Nowadays, reducing energy consumption is an important target for energy-intensive manufacturing systems due to many reasons such as global warming, legal obligations and lowering company expenses. Therefore, this paper focuses on energy-based scheduling problem in manufacturing systems. A mixed-integer nonlinear programming (MINLP) model is developed for a single machine scheduling problem with the sequence-dependent setup times and different arrival times in order to minimize the total energy consumption and the total tardiness. An energy-based genetic optimization (EGOP) method is proposed by adopting the genetic algorithm (GA) approach, which is a heuristic method to solve the problem. The objective values and the computation times are compared with the analytical solution and the General Algebraic Modeling System (GAMS) solution so as to evaluate the performance of the proposed method. As a result, it is seen that the proposed EGOP method provides effective results.

Background/Objectives: The delayed repair of failed medical devices, particularly when multiple devices are simultaneously unavailable, may adversely affect patient safety and healthcare continuity, making repair prioritization a critical hospital decision problem. This study develops an integrated multi-criteria decision-making (MCDM) framework for prioritizing failed medical devices for repair and evaluates the consistency and robustness of the resulting priorities. Its principal contribution is to address repair prioritization as a distinct operational decision problem by integrating deterministic and fuzzy MCDM approaches and robustness analyses within a unified framework. Methods: Seven evaluation criteria—patient safety risk (PSR), device function (DF), device criticality (DC), utilization frequency (UF), availability of alternative devices (AAD), resource impact (RSI), and device age (DA)—were considered. The framework was implemented in a hospital case study involving 32 medical devices using the Analytic Hierarchy Process (AHP), AHP-weighted scoring, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy Analytic Hierarchy Process (Fuzzy AHP), and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). Robustness was examined through ±10%, ±30%, and ±50% criterion-weight sensitivity analyses, criterion-removal analysis, comparative ranking analysis, and hospital expert-based AHP and class-based AHP scenarios. Results: In the baseline AHP-weighted scoring ranking, Anesthesia Machine (4.9233) and External Cardiac Pacemaker (4.8862) received the highest repair priorities. Comparisons with TOPSIS, Fuzzy TOPSIS, hospital expert-based AHP, and class-based AHP yielded Spearman’s rank correlation coefficient ($\rho$) values ranging from 0.8328 to 0.9952. Sensitivity analysis showed high overall ranking stability under substantial criterion-weight variations, while criterion-removal analysis also maintained high correlations ($\rho$ = 0.9901–0.9963), supporting the robustness of the overall ranking structure. Conclusions: The findings demonstrate that the framework can support hospital biomedical units in prioritizing failed devices, enhance the transparency and consistency of repair decisions, and contribute to more effective maintenance resource allocation.

In recent years, renewable energy (RE) has become a significant topic both globally and in Türkiye. Renewable energy systems (RESs), in particular, appear to play a significant role in decreasing many of the risks the world faces, such as global warming and increased carbon emissions. This issue is currently on the agenda of the United Nations Sustainable Development Goals (SDGs) under the heading of SDG 7: Affordable and Clean Energy, and research in this area is increasing over time. In this study, a decision-support model was developed to evaluate suitable sites for wind power plant (WPP) investments. In addition to considering the plant installation cost and operation and maintenance (O&M) cost, the model incorporates different pricing scenarios for transmission costs. Break-even times (BETs) were calculated for representative cities across the seven regions of Türkiye, taking into account the revenues obtained from the sale of energy generated by the WPPs. In addition, net present value (NPV), internal rate of return (IRR), and sensitivity analyses were conducted to further evaluate the long-term economic feasibility and robustness of the candidate investment locations. The results revealed that the developed framework and the comparative economic analysis could serve as a guide from an innovative perspective for entrepreneurs planning to invest in this sector in Türkiye.

The increasing adoption of sustainable production practices in the textile industry has highlighted the need to evaluate recycled raw materials from economic, environmental, and operational perspectives. This study develops an optimization-based decision-support framework to determine the preferred recycled cotton ratio in sock manufacturing by separately minimizing production cost and carbon emissions under operational constraints. The framework was developed using real industrial data from a single sock manufacturer, with the recycled cotton ratio (r) defined as the decision variable and wastage rate, defect rate, and production time incorporated as production-performance constraints. The model was implemented in Python 3.13.15 and solved using the Sequential Least Squares Programming (SLSQP) algorithm, with the solutions independently assessed through grid-based global verification and Pareto dominance analysis. Sensitivity, production constraint, raw-material price, electricity price, and break-even analyses were conducted to evaluate the stability of the preferred solution under different production and economic conditions. Under the baseline conditions, r = 1 was the preferred solution for both objectives, reducing production cost by 18.7% and carbon emissions by 34.9% compared with r = 0. The Pareto assessment identified r = 1 as the only non-dominated solution under the baseline parameterization. Although the preferred solution remained stable across the examined sensitivity and price scenarios, the break-even analysis showed that the economic preference shifted from r = 1 to r = 0 when the recycled-to-virgin cotton price ratio approached approximately 0.98. Overall, the proposed framework provides a structured decision-support approach for evaluating recycled cotton utilization by integrating economic, environmental, and operational considerations while identifying the conditions under which the preferred solution remains stable or changes.