Advances in Industrial Engineering

With advancements in technology and growing customer demand, the optimal design of hub networks in distribution systems, along with flow synchronization across the entire network, play a critical role in reducing costs and enhancing overall efficiency. These networks significantly contribute to optimizing delivery times and improving responsiveness to customer needs, particularly in the transportation of time-sensitive goods. This study develops a mixed-integer linear programming model for the synchromodal hub location problem. We synchronize the flow throughout the entire network, which consists of origin points, a sender hub, a receiver hub, and demand points. To replicate real-world conditions, we also consider the synchronization of product flow within the distribution hub distribution networks. The proposed model aims to minimize the total cost, which includes transportation costs, operational costs across the distribution network, fixed costs of establishing Hubs and deploying vehicles, and potential penalties incurred as a result of failures to meet customer demand in terms of quantity and timeliness. To aggregate long-, mid-, and short-term decisions, we examine several decisions across different time periods. These decisions include the incomplete hub location problem, the service network design problem involving the scheduling of all network nodes, the synchronization of shipment flows in an intermodal transportation system, as well as integration and sorting operations on all components of the hub distribution network. The model's performance is assessed using data from an actual case study in the Iranian food industry. We conduct various sensitivity analyses on key parameters of the problem and present the numerical findings.

Earned Value Management ( ) and Earned Schedule ( ) are crucial tools for controlling projects and preventing deviations from schedule and budget objectives. In early-return projects, meeting deadlines is critical; however, Earned Value alone may not provide an appropriate criterion for evaluating and analyzing time-related indicators. This study proposes a method to use statistical control charts that consider indicator deviations from the project's start (memory charts) and are more sensitive to schedule deviations. Specifically, Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts are employed to monitor the Schedule Performance Index ( ) based on the  system. Instead of Expected Value ( ),  is used for monitoring. Results demonstrate that both CUSUM and EWMA charts offer higher accuracy compared to classical Shewhart charts and produce fewer error alarms. The CUSUM chart shows less error (75% reduction in initial error alarms), while EWMA displays higher sensitivity (20% faster deviation detection). This proposed method can assist project managers in identifying schedule deviations more accurately and rapidly. The study utilized data from a 30-month construction project, applying normality tests and data transformation techniques to ensure statistical validity. The findings suggest that memory control charts based on  provide a more reliable and responsive approach to project schedule monitoring, particularly in time-sensitive projects.

As global population growth accelerates, demand for agricultural products has surged, leading to higher production, rising costs, increased water use, and food shortages. This study proposes a sustainable agricultural supply chain network that prioritizes water conservation while meeting customer needs. A mathematical model optimizes a closed-loop supply chain, maximizing demand for agricultural products and compost. The model minimizes costs, maximizes customer satisfaction, and reduces water consumption, ensuring sustainability. A stochastic programming approach manages supply and demand uncertainties through scenarios. Results show that increasing customer satisfaction raises costs and water use. For example, increasing the customer importance factor from 0.2 to 0.8 increases total costs by 4.53% and water use by 43.75%, highlighting the sensitivity of water use to customer satisfaction. Reducing processing center capacity decreases water use but increases costs and reduces customer satisfaction. A 50% reduction in capacity raises costs by 56.41%, decreases customer satisfaction by 4.44%, and reduces water use. Water use reductions vary by stage: a 50% reduction in agricultural production cuts total water use by 32.33%, while similar reductions in processing and composting yield smaller decreases of 17.86% and 28.32%, respectively. This underscores agricultural production as the most water-intensive phase. The model’s effectiveness is demonstrated through numerical examples and sensitivity analyses. Metrics such as the Number of Pareto Fronts (NPF) and Maximum Spread Index (MSI) are used to compare solutions. This study emphasizes aligning sustainable production, resource conservation, and customer needs to create a resilient agricultural supply chain.

In an era of rapid change, complexity, and uncertainty, organizations must rely on sustainable project portfolio management to achieve long-term objectives. In project-oriented environments, selecting the most suitable project portfolio remains a critical challenge. To address this, advanced decision-making approaches, particularly multi-criteria decision-making (MCDM) techniques, have been developed to support well-informed and dependable choices. This study develops a new synergistic integration of Interval-Valued Fuzzy Level-Based Weight Assessment (IVF-LBWA) and Interval-Valued Fuzzy Multi-Attribute Ideal Real Comparative Analysis (IVF-MAIRCA), to improve decision-making in uncertain environments. In contrast to traditional methods, these approaches utilize interval-valued fuzzy numbers, thereby increasing the precision of project ranking and selection. An application example involving five projects and six evaluation criteria is provided to demonstrate the practical application of these methods. The results indicate that IVF-LBWA and IVF-MAIRCA yield stable and consistent project rankings, reinforcing their applicability in real-world scenarios. A sensitivity analysis was performed across 40 different criteria weighting scenarios to evaluate the impact of weight variations on project rankings. The results demonstrate that the proposed integrated approach preserves ranking stability, reflecting decision-makers’ priorities and the relative importance of each criterion. These findings validate its effectiveness in managing uncertainty and supporting reliable decision-making. The findings confirm that this approach provides a systematic and reliable framework for sustainable project portfolio selection. By enhancing decision accuracy and strengthening resilience to uncertainty, it enables decision-makers to align project selection with long-term sustainability, resource efficiency, and strategic objectives.

A resilient supplier is able to persevere in the face of disruptions and risks. Selecting resilient suppliers is crucial for businesses to receive high-quality services quickly and at a low cost. Clustering resilient suppliers facilitates identifying the most efficient and resilient ones. Mathematical models used to evaluate supplier resilience and cluster suppliers have limitations when addressing large-scale problems and fuzzy data. New techniques, such as Machine Learning (ML) methods, can be used to mitigate these limitations and predict supplier performance accurately. Few studies have used ML methods to cluster suppliers based on resilience criteria in imprecise data environments. To bridge this gap, this study proposes an integrated approach using Fuzzy Data Envelopment Analysis (FDEA) and ML methods to predict efficiency scores and classify suppliers based on resilience criteria. These methods were applied to evaluate a spinning and weaving factory as a real-life case study, based on resilience criteria. The results demonstrated that among five algorithms- Decision Tree (DT), Random Forests (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Logistic Regression (LR) - the SVR algorithm had the best performance in predicting the efficiency and resilience of suppliers with the accuracy value of .85. Additionally, the suppliers were classified into weak, medium, and strong classes. Five ML algorithms were used to predict the class of new suppliers. Among the LR, DT, RF, KNN, and SVR algorithms, the DT had the highest accuracy value of 1, while the KNN had the lowest accuracy value of .55.