Perishable agricultural supply chains face significant uncertainties in production yield, market demand, and availability of resources. This paper presented a multi-period, multi-echelon, and multi-product mathematical model for a perishable agricultural supply chain network comprising farms, cold storage facilities, and wholesale markets. The model determined optimal allocation of cultivation area, harvest timing, duration of storage, and product flow distribution to minimize total supply chain costs, including production, transportation, storage, and import costs. A key innovation is the integration of interval programming to handle uncertainty in production costs, transportation rates, and water availability. Three metaheuristic algorithms including particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) were employed to solve the nondeterministic polynomial (NP)-hard problem. A comprehensive case study of three major perishable crops, i.e., potato, onion, and tomato, across 31 Iranian provinces and 24 candidate cold storage facilities attested to the effectiveness of the model. Results demonstrated that PSO outperformed GA and SA in solution quality by achieving a total cost of 64.14 trillion Rials, with 93.7% of costs attributed to production, 5.2% to transportation, and 1.1% to storage. The model reduced final product prices to one-third of market averages. Sensitivity analyses revealed that production cost was the most sensitive parameter, with a 100% increase triggering economically viable imports. Water availability reduction below 10% of baseline rendered domestic production infeasible. The model achieved 19.9% average water savings (1.062 billion cubic meters annually) through optimized cultivation patterns. The proposed framework provided agricultural policymakers with a quantitative tool for balancing domestic production, investment in storage capacity, and import decisions under uncertainty.

Rice is a strategic food commodity, and its supply chain involves farms, mills, distribution centres, and markets. This study presented a multi‑objective mathematical model for a rice supply chain that minimized total costs (economic objective), soil erosion caused by water used in cultivation (environmental objective), and total water consumption. A real case study from Iran including four major rice‑producing regions was analysed under three water availability scenarios. To handle uncertainty in rainfall and irrigation water, stochastic programming was applied and the multi‑objective model was solved using extended goal programming. Sensitivity analyses examined changes in water availability, objective function weights, and import limits. Results demonstrated that under water‑scarce conditions, production in arid regions (e.g., Khuzestan) was not economically or environmentally viable, leading to increased imports. The model provides a practical tool for policymakers balancing food security, cost, environmental sustainability, and water conservation.

Customer retention in the telecommunications industry poses a critical challenge in data-driven business operations, while high predictive accuracy does not necessarily translate into superior commercial outcomes under asymmetric misclassification costs. This study investigated a profit-oriented decision analytics framework for customer churn management by integrating predictive performance with business value optimization. A cost-sensitive Cost-Sensitive Improved Sparrow Search Strategy Algorithm Stacking (CS-ISSA-Stacking) framework was developed by incorporating customer lifetime value (CLV) and marketing intervention costs into an Expected Total Profit (ETP) objective function. An Improved Sparrow Search Algorithm (ISSA) was constructed using Tent chaotic mapping and Cauchy mutation mechanisms to enhance global optimization capability. EXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) were employed as heterogeneous base learners, and ISSA was used to optimize ensemble fusion weights and decision thresholds for profit-driven prediction. The proposed framework could effectively improve ETP while maintaining competitive predictive performance. Compared with conventional prediction models optimized solely for classification accuracy, the proposed approach achieved a substantial improvement in customer retention profitability. The findings demonstrated that the optimized decision threshold significantly reduced the false negative rate in high-value customer identification. Furthermore, SHapley Additive exPlanations (SHAP)-based interpretation revealed that monthly charges, contract type, and tenure were the most influential factors affecting customer churn behavior. Profit-sensitive decision mechanisms provided a more effective strategy than traditional accuracy-oriented prediction approaches in customer churn management. The proposed framework provides a practical decision-support tool for intelligent customer retention and offers new insights into integrating business analytics with strategically operational decision making in the competitive telecommunications environments.

The increasing integration of environmental, social, and governance (ESG) considerations into financial markets has raised fundamental questions regarding their roles in investment decision making. In particular, it remains unclear whether ESG-oriented investment strategies mirror substantive changes in portfolio construction or primarily follow prevailing market trends. This study examined the decision relevance of ESG signals by analyzing the behavior and performance of ESG-oriented mutual funds. Using a sample of 41 funds, a data-driven analytical framework was employed to evaluate portfolio composition, risk–return performance, and the determinants of ESG ratings. The analysis first considered whether ESG funds systematically allocated capital toward firms with stronger ESG profiles. Although a modest tilt toward higher-rated companies was observed, the differences relative to conventional funds remained limited and, in most cases, statistically insignificant, thus indicating that ESG considerations were not the dominant driver of portfolio selection. The second part evaluated fund performance within a risk–return framework using benchmark-adjusted measures and information ratios (IR) . While all funds generated positive returns over the sample period, the majority failed to outperform their benchmarks. Only a small subset exhibited consistently favorable risk-adjusted performance. These findings suggested that ESG-oriented strategies did not provide a reliable basis for achieving superior financial outcomes and might involve trade-offs in portfolio allocation. Finally, cross-sectional regression analysis demonstrated that ESG ratings were strongly associated with firm-specific characteristics, especially size and profitability. This result indicated that ESG scores might reflect underlying financial capacity rather than deliberate sustainability-oriented decisions. Taken together, this study implied that ESG signals offered limited standalone values for guiding decisions of investment. Effective portfolio design therefore requires a broader analytical approach that integrates ESG metrics with conventional financial indicators.

With the rapid expansion of e-commerce, last-mile delivery in express logistics faces significant challenges, including low efficiency and high operational costs. Taking the Xiqing District of Tianjin as a case study, this research proposes a three-stage framework integrating complex network theory and machine learning. First, the Louvain algorithm is employed to achieve intelligent partitioning of delivery areas, resulting in a modularity increase to 0.789. Second, an eXtreme Gradient Boosting (XGBoost) model is utilized to predict terminal service modes, achieving an accuracy of 87.8%. Finally, a route planning model is constructed using Particle Swarm Optimization (PSO). To validate these methods, a three-day logistics system simulation was conducted via AnyLogic to evaluate the effectiveness of different delivery policies. The results demonstrate that, compared to traditional independent delivery, the joint delivery approach reduces total costs by 25.32%. Furthermore, by introducing a carbon emission accounting model, leading to an estimated 25% reduction in daily carbon emissions, achieving a win-win situation for both economic and environmental benefits.