Mehdi Safaei

Shipping- and trade-exposed economies need measures that distinguish cost pressure from operational disruption. We construct a six-block Monthly Systemic Cost-pressure and Supply-chain index (MSCS) for Türkiye using 78 monthly observations and estimate Bayesian structural time-series counterfactuals from January 2020–February 2026. March 2026 observed MSCS is 115.608, against a counterfactual mean of 111.264; the deviation is 4.344 points (90% CrI 2.355 to 6.225; Pr > 0 = 1.0000). The March–June cumulative deviation is 17.953 index-point months (90% CrI 7.454 to 28.193; Pr > 0 = 0.9975). Energy and fertilizer–agriculture show the clearest March block signals. Transport-and-storage H-PPI increased in observed levels but remained below its fitted trend mean, and the March Logistics–Transport block credible interval included zero. Neither Mersin throughput nor nominal intermediate-goods imports showed an abnormal downside deviation under the specified trend models. These results indicate cost pressure, not directly established operational disruption. Because the post-event window contains only four monthly observations, the estimates do not establish medium- or long-run persistence. Origin-specific standardisation, rolling forecasts, 26 placebos and specification checks evaluate predictive reliability; the placebo rank is 1/27. The estimates remain conditional on model assumptions and a revised data snapshot rather than a real-time vintage replay. The contribution is a transparent pressure-monitoring architecture connecting shipping, logistics and trade indicators to posterior uncertainty and an illustrative institutional-review protocol.

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.

Optimization and Strategic Decision Models in Industrial Engineering examines how analytical methods support decisions in production, logistics, healthcare, and infrastructure systems. Its eleven chapters address problems ranging from delivery prediction and warehouse performance to capacity planning, industrial location selection, and green vehicle routing. Further contributions explore pharmaceutical supply chains, maintenance contractor evaluation, remanufacturing, occupational safety, post-disaster transportation resilience, and the development of generative AI products in healthcare. The volume brings together optimization, multi-criteria decision-making, machine learning, cooperative game theory, and scenario analysis. Through modelling studies, decision frameworks, and a review of pharmaceutical supply chain optimization, it examines how costs, risks, environmental impacts, and stakeholder priorities enter the decision process. For researchers, graduate students, and practitioners, this collection offers examples of how industrial engineering methods can be applied across sectors, with attention to the assumptions and practical conditions that shape their use.

Geleceğin mühendisliği, teknolojik yeniliklerin çevresel ve toplumsal sorumluluklarla harmanlandığı sürdürülebilir bir vizyonla biçimlenmektedir. İklim krizleri, kaynak kıtlığı ve hızlı sanayileşmenin beraberinde getirdiği çevresel zorluklar, elektrik-elektronikten makineye, inşaattan bilgisayara, çevre ve kimya mühendisliğinden malzemeye kadar tüm disiplinlerde dönüşümü zorunlu kılmaktadır. Mühendislikte Yenilikçi Yaklaşımlar ve Sürdürülebilir Gelecek; HDR/EGS jeotermal rezervuar performanslarından hava alıklarında sürükleme azaltımına, Endüstri 5.0 ve akıllı İSG teknolojilerinden 3B/4B yazıcılarda döngüsel malzemeye kadar geleceğin teknolojilerine odaklanıyor. Yapı kaplamalarında yağmur suyu hasadından sürdürülebilir atıksu yönetimi ve Sıfır Sıvı Deşarjına (ZLD), tribolojide yeşil ester kullanımından tekstil boyamacılığında yeşil kimyaya, köpük beton ve silis dumanı uygulamalarından Yaşam Döngüsü Değerlendirmesi (LCA) ve karbon optimizasyonuna uzanan zengin içeriğiyle, çok disiplinli mühendislik uygulamalarının en güncel örneklerini sunuyor. Araştırmacılar, akademisyenler, lisansüstü öğrenciler ve sanayi paydaşları için hazırlanan bu eser, teori ile uygulamayı buluşturarak sürdürülebilir, dirençli ve yenilikçi bir mühendislik geleceğine yönelik kapsamlı bir yol haritası çizmektedir.

COVID-19 created major pressure on global supply chains, but it remains uncertain whether this coincided with changes in countries’ logistics performance groups. This study investigates this issue using World Bank Logistics Performance Index data for 2016, 2018 and 2023. K-Means clustering was separately carried out on six LPI dimensions for 160 countries in both 2016 and 2018, and 139 countries in 2023. The 2023 edition reflects late-2022 survey responses. For transition analysis, 154 countries were matched in 2016–2018 and 134 in 2018–2023. In the primary three-cluster specification, 130 (84.42%) and 112 (83.58%), respectively, stayed in their former group; the latter period included 15 upward and seven downward movements. Silhouette favored two clusters, and alternative specifications changed some memberships. Five component means improved in the balanced 129-country panel in 2018–2023, while timeliness declined. The findings describe relative persistence and heterogeneous score changes, not a causal pandemic effect.

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.

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 article is an in-depth analysis of the performance and efficiency of various control systems used in quadrotor unmanned aerial vehicles (UAVs). The study is focused on the comparison of three main control approaches, including Sliding Mode Control (SMC), Fuzzy Logic Control (FLC), and an extended version of Sliding Mode Control with the use of the Gray Wolf Optimizer (SMC-GWO), as well as a supportive validation model the Genetic Algorithm (SMC-GA). Based on the Newton–Euler formulation, the mathematical model of a quadrotor has been developed to provide a true picture of the dynamic behavior of the quadrotor. The model was then implemented in MATLAB/Simulink 2025b to test the performance of the system in its nominal and perturbed conditions. The findings have shown that the hybrid SMC-GWO controller has significant improvement in response speed, accuracy, and stability compared to the other controllers. Precisely, the SMC-GWO demonstrated 78.46 percent decrease in rise time and 23.40 percent decrease in settling time compared to the traditional SMC, as well as a nearly negligible steady-state error (SSE = 0.0008) in the roll channel. The proposed controller in the pitch channel reduced the rise time by 93.65 percent and the settling time by 20.22 percent, with a much smoother and more stable tracking and an effectively negligible steady-state error (SSE = 0.0001). The hybrid controller in the yaw channel had a 77.94 percent better rise time and 23.16 percent better settling time, resulting in a steady-state error of 0.0022. In relation to altitude control, SMC-GWO decreased the rise time by 91.87 percent and settling time by 25.04 percent over classical SMC, yet the steady-state error was almost zero. Under constant, time-varying actuator disturbances, the SMC-GWO controller also demonstrated better system stabilization and trajectory-tracking behavior than both SMC and FLC, as well as slightly better behavior than SMC-GA in the presence of faults and disturbances. These results verify that a UAV control framework based on the combination of the Gray Wolf Optimizer and Sliding Mode Control is more resilient, quick, and significantly more precise.

Introduction: Optimization of distribution for essential medical products is important goal in health systems. For this purpose, the network design should consider economic, environmental, and social aspects together. This issue is especially serious in Türkiye, where regional disparities, infrastructure differences, and limited access to healthcare make pharmaceutical distribution more complicated. Methods: This study develops a tri objective mathematical programming model for sustainable distribution network design of essential medical products in Türkiye. The model tries to minimize logistics cost and carbon emission at the same time, while it maximizes social benefit through regional job creation. Since the problem is NP hard, two multi objective metaheuristic algorithms, NSGA II and MOPSO, were applied for solving large scale instances. Their performance was evaluated by mean ideal distance, spacing metric, number of Pareto solutions, and computational time. Validation was done through comparison of metaheuristic results with exact MILP solutions in small scale instances. Results: The results show that both algorithms produced near optimal solutions with good quality. MOPSO had faster convergence, lower mean ideal distance, and also more uniform Pareto front, while NSGA II gave wider spread of alternative solutions. No statistically significant difference was found in the number of non-dominated solutions. The case study results also show that the proposed model can help simultaneous improvement of economic, environmental, and social performance in Turkish healthcare distribution network. Discussion: The proposed framework gives a practical decision support basis for sustainable and resilient healthcare distribution network design in Türkiye. Its contribution is that it brings together cost, carbon emissions, and regional employment generation in one unified multi echelon optimization structure, and also shows that metaheuristic solution methods can be useful for real world healthcare logistics planning.