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.

Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to apply fuzzy logic to model the risk of forest fires in the Cajamarca-Peru region, incorporating variables that represent biological, topographic, socioeconomic, and meteorological factors. The analysis was based on the acquisition, editing and rasterization of the database, application of fuzzy membership functions and image fuzzification, fuzzy superposition and spatial reclassification of forest fire risk. The results obtained show that 71.68% of the area is under very low or medium forest fire risk. However, 28.32% of the study area has a high to very high fire risk, which makes the occurrence of fires susceptible to the lack of rain and water in the soil. It was found that biological, topographic, and socioeconomic factors with their respective variables are directly influenced by meteorological factor variables such as temperature, rainfall and water availability. Fuzzy logic offered flexibility in modeling wildfire risk in the region, proving to be a useful tool for predicting and mapping wildfire risk.

Fires are devastating risky events in forests, having a negative effect on resources, biodiversity, economics, animal life, and putting people in danger. The goal of this study is to use geospatial techniques to identify areas in Jordan that are at risk of forest fires. The research area extends 50 kilometers north and 15 kilometers east from the Dead Sea. The forest fire risk zones map was developed using six factors: land cover class, aspect, proximity to settlements, elevation, slope, and proximity to roads. All of the factors have been selected based on their fire sensitivity or capacity to cause fire. In this study, a Turkish model with fuzzy logic and Analytical hierarchy analysis (AHP) was utilized to classify the area into five categories of risk ranging from very low to very high. According to the findings, approximately 12.12% of the study area is classified as very low risk, 25.54 % is classified as medium risk, while 12.84% is classified as very high risk. Over the last ten years, the map has been confirmed by prior fire occurrences using data from civil defense archives. This conclusion was very useful in gaining an understanding of the geographical distribution of fire-vulnerable zones. The research found that the GIS approach combined with AHP and fuzzy logic is a useful tool for estimating such kinds of maps.

The growing adoption of communication and automation technology in smart energy grids has greatly amplified their vulnerability to cyber-attacks, and therefore, intrusion detection in a timely and reliable manner has become a pressing necessity. In this context, this paper will seek to fill this gap by introducing a hybrid fuzzy-deep learning framework, which combines uncertainty-sensitive feature modeling with spatiotemporal deep representation learning. A convolutional recurrent neural network is used to encode coordinated spatial and temporal patterns of attacks in the form of graded representations of raw grid and network measurements using the help of fuzzy logic. With this integration it is possible to have end to end learning in nonstationary and ambiguous operating conditions. The proposed framework achieves a detection accuracy of 97.6%, an F1-score of 0.96, and a false positive rate of 3.1%, outperforming representative machine learning and deep learning baselines. In addition, the average detection latency of 29.6 ms confirms its suitability for real-time monitoring applications. The primary value of the work is its ability to show that the systematic combination of the uncertainty modeling based on fuzzy with deep spatiotemporal learning can considerably increase the reliability of detection and the feasibility of operation, which can provide a viable way to achieve the goal of resilience in cybersecurity of smart energy grids.

The challenge of improving construction safety performance is observed in many countries. Safety is considered by practitioners and researchers as an important topic in construction industry sites. Despite the findings of implementing safety programs, it is revealed that accidents and injuries are not perfectly reduced in construction projects. In the literature, authors tried to establish several frameworks and proposed methods to reach this objective by identifying the key factors affecting safety performance. The aim of this study is to present critical factors used in the implementation of safety programs and to explore their relationships using Interpretive Structural Modeling (ISM). Then, via ISM technique, the overall structure among factors was revealed. By using the Fuzzy MICMAC analysis, the factors were classified into four groups based on their driving power and dependence power. The results showed that “Safety Training” and “Management Commitment” have the most important impact on safety programs, but also it is very important to study the interactions among factors at different stages. This analysis offers key resources for practitioners and decision makers by analyzing the relationships between factors and its driving or dependence strength. These results shed lights on the effective development of measures to facilitate the implementation of safety programs in the construction sector.

The relevance of using the provisions of the fuzzy logic theory in assessing the readiness of the enterprise to change is substantiated. This made it possible to objectively assess the current state of the enterprise, using the nonlinear principles of forming conclusions, to simulate the experts' reflections on the level of enterprise readiness to change. It is proposed to use the McKinsey 7S model in the process of structuring the enterprise's internal environment. On the basis of this model, a fuzzy-multiplier model for evaluating the enterprise's readiness for changes, is constructed, which is presented in the form of a hierarchical relationship between the input variables, groups of input variables; integral characteristics of the elements of the enterprise that characterize its activities, in particular: strategy, structure, systems, style/culture, staff, skills, common values and output variable and they characterize the integral indicators of enterprise readiness for changes. Questionnaires are developed and a survey of gas transportation company staff is conducted on readiness for changes. The result of the expert opinions elaboration is received on the basis of the following methods application; namely statistical processing of expert opinions; the method of pairwise comparisons and the method of fuzzy clusterization. The functions of all parameters membership of the constructed system are obtained. On the basis of the averaged membership functions of input and output parameter terms, the correspondence between membership functions and control rules according to Zadeh is created and the structure of the Mamdani type in the MATLAB system is synthesized. As a result, the assessment of the gas transportation enterprises readiness level to change is obtained. It is substantiated that the obtained results are considered as the basis for further effective decision making in order to ensure the development in the conditions of the instability of the functioning environment.

The rapid advancement of drone technology has expanded their applications across various sectors, necessitating robust real-time decision-making systems. Traditional algorithms often falter in dynamic and unpredictable environments. This paper introduces a fuzzy logic-based approach to enhance the decision-making capabilities of autonomous drones. Utilizing Monte Carlo simulations, the proposed model was evaluated through three distinct experiments involving 300, 600, and 950 scenarios respectively. The first experiment demonstrated an obstacle avoidance efficiency of 82.00%, an 8.00% reduction in energy consumption, a decision accuracy of 95.33%, and a mission success rate of 79.33%. The second experiment showed an avoidance efficiency of 82.50%, maintaining the energy consumption reduction at 8.00%, with a decision accuracy of 95.83% and a mission success rate of 78.33%. The third experiment achieved an avoidance efficiency of 82.11%, with an 8.00% reduction in energy consumption, a decision accuracy of 95.26%, and a mission success rate of 78.31%. These results highlight the superior performance of fuzzy logic in real-time decision-making for autonomous drones compared to traditional methods.

This paper aims to provide a fuzzy framework for aligning human resources strategies with busi-ness strategies at The University of Applied Science and Technology. This research is an applied research based on the purpose and the research time horizon is cross sectional. In this research, 15 experts are identified using snowball method. The necessary data are gathered based on 3 questionnaires including Delphi questionnaire, human resources (HR) and business strategic questionnaire. The results show that some couples of HR strategies were aligned with business strategies. Then “if-then” rules are described in FuzzyTech software and the study determines which business strat-egies must be aligned with human resources strategies using the analysis. The paper ends by providing some suggestions based on 16 possible scenarios.

As archipelagic region, an effort to effectively enhance and accelerate the development of each sub-districts to boost the rapid development of Southeast Maluku district in Indonesia cannot happen as long as the local government fails to identify the real potentials and power in fisheries sector of each sub-districts. Identification of each sub-district fisheries top sub-sector has to be based on the human resources, natural resources, infrastructure, current and potential market, current policy of local and central government. A multi-criteria decision making (MCDM) is one of the powerful tools to provide a better result based on complicated factors involved. This paper proposed an integrated MCDM, to tackle the complicated factors in order to provide the best commodities on each sub-districts. Bottom-up concept was used to have a comprehensive result, by combining Fuzzy logic with Analytical Hierarchy Process (AHP) to measure the factors using fuzzy logic with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for determining the top sub-sector in fisheries. For comprehensive assessment of macro factors the study used experts ranging from government, scientists, practitioners to NGOs. On the other hand, for micro factors the survey used field instructor, field officer, fishers and farmers. The results provide a guideline for local and central government to form a better policy regarding the development of each sub-district including farmers, fishers and coastal communities in each sub-district to focus on commodities that benefited their regions’ resources and coastal community’s capabilities. By doing so, we hope to contribute on crafting an integrated and collective path on reaching the goal which is the welfare of coastal region.

This paper presents the case study of fuzzy statistical process control which has been simulated for variable and discontinuous production within a particular time frame in a key manufacturing work-shop. In order to reduce waste production and increase productivity, dimensional inspection from raw product is categorized into three groups: product of type A, product of type B, and discard. In first part, the appearance characteristics of product is defined as fuzzy membership function as the input of the system in order to allocate the output obtained from fuzzy inference of product to one of the three quality levels. Afterwards, each quality level is assigned to its own group by means of Monte Carlo simulation techniques. In the second part, with fuzzy development of a multinomial p chart, the production process is illustrated as a control chart within the particular period of time.