In response to the global imperative of mitigating greenhouse gas emissions (GHGs) and the shifting landscape of business models toward multi-channel structures, this study delves into the intricacies of a green supply chain. Operating through both online and traditional channels with uncertain demands, the producer’s distribution strategy prompts an exploration of supply chain dynamics. Utilizing an integer programming model, this study calculates optimal prices, optimizes total profit, and minimizes transportation costs to curtail carbon dioxide emissions, depending on the transportation mode. Additionally, this study incorporates renewable energy sources into the production and transportation processes to further minimize carbon dioxide emissions. The integration of renewable energy not only supports environmental goals, but also contributes to the overall profitability of the supply chain by reducing energy costs. Employing a theoretical technique for linearization, the model, resolved through the Jimenez and TH methods, demonstrates efficacy in reconciling economic and environmental goals. The Jimenez method enables the transformation of fuzzy parameters into deterministic equivalents, allowing for a more reliable optimization during uncertainty, while the TH method provides an interactive fuzzy multi-objective approach, aligning the model’s dual objectives for both economic and environmental goals. Notably, when transportation costs to both markets are equal, the model prioritizes devices with a lower environmental impact, showcasing adaptability. Furthermore, the proposed solution empowers decision makers to influence pricing and enhance the entire supply chain’s profitability. In conclusion, this research offers nuanced insights, strategically aligning economic viability with environmental sustainability in the discourse on green supply chains.

Introduction: Demand forecasting in pharmaceutical supply chains is not a simple task. In regulated markets it becomes more difficult, because seasonality, epidemic waves, and also policy changes can make demand behavior unstable. This study proposes a hybrid residual learning approach for forecasting pharmaceutical demand in Türkiye. Methods: The model uses Support Vector Regression (SVR) together with Deep Neural Networks (DNN). In this structure, SVR estimates the main or baseline part of demand, and DNN tries to learn the remaining nonlinear residual variation. The framework was tested with real ERP-based demand data, including 10 critical pharmaceutical products in a 24-month period. For evaluation, temporal holdout testing was used. The model was also compared with ARIMA, Prophet, Random Forest, XGBoost, and LSTM. Statistical validation was done by paired t-tests and Diebold–Mariano tests. Results: The proposed SVR–DNN framework performed better than the standalone SVR and DNN models. It also stayed as the strongest model when it was compared with the wider benchmark models. The hybrid model gave the lowest forecasting errors, and it showed the highest explanatory performance among the tested models. Discussion: The results show that when baseline demand estimation is separated from residual correction, forecasting performance can be improved in pharmaceutical demand conditions that are sensitive to disruptions. SHAP analysis also helped the interpretation of model, because it showed the main factors that affect demand variation. The proposed framework gives an interpretable and context-aware forecasting model for pharmaceutical supply chains working under regulatory and seasonal disruption.

This study introduces a comprehensive model to evaluate multiple capabilities within the sustainable supply chain evaluation framework. The primary aim is to determine the significance of various capabilities in the context of sustainable supply chains. The research involved a sample of sixteen companies operating in Iran’s energy sector. The findings indicate that the majority of these companies are at level two in terms of capability. Therefore, it is recommended that these companies employ this model to assess their capability levels and identify any existing gaps. Methodologically, a checklist tool was used to refine the criteria using the fuzzy Delphi method. Subsequently, an appropriate model was chosen and developed by reviewing existing evaluation models. The criteria were compared and finalized using the Analytic Hierarchy Process. Finally, the criteria were further refined and validated through a fuzzy expert system, incorporating Adaptive Neuro-Fuzzy Inference System and Fuzzy Inference System. The developed model was then simulated and validated using MATLAB Simulink software (R2017b).

The final agreement on the timing of project completion is one of the obvious problems between project managers and their clients. There have been numerous reports of customers requesting shorter completion times than previously announced. This request will definitely affect the three project factors of overall cost, final quality of the project, and risk of implementation. This paper proposes a multipurpose cumulative complex linear programming to minimize "project overhead," "increase projects total risk" and "increase overall project quality" due to “time constraints." In other words, the proposed study is fully implemented among the four goals mentioned to shorten the project duration. Computational experiments have also been used to evaluate the performance of the proposed model. The main objective of this paper is to optimize the integration of the four factors of the survival pyramid (time, cost, quality, and risk) in industrial projects simultaneously under uncertainty. An innovative solution based on the multi-objective genetic algorithm (NSGA-II) is presented. This model is then used to solve a problem in another study and its results, strengths, and weaknesses compared to the previous model are evaluated. The results show the performance of the proposed model in all four factors is better than the previous models.

Introduction: Sustainable supply chain performance cannot be assessed only by looking at one group of indicators. It needs a framework that can bring environmental, social, and organizational factors together with operational outcomes. Traditional multi-criteria decision-making methods usually depend on fixed weights and mostly linear logic. This makes them weak when sustainability relations are nonlinear, or when variables affect each other in interaction form. For this reason, this study proposes an explainable machine learning framework to assess sustainable supply chain performance. Methods: The framework applies dual-model architecture. Random Forest regression is used for prediction of continuous Sustainability_Score, and XGBoost classification is used for grouping firms into sustainability categories. The dataset contains environmental indicators, organizational and human-capital variables, also structural indicators related to supply chain. Sustainability performance is measured by a composite ESG-based index. After that, this index is transformed into ordinal classes. SHAP analysis is used to explain effects of features and also interactions between them. The stability of these explanations is checked across cross-validation folds. The framework is also compared with baseline machine learning models and with TOPSIS, as one traditional MCDM method. Results: A holdout test set was used to evaluate the proposed framework. The Random Forest regression model reached R ² = 0.9094 on this test set. The XGBoost classification model also reached AUC = 0.9565. The SHAP values show that sustainability performance of firms is mainly driven by emissions intensity, organizational trust, and employee participation. The SHAP stability test also shows that feature importance remains stable across validation folds. Discussion: The findings show that sustainable supply chain performance is not made only by environmental factors. Social and organizational conditions also play important role in this performance. Based on interaction analysis, social factors become more visible and more influential when emissions are at lower level. Therefore, the proposed framework can be considered as an interpretable and data-driven alternative for fixed-weight MCDM approaches. It can also support ESG-oriented strategies, supplier evaluation, and procurement decisions which have sustainability focus.

Pharmaceutical supplier audits use different evidence related to service continuity, quality, traceability, and temperature control. This study uses seven operational indicators to examine whether three resilience-related profiles, assigned by an internal committee of five members, can be reproduced. It does not aim to predict independently observed disruption outcomes. The dataset covers 12 months of 2025 and contains 13,318 recorded evaluations of 441 anonymized suppliers from seven distribution hubs. In the ten-fold nested evaluation, suppliers were grouped and tree depth was selected within the inner folds. The decision tree obtained an accuracy of 0.9532, with a 95% t-based confidence interval of 0.9503–0.9562. Its macro-F1 was 0.9552 (0.9524–0.9579). For the October–December temporal test, suppliers had already appeared in the January–September training data. The accuracy was 0.9499. A further time-aware evaluation, with suppliers kept separate between training and testing, gave an accuracy of 0.9496 within the same pharmaceutical distribution network. This result does not represent external validation. In the test data permutation importance results, order fulfillment performance, the positively oriented Good Manufacturing Practice (GMP) alert score, and cold-chain logistics made the largest contributions. The committee classifications had limited agreement with clusters obtained using K-means, hierarchical clustering, and density-based clustering. The level of agreement also changed depending on the method selected. The trained decision tree has a depth of five, and its decision paths can be examined by human reviewers. These results show the agreement of the model with the committee’s recorded profiles. They do not show that actual shortages, disruptions, or recovery times can be independently predicted by the model.