Hybrid algorithms are widely used today to increase the performance of existing algorithms. In this paper, a new hybrid algorithm called IMVOSA that is based on multi-verse optimizer (MVO) and simulated annealing (SA) is used. In this model, a new method called the black hole selection (BHS) is proposed, in which exploration and exploitation can be increased. In the BHS method, the acceptance probability feature of the SA algorithm is used to increase exploitation by searching for the best regions found by the MVO algorithm. The proposed IMVOSA algorithm has been tested on 50 benchmark functions. The performance of IMVOSA has been compared with other latest and well-known metaheuristic algorithms. The consequences show that IMVOSA produces highly successful and competitive results.

Molecular electron density theory has been performed with the B3LYP/6-31(d,p) method to study the [3+2] cycloaddition processes between azidobenzene and propionaldehyde, the reactivity indices, activation and reaction energies are computed. The reaction and activation energies indicate that this [3+2] cycloaddition reaction is regiospecific, in good agreement with the experimental results. ELF examination revealed that the mechanism of these cycloaddition reactions takes place in two steps. In addition, a docking approach was performed on the products investigated, and the interaction with the protein protease COVID-19 (PDB ID: 6LU7), the results confirm that the presence of triazole and isoxazole rings increases the affinity of these products.

Lean manufacturing in the manufacturing sector corresponds to lean management in the service sector. When health services are considered, it is seen that the concepts that are important in the manufacturing sector have the same importance for health services. Because waste is the main problem of every sector. Lean management approach in the health sector enables improvements by developing solutions to many other problems of healthcare professionals, patients and hospitals, such as reducing transaction costs, ensuring patient safety, and reducing processing times. This study was carried out with the aim of establishing a lean health system by selecting the most appropriate lean management methodology in order to identify and prevent waste in health processes and to increase efficiency and service quality. In the study, the evaluation of lean methodologies that can be applied to prevent waste in health services is considered as a multi-criteria decision-making problem and a model has been proposed. In the proposed model, wastes were determined as research criteria and lean methodologies were determined as research alternatives. The solution phase of the problem was carried out in two stages with multi-criteria decision-making techniques. In the solution phase, the importance weights of 24 decision criteria were calculated with the SWARA method, while 5 alternatives were evaluated based on the decision criteria with the WASPAS method and the most suitable alternative was selected.

Nowadays, strategic planning and the establishment of long-term objectives are pivotal for companies from diverse economies based on geographical location, demographic distribution, financial challenges, and distinct customer portfolios. Defining strategic goals is vital more than ever however requires acceptance, adoption, and execution by the entire workforce. In this sense, this paper presents an integrated methodology that combines the Balanced Scorecard and the Interval-Valued Hesitant Fuzzy DEMATEL approaches to enhance strategic management in a Medical Device SME located in Istanbul-Turkey. The proposed approach aims to provide a holistic perspective on organizational performance and facilitate decision-making processes for sustainable growth and competitive advantage.

This study proposes a new scenario planning approach which consists of two main stages: evaluating scenarios under multiple criteria and selecting a manageable number of representative scenarios covering a wide range of future developments. In the evaluation stage, the interaction between criteria has been considered, which offers a significant contribution both to the scenario literature and practice. In the selection stage, a mathematical programming model has been developed to ensure the selection of distinct scenarios with high evaluation values. The approach is applied to an urban mobility system in a metropolitan area. The selected scenarios provide valuable insights into the future of urban mobility, serving as a basis for identifying strategies. The proposed approach does not offer a solution only for scenario planning problems, it can be effectively applied to similar problems in different areas.

The vehicle routing problem is widespread in terms of optimization, which is known as being NP-Hard. In this study, the vehicle routing problem with capacity constraints is solved using cost- and time-efficient metaheuristic methods: an invasive weed optimization algorithm, genetic algorithm, savings algorithm, and hybridized variants. These algorithms are tested using known problem sets in the literature. Twenty-four instances evaluate the performance of algorithms from P and five instances from the CMT data set group. The invasive weed algorithm and its hybrid variant with savings and genetic algorithms are used to determine the best methodology regarding time and cost values. The proposed hybrid approach has found optimal P group problem instances with a 2% difference from the best-known solution on average. Similarly, the CMT group problem is solved with about a 10% difference from the best-known solution on average. That the proposed hybrid solutions have a standard deviation of less than 2% on average from BKS indicates that these approaches are consistent.

In this study, the delamination resistance of carbon fiber reinforced polymers (CFRP) consolidated with titanium alloy at the interface between the metal and composite was investigated experimentally and numerically. End-notched flexure (ENF) tests were performed to assess the fracture toughness (GIIC) for Mode II crack expansion of Ti6Al4V titanium alloy/CFRP composite parts. The EFN test is applied to Ti6Al4V-carbon fiber/low melt poly (aryl ether ketone) (CF/LM-PAEK) and Ti6Al4V-carbon fiber/poly (ether ketone ketone) (CF/PEKK) composites with the [0°]24 stacking sequence of unidirectional (UD) fibers. Experimental results indicate that the LM-PAEK composites exhibited Mode II strain energy release rate values 27.64 % higher than those of the PEKK composites. The finite element simulation by LS‐DYNA shows good correlations with the experimental results, with an average error of 5.44 % for the PEKK and 10.58 % for the LM-PAEK, respectively.

The current research addresses the inventory classification problem of community pharmacies, which have a dual role as both a vital component of the pharmaceutical supply chain and a typical retail store. Despite the existing literature indicating that pharmacists may lack knowledge on inventory management, it seems that the MCIC literature is weak in explaining how pharmacists can benefit from MCDA techniques in all aspects. To bridge this gap, the study aims to demonstrate that pharmacists can utilize MCDA techniques to gain deeper insights beyond mere classification in the context of inventory management. Real-world data from a community pharmacy in Turkey was classified using the EDAS method. Sensitivity analysis was performed for MCDA inputs, about which pharmacists may lack information. Scenario findings based on criterion weights and threshold values offer important managerial implications for pharmacists. This study provides a critical contribution to the literature on inventory management in community pharmacies by highlighting the potential of MCDA techniques to support decision-making beyond mere classification. The sensitivity analysis also sheds light on areas where pharmacists may lack knowledge and suggests ways to address these gaps. Overall, the study underscores the need for pharmacists to have a deeper understanding of inventory management and highlights the potential benefits of MCDA techniques in addressing this challenge.

The purpose of the loop layout problem is to determine the optimal sequence for operations by giving a cyclical arrangement of the machines along a given route. Calculating the initial cost of processes is critical in general. Additionally, these expenses can be reduced by proposing an alternate flow for the system's components. It has been observed that machines at gyms are utilized in accordance with a certain program, and so they require the optimal arrangement to accommodate these programs. In this study, the best cyclical arrangement of the machines in a gym was determined using an intuitive method. Customers enrolled in various training programs will be required to change machines in accordance with the sequence of the machines in their training program. The purpose of this study is to determine the optimal layout of these machines in order to alleviate gym traffic congestion and provide a more intuitive movement plan for consumers. The purpose is to build a layout plan that minimizes backward movement using an objective function created from the positions of the machines in the training programs, the number of customers who use these programs, and the frequency with which the machines appear in multiple programs. For the first time in gyms, an effective solution to the machine layout problem has been proposed using an optimization approach.

This study considers a customer order scheduling (COS) problem in which each customer requests a variety of products (jobs) processed on a single flexible machine, such as the computer numerical control (CNC) machine. A sequence-independent setup for the machine is needed before processing each product. All products in a customer order are delivered to the customer when they are processed. The product ordered by a customer and completed as the last product in the order defines the customer order’s completion time. We aim to find the optimal schedule of the customer orders and the products to minimize the customer orders’ total completion time. We have studied this customer order scheduling problem with a job-based processing approach in which the same products from different customer orders form a product lot and are processed successively without being intermingled with other products. We have developed two mixed-integer linear programming models capable of solving the small and medium-sized problem instances optimally and a heuristic algorithm for large-sized problem instances. Our empirical study results show that our proposed tabu search algorithm provides optimal or near-optimal solutions in a very short time. We have also compared the job-based and order-based processing approaches for both setup and no-setup cases and observed that the job-based processing approach yields better results when jobs have setup times.