University of Missouri--Columbia, 2025) Golghamat Raad, Nima; Rajendran, Suchithra[EMBARGOED UNTIL 08/01/2026] This dissertation develops a unified robust optimization framework for strategic facility-location decisions in service logistics under uncertainty. Motivated by the growing need for infrastructure resilience against demand volatility, budget constraints, and environmental targets, this research integrates multi-criteria decision-making (MCDM) methodologies with stochastic, fuzzy, and regret-based optimization techniques across increasing levels of network complexity. Study 1 focuses on robust single-facility location under uncertainty, proposing a two-stage potential-assessment and siting approach for regional airports. The methodology combines a Robust Slack-Based Measure Data-Envelopment Analysis (SBM-RDEA) to assess existing facilities, regression modeling to identify influential factors, and GIS-based weighted overlays for suitability mapping. Applied to Sistan-and-Baluchestan Province, Iran, the model significantly narrows the candidate location area, pinpointing Zahedan County as optimal, outperforming conventional entropy--AHP methods. Building upon this approach, Study 2 expands the scope to multiple-facility network location under uncertainty, specifically addressing a closed-loop dry-port network. It employs a hybrid Fuzzy SWARA-COPRAS method to derive expert-validated weights for economic, environmental, infrastructural, and socio-political criteria. These weights guide a multi-stage fuzzy-stochastic chance-constrained program utilizing a novel robustness metric that balances expected benefit, worst-case profit, and bounded relative regret. An Iranian national case illustrates that establishing four strategically located inland terminals interconnected by rail substantially reduces costs, lowers CO₂ emissions, and maintains profitability under significant demand fluctuations. Study 3 further advances the model by integrating routing considerations into multiple facility location decisions, focusing on depot and battery-swapping networks for delay-sensitive drone deliveries. The research formulates a lexicographic, regret-aware location--routing MILP that simultaneously maximizes on-time service reliability, extends spatial coverage, and minimizes capital investments amidst drone endurance uncertainties. Computational experiments using adaptive large-neighborhood search achieve near-optimal solutions for metropolitan-scale problems. A case in Dallas--Fort Worth demonstrates the practical benefits of optimized depot layouts in meeting critical service deadlines for medical deliveries at reduced infrastructure investment. Collectively, the dissertation reframes robustness as a comprehensive design principle, bridging strategic location planning with tactical network optimization and operational routing decisions. By coupling data-driven criteria weighting with robust and tractable optimization models, it provides actionable frameworks for planners in air transport, inland freight logistics, and drone delivery services. These tools enable informed decision-making, effectively balancing cost, resilience, and sustainability across diverse future scenarios.Item Design and evaluation of EV charging networks : a case study of the SF--LA corridor(University of Missouri--Columbia, 2025) El-Rjoob, Sora; Rajendran, Suchithra[EMBARGOED UNTIL 08/01/2026] The growing adoption of electric vehicles (EVs) presents both opportunities and challenges for transportation systems, particularly in high-volume corridors. Although EVs provide environmental and economic benefits, inadequate charging station capacity can lead to congestion, long waiting times, and grid strain. This study employs a simulation-based model to evaluate EV network performance along the San Francisco--Los Angeles corridor, utilizing real-world arrival rates, behavioral factors, and grid constraints. The model implements a non-stationary M(t)/M/c simulation approach in Python to estimate and compare waiting times, booth requirements, and related costs across several scenarios under demand uncertainty, driver behaviors, and grid constraints. The simulation results show how booth allocation and system performance metrics respond to arrival rates and behavioral variations. This provides a flexible framework, supporting decision-making for infrastructure planners and offers guidance on trade-offs between costs, sustainability, and service quality.Item Microelectronic assembly - capacity, supply and assurance(University of Missouri--Columbia, 2025) L. Kutney, James; Rajendran, Suchithra[EMBARGOED UNTIL 05/01/2026] This thesis uses a survey of literature method to lay the foundation for recommendations that minimize the probability of unintended supply disruptions, shortages, or sabotage of microelectronic assemblies. This paper describes manufacturing (microchip manufacturing, electronic design, wafer preparation, photolothography, material addition/removal/modification, chiplet assembly, packaging and testing) of a microelectronic assembly, including the semiconductor chip. This paper reviews the microassembly industry standards (via Appendix) according to general test method categories (functional, parametric, reliability, structural, burn-in and packaging) and by major standards organization (JEDEC, MIL-STD, IEC, IEEE, AEC and IPC) as well as specialty standards organizations involved in specialized methods and applications such as chiplets (UCIe) and defense procurement (DFARS). With the above background, the author introduces technical and non-technical market trends threatening assurance of microelectronics capacity and supply such as geopolitics, international and domestic tariff and subsidy policies, supplier concentration, dis-intermediation of integrated fabs, electronic design automation startups and advanced node (photolithography-limit-driven) technologies like 3D, FinFET and factory optimization tools. With a basis of understanding of the manufacturing processes, quality standards and market forces, the thesis introduces artificial intelligence, and machine learning (via Appendix) in the context of human-included, or human-in-the-loop decision processes focused on microelectronic assembly capacity, supply and assurance. The author then demonstrates the benefits of commercial large language models for the testing of queries on such critical items as: current standard coverage and gaps in technical standards for custom integrated circuits and 'comparison of performance parameters' for advanced testing equipment. The thesis then considers mixed human-in-the-loop and model-based exercises that have value for group supply chain problem solving using enhanced human-included artificial intelligence.