Hansuk Sohn

Efficient scheduling and resource allocation in dental prosthesis supply chains are critical for improving patient outcomes and reducing turnaround times. Traditional workflows often suffer from delays due to fragmented routing, clinic bottlenecks, and limited visibility across production stages. To address these challenges, we develop a mixed-integer linear programming (MILP) model that jointly optimizes patient scheduling, routing decisions, and capacity constrained resource allocation in a digitally enabled, 3D printing-based prosthesis workflow. The model incorporates patient routing decisions (direct-to-lab vs. via-clinic), lab capacity constraints, material availability, and fixed delivery delays. The model minimizes lead time while enforcing fairness through worst-case constraints and deterministic delivery assumptions. Results from a baseline scenario involving 850 patients across a 45-day planning window show that the optimized system achieves balanced lab utilization, equitable lead time distribution, and full satisfaction of routing constraints. Comparative analysis highlights the significant advantage of direct-to-lab workflows over clinic-mediated routes. Scenario-based sensitivity analyses reveal the impact of routing strategies, inventory thresholds, and delivery penalties on system performance. The study demonstrates how simulationinformed optimization can guide strategic planning in healthcare logistics, offering a reproducible and adaptable framework for real-world deployment.

The traveling salesman problem (TSP) is the canonical combinatorial optimization problem famous throughout literature. There exists an objective function associated with every feasible solution. However, the increase in the number of possible solutions makes this an NP-Hard problem. We show that the central limit theorem (CLT) applies to the problem. We then conduct extensive computational testing to show that the cycle lengths tend to a normal distribution as the problem grows large. When the size of the TSP problem exceeds computational power, better understanding solution distributions allows us to save resources. This is a non-trivial result as understanding solution distributions in huge TSP problems helps us to minimize computational effort that may not lead to significantly better results. El problema del agente viajero (“Traveling Salesman Problem” o TSP) es el problema canónico de optimización combinatoria usando en la literatura. Existe una función objetivo asociada con cada solución factible. Sin embargo, la tasa de aumento en el número de soluciones posibles hace que este sea un problema NP Difícil. Mostramos que el teorema del límite central (Central Limit Theorem o CLT) se aplica al problema y luego realizamos pruebas computacionales extensas para mostrar que las longitudes de los ciclos tienden a una distribución normal a medida que el tamaño del problema crece. Cuando el tamaño del problema TSP excede el poder computacional, una mejor comprensión de las distribuciones de soluciones nos permite ahorrar recursos. Este es un resultado no trivial, ya que comprender las distribuciones de soluciones en problemas TSP enormes nos ayuda a minimizar el esfuerzo computacional que puede no conducir a resultados significativamente mejores.

In this paper, a statistical distribution is presented that possesses the ability to describe failure rates exhibiting both monotonic and non-monotonic behaviors, and the bathtub curve, which represents the performance of a device in reliability engineering. The proposed distribution is based on the sum of the hazard functions of the Chen distribution and the Perks distribution, thus presenting the Chen–Perks distribution (CPD). Statistical properties of the CPD focused on reliability engineering are presented to make the model attractive to practitioners of the discipline. The parameters of the CPD were calculated via the maximum likelihood estimator. On the other hand, a comparative analysis was conducted in three study cases to determine the behavior of the CPD relative to other distributions that can describe failure times with the shape of a bathtub curve. The results show that the CPD can offer competitive results, which practitioners can consider when conducting reliability analysis.

The supply chain has been significantly impacted recently, and as a result the handling of products at the delivery and supply stage is of great importance. Currently, the agronomic industry is one of the most studied, but the reliability of harvests is not usually evaluated. This study uses a novel reliability analysis with an accelerated life test to expose the European cucumber (Cucumis sativus L.) to accelerated temperature and humidity conditions. The objective is to observe the effect of both factors on the deterioration of the product. The analysis includes a degradation analysis to determine the significant factors causing degradation, followed by an accelerated life test (ALT) to determine the product’s shelf life. Finally, through the development of a reliability model, storage times for cucumbers under normal storage conditions are predicted.

The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.

Decision-makers have rightly come to expect thorough, relevant analysis prior to allocating resources. The field of education planning is no different in its requirement for such analysis. In this research, we present a discrete event simulation model in which individual students and teachers flow through the Afghanistan education system across a variable time horizon. We explain the simulation and its premises through some verification and validation processes that allow the user to glean insight into the implications of security, policy, and infrastructure in this complex and capricious environment.