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.

Amongst various structures, 2D panels are widely used in many industries and in each application, there are many ambient vibrations which can be converted into electrical energy. The very efficient method to utilize can be using piezoelectric patches which are stuffiest enough for various applications like energy harvesting. This study presents a novel approach for investigating energy harvesting in smart structures using composite panels integrated with piezoelectric patches. The panels are chosen to symmetry-balanced laminated composites, and modal and harmonic analysis conducted using the Rayleigh-Ritz method. To compute the kinetic and potential energy components of the piezoelectric patches at a local level, piecewise Heaviside functions are employed, and these energy components are integrated into the equations of motion together with those of the host composite plate. The results of the numerical method are validated by a commercial finite element software (FEM) COMSOL and there is a good match between FEM and this paper results. By subjecting the laminated composite with piezoelectric patches to forced vibration while varying the lamination parameter, the power output is optimized. The findings emphasize the substantial impact of the lamination parameter on power output, indicating that modifications can result in significant power output increase.

As customers' orientation towards environmental products increases, manufacturers and other members of the supply chain are looking for ways to conduct their operations in an environmentally and cost-effective manner. To find a solution that compensates these requests, a game theoretical approach is developed for a two-stage green supply chain consisting of a supplier and a producer. A Stackelberg game model based on asymmetric information structure is developed to find the optimal lot sizes and raw material sales price for raw material supplier, and the product sales price and the environmental cost for the producer. The developed approach is illustrated on a real-world case study that deals with production and raw material procurement processes of a plastic plug and compared to a scenario in which no environmental expenditures exist. The effect of changes in the model has been observed by tuning some significant parameters with the experimental design approach.