This paper aims to design an inventory model for a retail enterprise with a profit maximization objective using the opportunity for a price discount facility given by a supplier. In the profit maximization objective, the demand should be increased. The demand can be boosted by lowering the selling price. However, lowering the selling price may not always give the best profit. Impreciseness plays a vital role during such decision-making. The decision-making and managerial activities may be imprecise due to some decision variables. For instance, the selling price may not be deterministic. A vague selling price will make the retail decision imprecise. To achieve this goal, the retailer must minimize impreciseness as much as possible. Learning through repetition may be a practical approach in this regard. This paper investigates the impact of fuzzy impreciseness and triangular dense fuzzy setting, which dilutes the impreciseness involved with managerial decisions. Based on the mentioned objectives, this article considers an inventory model with price-dependent demand and time and a purchasing cost-dependent holding cost in an uncertain phenomenon. This paper incorporates the all-units discount policy into the unit purchase cost according to the order quantity. In this paper, the sense of learning is accounted for using a dense fuzzy set by considering the unit selling price as a triangular dense fuzzy number to lessen the impreciseness in the model. Four fuzzy optimization methods are used to obtain the usual extreme profit when searching for the optimal purchasing cost and sale price. It is perceived from the numerical outcomes that a dense fuzzy environment contributes the best results compared to a crisp and general fuzzy environment. Managerial insights from this paper are that learning from repeated dealing activities contributes to enhancing profitability by diluting impreciseness about the selling price and demand rate and taking the best opportunity from the discount facility while purchasing.

During the past two decades, value management (VM), has developed into a recognized construction practice. However, the methods and activities associated with VM adopt informal approaches in developing countries. This study aims to explore the critical success factors (CSFs) of VM implementation. Consequently, VM CSFs were investigated from the previous literature and further categorized over a semi-structured interview. The importance of these CSFs investigated by 335 structured questionnaires completed by residential building professionals. Subsequently, the exploratory study using the exploratory Pearson correlation of the VM CSFs was employed to validate the categorization resulting from a semi-structured interview and pilot study phases. Based on the validation results, the VM CSFs may be divided into four dimensions: culture and environment, workshop dynamics, stakeholder and knowledge, and standardization. Through important relative index (RII) analysis, the essential CSFs creates a VM team from a variety of disciplines, VM knowledge, experience of participants, and professional experience of the different participants’ diverse disciplines. In addition, this research used a stationary analytic strategy to evaluate the degree to which VM critical success factors (CSFs) have been incorporated into residential construction projects in Egypt. The results revealed that “establishing the roles and purposes of various professions” was the stationary success factor for adopting VM. This research establishes a road map for successful VM implementation via VM CSFs in Egypt and other underdeveloped nations. Stakeholders in the residential construction sector would benefit from this study by learning more about VM CSFs and how they may be used to increase the value of their projects.

Multi-criteria decision-making (MCDM) is now frequently utilized to solve difficulties in everyday life. It is challenging to rank possibilities from a set of options since this process depends on so many conflicting criteria. The current study focuses on recognizing symptoms of illness and then using an MCDM diagnosis to determine the potential disease. The following symptoms are considered in this study: fever, body aches, fatigue, chills, shortness of breath (SOB), nausea, vomiting, and diarrhea. This study shows how the generalised dual hesitant hexagonal fuzzy number (GDHHχFN) is used to diagnose disease. We also introduce a new de-fuzzification method for GDHHχFN. To diagnose a given condition, GDHHχFN coupled with MCDM tools, such as the fuzzy criteria importance through inter-criteria correlation (FCRITIC) method, is used for finding the weight of criteria. Furthermore, the fuzzy weighted aggregated sum product assessment (FWASPAS) method and a fuzzy combined compromise solution (FCoCoSo) are used to rank the alternatives. The alternative diseases are chosen to be malaria, influenza, typhoid, dengue, monkeypox, ebola, and pneumonia. A sensitivity analysis is carried out on three patients affected by different diseases to assess the validity and reliability of our methodologies.

Pythagorean Fuzzy Numbers are more capable of modeling uncertainties in real-life decision-making situations than Intuitionistic Fuzzy Numbers. Majority of research in Pythagorean Fuzzy Numbers, used in Multiple Criteria Decision-Making problems, has focused on developing operators and decisionmaking frameworks rather than the methodologies of generating the Pythagorean Fuzzy Numbers. Hence, this study aims at developing a novel aggregation method to generate Pythagorean Fuzzy Numbers from decision makers' crisp data for Multiple Criteria Decision-Making problems. The aggregation method differs from other methods, used in generating Intuitionistic Fuzzy Numbers, by its ability to measure the uncertainty degrees in decision makers' information and using them to generate Pythagorean Fuzzy Numbers. Initially, decision makers evaluate alternatives based on preset criteria using crisp decisions (i.e., crisp numbers) which are assigned by decision makers. A normalization