The textile industry has witnessed remarkable growth, largely driven by the expansion of e-commerce. To meet the increasing demand for faster order fulfillment, warehouses are adopting advanced automation technologies. This study focuses on the idle time analysis within a goods-to-person order picking system in an automated warehouse. As a first step, a cause-and-effect tree is developed to systematically identify potential factors that contribute to idle time. Subsequently, the most significant indicators are determined through a mathematical model based on the Least Squares Principle. This approach prioritizes the indicators that have the most impact, enabling a targeted analysis of the root causes of idle time and providing valuable insights to improve system efficiency.

Healthcare systems are essential to society but face numerous challenges in maintaining their resilience and long-term viability. Disruptions, much like those experienced in supply chains-can significantly hinder operational continuity. To mitigate such risks, strategic investments can be made to reduce the likelihood of service interruptions. However, given the constraints of a limited protection budget, it is crucial to allocate resources efficiently to enhance the resilience of selected critical facilities. The objective of this research is to develop an optimal protection strategy that minimizes both patient travel distances for accessing care and the number of patients left without timely treatment due to disruptions. Since accurately predicting facility failures remains difficult, robust optimization provides a valuable framework. In particular, a scenario-based robust decision-making approach allows for the evaluation of multiple plausible disruption scenarios, ensuring that the selected strategy performs well under various conditions. Our methodology leverages scenario analysis to identify the most effective investment strategy for sustaining healthcare system performance over time. Each scenario corresponds to a different allocation of protective investments, and we assess their respective impacts on system resilience. By analyzing the relationship between the fortification of specific facilities and overall system performance, we generate insights to inform budget allocation decisions aimed at maximizing resilience. This process helps quantify the relative importance of each facility, enabling an optimized distribution of resources within fixed budgetary limits.

The design freedom enabled by additive manufacturing, combined with the unique properties of generative lattice structures, has gained increasing attention in thermal system technologies, especially for the development and fabrication of heat exchangers. In this study, a hybrid heat exchanger was developed by integrating a Diamond unit cell, known for its high thermal performance, with an IWP unit cell, which offers low flow resistance. The goal is to reduce flow resistance while maintaining heat transfer efficiency. A physical model was simulated using ANSYS software to evaluate the heat exchanger’s performance. The results indicate that integrating IWP cells into a Diamond unit cell structure effectively reduces flow resistance, though with a slight compromise in heat transfer performance. This balance provides a viable design strategy for heat exchangers in applications requiring precise flow resistance control.

This paper introduces a novel information sharing mechanism, the Improved Information Sharing Mechanism (I2SM), an adaptive real-time framework designed to enhance the performance of metaheuristic algorithms. I2SM dynamically collects and evaluates critical metrics, such as improvement rates and stagnation levels, through parallel processing, enabling real-time actions such as hybridization and parameter tuning. The mechanism’s adaptive nature ensures efficient handling of diverse optimization challenges by dynamically balancing exploration and exploitation, with a reasonable tradeoff in execution time.To assess the performance of the proposed I2SM mechanism, we selected the Particle Swarm Optimization (PSO) algorithm as a representative test framework. Empirical results from various benchmark functions demonstrate that PSO integrated with I2SM achieves superior performance, outperforming standard PSO in 90% of the cases. Although I2SM-PSO incurs a slightly higher execution time compared to standard PSO, significant improvements in solution quality validate its efficiency. However, this increase in execution time highlights a limitation that should be addressed in future research to optimize computational efficiency while maintaining performance gains.

Quality control is essential in the manufacturing and production sectors. This study advances Incoming Quality Control (In-QC) by proposing a new data-driven sampling plan that optimizes decision-making for quality inspection with a system of partial control. This system is characterized by specific parameters that facilitate strategic decisions. The methodology’s efficacy is validated through targeted simulations, followed by a practical application in a real-world case study. This dual-phase evaluation underscores the approach’s utility and effectiveness, aiming to improve In-QC practices with significant implications for the industry.

This paper presents a comprehensive approach for reliability assessment and optimization of ball joints by combining finite element analysis (FEA) method with machine learning algorithms, in particular by exploiting the power of the XGBoost machine learning algorithm. FEA simulations were carried out using ANSYS to generate a dataset encompassing various solder joint geometrical parameters configurations. Key input parameters, including geometric dimensions, were identified by sensitivity analysis and used as features for training the XGBoost model. The trained model demonstrated solid performance. Feature importance analysis revealed critical factors influencing solder joint reliability and provided insights for optimization strategies. This research provides practical recommendations for optimizing solder joint and material design to improve reliability and performance in electronic packaging applications.

In recent years, climate change and health crises have multiplied, including the global pandemic of Covid-19. These crises have revealed the vulnerability of healthcare systems, but also their ability to adapt quickly and become resilient. However, this adaptation often comes at a cost. To address this problem, it is necessary to identify and prioritize measures to strengthen the current health system and make it more able to cope with future crises. One approach is to strengthen critical assets through effective investment strategies. The purpose of this study is to identify an optimal investment strategy to improve the reliability of health systems. We proposed a bi-objective investment strategy to strengthen healthcare systems against random disruptions. We contribute to the literature with a novel two-step modeling approach. First, we model the patient journey through the healthcare system as a Markov chain. The Markov chain allows us to capture the sequential nature of the patient journey and to analyze the probability of patient transitions between different stages in the system. Second, we have a bi-objective model mathematical model that calculates the optimal investment strategy based on the probabilities established earlier in order to minimize both patient travel distance and the number of patients waiting for care. The model and its computational results with a full enumeration approach are presented in the paper. The existence of a large number of facilities and investment levels increases the time needed to evaluate all solutions and find the optimal set of solutions. Therefore, it is important to consider the need for granularity in the investment levels when applied to real-world situations.

This paper proposes a novel approach for the improvement of production quality in knitting workshops in the textile industry. The method is based on two steps. The first step consists of analyzing the performance of each operator to detect the different types of quality defects on the different machines of the workshop. The second step consists of optimizing the assignment of the operators to the different machines taking into account their performance in order to maximize the detection rate of the quality defects. The evaluation of the operator's performance is carried out using a multi-criteria analysis method called TOPSIS: Technique for Order by Similarity to the Ideal Solution. This performance indicator serves as a crucial input for the second proposed method, which is an optimized model for assigning operators in the workshop taking into account different constraints. The proposed approach is tested based on a real industrial configuration and the obtained results show its effectiveness.