Informatica

In this study, Intuitionistic Fuzzy Consistency Method (IF-FUCOM) and Grey Relation Analysis (GRA) were combined to assess the effects of Bacillus subtilis bacteria on concrete properties, as well as to determine the optimal bacteria concentration and curing day. Three different concentrations of bacteria were added to the mortar mixes, like 103, 105, and 107 cells/ml of water. Mortar samples were left to cure for 7 days, 14 days, and 28 days to evaluate compressive strength, water absorption, crack healing. According to the proposed algorithm, 105 bacteria are the optimal concentration, while 28 days is the ideal curing time.

The focus of this paper is on the criteria weight approximation in Multiple Criteria Decision Making (MCDM). An approximate weighting method produces the weights that are surrogates for the exact values that cannot be elicited directly from the DM. In this field, a very famous model is Rank Order Centroid (ROC). The paper shows that there is a drawback to the ROC method that could be resolved. The paper gives an idea to develop a revised version of the ROC method called Improved ROC (IROC). The behaviour of the IROC method is investigated using a set of simulation experiments. The IROC method could be employed in situations of time pressure, imprecise information, etc. The paper also proposes a methodology including the application of the IROC method in a group decision making mode, to estimate the weights of the criteria in a tree-shaped structure. The proposed methodology is useful for academics/managers/decision makers who want to deal with MCDM problem. A study case is examined to show applicability of the proposed methodology in a real-world situation. This case is engine/vehicle selection problem, that is one of the fundamental challenges of road transport sector of any country.

We study an inventory control problem of a perishable product with a fixed short shelf life in Dutch retail practice. The demand is non-stationary during the week but stationary over the weeks, with mixed LIFO and FIFO withdrawal. The supermarket uses a service level requirement. A difficulty is that the age-distribution of products in stock is not always known. Hence, the challenge is to derive practical and efficient order policies that deal with situations where this information is either available or lacking. We present the optimal policy in case the age distribution is known, and compare it with benchmarks from literature. Three heuristics have been developed that do not require product age information, to align with the situation in practice. Subsequently, the performance of the heuristics is evaluated using demand patterns from practice. It appears that the so-called STIP heuristic (S for Total estimated Inventory of Perishables) provides the lowest cost and waste levels.

Over the past decades, many methods have been proposed to solve the linear or nonlinear mixing of spectra inside the hyperspectral data. Due to a relatively low spatial resolution of hyperspectral imaging, each image pixel may contain spectra from multiple materials. In turn, hyperspectral unmixing is finding these materials and their abundances. A few main approaches to performing hyperspectral unmixing have emerged, such as nonnegative matrix factorization (NMF), linear mixture modelling (LMM), and, most recently, autoencoder networks. These methods use different approaches in finding the endmember and abundance of information from hyperspectral images. However, due to the huge variation of hyperspectral data being used, it is difficult to determine which methods perform sufficiently on which datasets and if they can generalize on any input data to solve hyperspectral unmixing problems. By trying to mitigate this problem, we propose a hyperspectral unmixing algorithm testing methodology and create a standard benchmark to test already available and newly created algorithms. A few different experiments were created, and a variety of hyperspectral datasets in this benchmark were used to compare openly available algorithms and to determine the best-performing ones.

The performance evaluation of public charging service quality is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In this paper, an extended TOPSIS model is established to provide new means to solve the performance evaluation of public charging service quality. The TOPSIS method integrated with FUCOM method in probabilistic hesitant fuzzy circumstance is applied to rank the optional alternatives and a numerical example for performance evaluation of public charging service quality is used to test the newly proposed method’s practicability in comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute. The main results of this paper: (1) a novel PHF-TOPSIS method is proposed; (2) the extended TOPSIS method is developed in the probabilistic hesitant fuzzy environment; (3) the FUCOM method is used to obtain the attribute weight; (4) the normalization process of the original data has adapted the latest method to verify the precision; (5) The built models and methods are useful for other selection issues and evaluation issues.

This study introduces a new multi-criteria group decision-making model in organ transplant transportation networks under uncertain situations. A new combined weighting approach is presented to obtain expert weights with various kinds of opinions by integrating similarity measure and subjective judgments of experts. Also, the CRITIC approach is given to obtain transportation criteria weights. Finally, a novel integrated ranking approach is proposed to calculate the rank of each alternative based on ideal point solution and relative preference relation (RPR) methods. This study regards an interval-valued intuitionistic fuzzy set to cope with the vagueness of uncertain conditions in a real case study.

The Gene Ontology (GO) knowledge base provides a standardized vocabulary of GO terms for describing gene functions and attributes. It consists of three directed acyclic graphs which represent the hierarchical structure of relationships between GO terms. GO terms enable the organization of genes based on their functional attributes by annotating genes to specific GO terms. We propose an information-retrieval derived distance between genes by using their annotations. Four gene sets with causal associations were examined by employing our proposed methodology. As a result, the discovered homogeneous subsets of these gene sets are semantically related, in contrast to comparable works. The relevance of the found clusters can be described with the help of ChatGPT by asking for their biological meaning. The R package BIDistances, readily available on CRAN, empowers researchers to effortlessly calculate the distance for any given gene set.

In practical linguistic multi-criteria group decision-making (MCGDM) problems, words may indicate different meanings for various decision makers (DMs), and a high level of group consensus indicates that most of the group members are satisfied with the final solution. This study aims at developing a novel framework that considers the personalized individual semantics (PISs) and group consensus of DMs to tackle linguistic single-valued neutrosophic MCGDM problems. First, a novel discrimination measure for linguistic single-valued neutrosophic numbers (LSVNNs) is proposed, based on which a discrimination-based optimization model is built to assign personalized numerical scales (PNSs). Second, an extended consensus-based optimization model is constructed to identify the weights of DMs considering the group consensus. Then, the overall evaluations of all the alternatives are obtained based on the LSVNN aggregation operator to identify the ranking of alternatives. Finally, the results of the illustrative example, sensitivity and comparative analysis are presented to verify the feasibility and effectiveness of the proposed method.

Multiple Criteria Decision-Making (MCDM) is one of the most reliable and applicable decision-making tools to address real-life complex and multi-dimensional problems in accordance with the concepts of sustainable development and circular economy. Although there have been several literature reviews on several MCDM methods, there is a research gap in conducting a literature review on the Multi-Attributive Border Approximation area Comparison (MABAC) as a useful technique to deal with intelligent decision-making systems. This study attempts to present a comprehensive literature review of 117 articles on recent developments and applications of MABAC. Future outlook is provided considering challenges and current trends.