Freight allocations must meet service and environmental requirements before demand and emission factors are observed. This paper studies a moment-based distributionally robust extension of the Hitchcock problem with joint demand protection and a separate probabilistic emission cap. Established Bonferroni and Chebyshev–Cantelli bounds give a safe second-order cone program for fixed destination risk budgets. Nonnegative extremal distributions provide fixed-plan reliability diagnostics, and a convex regime allows the budgets to be optimized. Experiments use an illustrative network and twenty larger synthetic networks, with five cap positions per larger network. They show that the cap may change mean emissions and their variability in different directions. Optimizing service-risk budgets makes the tested cap inactive on the illustrative network and on five of the larger networks, while a carrying-cost sensitivity restores its activity in the illustrative case. Controlled dependence changes also remove the safe model’s feasibility margin without increasing marginal emission variances. The results show how service policy, inventory cost, and emission dependence shape the cost of environmental protection under the stated moment assumptions.
This paper develops a comprehensive inventory optimization framework for non-perishable items within a two-warehouse system under a dynamic demand environment influenced jointly by selling price and time. Unlike conventional inventory models that assume static demand structures and single storage facilities, the present study integrates price-sensitive and time-varying demand with warehouse-specific deterioration effects. The proposed model considers an own warehouse with limited capacity and a rented warehouse characterized by higher holding costs. A realistic inventory depletion model is formulated using a system of differential equations that captures both demand-driven consumption and deterioration across multiple time intervals. The decision problem is formulated to jointly determine the optimal selling price and replenishment cycle length that maximize the average profit per unit time. Due to the nonlinear and implicit structure of the model, a hybrid numerical optimization approach is employed. The existence of at least one optimal solution is established through the theoretical analysis, while a hybrid numerical optimization approach is employed to identify the reported solution. Numerical optimization and computational verification support the reported optimum for the investigated parameter setting. A detailed sensitivity analysis is conducted to examine the influence of demand parameters, cost components, and warehouse characteristics on optimal decisions. The findings demonstrate that incorporating both price and time dependence in demand significantly enhances profit performance and provides greater flexibility in decision-making. The model offers valuable managerial insights for industries dealing with non-perishable goods, particularly in environments where pricing strategies and storage policies must be coordinated under dynamic market conditions.
Nowadays, the Internet of Things (IoT) sector is crucial for constructing smart paradigms. Privacy and security are taken as significant problems in any real-world applications dependent on the IoT system. The protection hurdles in IoT-aided devices create security threats that hinder smart sector development. Hence, there is an essential for Intrusion Detection Systems (IDSs) developed for IoT sectors to minimize the IoT-based security attacks that create several security issues. Conventional IDSs cannot be utilized in general IoT-aided networks because of the multiple attributes concerning computational capability, memory capacity, and battery life. To conquer these problems, multiple IDSs have been suggested in research works. But, various IDSs meet issues of low accuracy and high face alarm in the detection of anomalies. To address the vulnerabilities in the conventional systems, an ensemble deep learning mechanism is recommended to recognize the presence of intrusion in the IoT sector. Firstly, the raw data is collected from standard data sources. Furthermore, it is followed by feature extraction, which is done by utilizing Principal Component Analysis (PCA), T-distributed Stochastic Neighbor Embedding (TSNE) and statistical features. Subsequently, the overall features are fused and the weights optimally determined using the Fitness Value of Pelican Optimization Algorithm (FVPOA). Finally, the fused features are subjected to the Ensemble Networks (EnsNet), which are built with Long Short-Term Memory (LSTM), Deep Markov Random Field (Deep MRF), Recurrent Neural Network (RNN), and Ridge Classifier. Then, the final detected outcome is acquired by average score-based fusion. Therefore, the validation of the model takes place by employing divergent measures. On the contrary, the proposed work achieves a higher detection rate and also helps to evade third-party intrusion in the network.
In complex environments with incomplete information, multi-criteria decision analysis (MCDA) techniques, such as analytic hierarchy process (AHP) and best worst method (BWM), are widely applied based on decision makers’ preferences. However, both methods face challenges in fluctuating environments where criteria or alternatives frequently change, or criteria weightings vary significantly. To reduce decision-making workloads in such dynamic scenarios, this study proposes a novel approach, namely the absolute reference method (ARM). A consistency formulation for ARM is also developed, applicable to different measurement scales and extendable to assess consistency in the linear BWM. To validate the proposed ARM and its consistency formulation, some illustrative examples are provided. Furthermore, a simulation experiment was conducted with a controlled number of criteria and varying levels of input inconsistency. The empirical results indicate that ARM achieved a significantly lower average deviation (0.274), measured by the mean square error between the comparison inputs and their derived weight ratios, outperforming both the BWM (0.389) and the AHP (0.436), equivalent to 29.6
This study examines AI adoption among Indian manufacturing SMEs using an integrated Technology-Organization-Environment (TOE) and Resource-Based View (RBV) framework. Through mixed-methods analysis of 450 SMEs and 25 executive interviews, we investigate adoption drivers, barriers, and performance outcomes. Results show 68 β = 0.42, p< 0.001), organizational culture ( β = 0.38, p< 0.001), and environmental factors ( β = 0.31, p< 0.01) are significant predictors of AI adoption. Mediation analysis confirms that AI adoption mediates the relationship between TOE factors and performance outcomes (indirect effects ranging from 0.14 to 0.19, all p< 0.05), with AI adoption serving as the mechanism through which TOE antecedents translate into organizational performance. Multi-group analysis reveals heterogeneous effects across firm sizes and industry sectors, with technology readiness more critical for larger firms and organizational culture more decisive for micro enterprises. We contribute an empirically grounded decision-support framework for staged AI implementation and investment prioritization, translating SEM findings into actionable heuristics for SME managers. AI adopters show significantly higher scores on operational efficiency (23 p< 0.001, Cohen’s d = 0.98) and competitive advantage (18 p< 0.001, Cohen’s d = 0.71) compared to non-adopters in cross-sectional analysis, consistent with the strategic value of systematic adoption approaches. This study investigates why Indian manufacturing small and medium enterprises (SMEs) adopt artificial intelligence (AI) and what happens to their performance when they do. We surveyed 450 SMEs and interviewed 25 senior executives across five states and five industry sectors. We find that while 68
