Efficient decision-making in fruit production involves evaluating multiple criteria, such as yield, fruit quality, and resistance, to rank available alternatives. Multi-Criteria Decision-Making (MCDM) methods provide a structured and objective framework for such tasks. This paper presents a web-based application named FRUITrank, designed to implement the MARCOS MCDM method for ranking and selection of plum varieties. The application uses a predefined set of criteria, whose weights were determined externally by using other MCDM methods. By leveraging a simple and intuitive interface, the application aims to overcome barriers to the practical adoption of MCDM methods among researchers and fruit producers, such as mathematical complexity and lack of accessible tools. The application was tested using a set of 11 criteria relevant to plum production, demonstrating its capability to deliver reliable and transparent rankings. This paper builds upon prior research in MCDM applications for agriculture, offering a practical solution for producers and researchers to enhance decision-making processes. Future improvements to the developed tool may include automated criteria weight calculation and broader applicability across various agricultural contexts.

Considering the characteristics of urban planning that are becoming increasingly demanding, and the trend that urban zones should meet users’ needs based on the principle of everything in one place, this paper evaluates the potentials of urban zones in Novi Sad. An expert analysis defined 25 criteria related to urban, traffic, architectural, environmental and sociological aspects to assess the current potentials of urban zones in a sustainable manner. Based on these criteria, 10 urban zones were evaluated using a multi-structure fuzzy MCDM model, including: the Fuzzy FUCOM, IMF SWARA and Fuzzy OPARA methods, and the Fuzzy Heronian Mean and Fuzzy Bonferroni operators. Fuzzy FUCOM was applied to determine the importance of the main groups of criteria, while IMF SWARA was used to determine the importance of sub-criteria, with the final weights obtained using the Fuzzy Heronian Mean operator. The Fuzzy OPARA method was implemented to determine the rankings of urban zones based on the potentials they offer. This model represents an innovation, as it is being presented for the first time in the literature. The final values of the urban zones show that Liman and the Center are the two urban zones with the greatest potential, which was confirmed through extensive verification analysis. Such modeling can provide support in the sense that the management of the city can obtain information about the shortcomings and potentials of the location, which allows for the definition of a more specific planning and development policy, based on the previously verified state.

Over 1.1 million deaths occur annually from workplace injuries and diseases, with higher risks in developing countries. Occupational safety studies commonly use quantitative or qualitative methods, but these often fail to address uncertainty. This research targets the Libyan Steel Company (LISCO), aiming to analyze safety risks and develop a structured approach to identify optimal risk mitigation strategies. To this end, the Fuzzy Weights by ENvelope and SLOpe (F-WENSLO) method was chosen to determine the weights of three main safety risks and a total of 18 sub-risks belonging to them, and the fuzzy Bonferroni mean aggregation operator is applied to synthesize expert opinions. The Fuzzy Alternative Ranking Technique based on Adaptive Standardized Intervals (F-ARTASI) method was used to identify and rank the most appropriate safety interventions. While the primary risks identified under the main criteria and sub-criteria are occupational diseases and noise-induced diseases, with weights of 0.4737 and 0.1313, respectively, the intervention strategy deemed most effective for enhancing occupational safety is behavioral safety programs, which hold a weight of 11.0341. The sensitivity test of the analysis results reveals that although the criteria weights and the parameters used in the analysis vary under various scenarios, the ranking of the alternatives remains consistent. Since the general ranking of the alternatives is the same in other methods, decision makers will reach similar results no matter which method they use. This shows that a flexible and reliable decision-making approach is adopted in the process of optimizing occupational safety risks. This research emphasizes the critical importance of prioritizing occupational diseases and natural hazards in the formulation of occupational safety strategies and thus aims to contribute to the protection of workers in industrial plants such as LISCO.

A novel method for solving the multiple-attribute decision-making problem is proposed using the complex Diophantine interval-valued Pythagorean normal set (CDIVPNS). This study aims to discuss aggregating operations and how they are interpreted. We discuss the concept of CDIVPN weighted averaging (CDIVPNWA), CDIVPN weighted geometric (CDIVPNWG), generalized CDIVPN weighted averaging (CGDIVPNWA) and generalized CGDIVPN weighted geometric (CGDIVPNWG). This study aimed to examine several aggregation operators using complex Diophantine interval-valued Pythagorean normal sets. We calculated the weighted average and geometric distance based on an aggregating model. We demonstrate that complex Diophantine interval-valued Pythagorean normal sets satisfy algebraic structures such as associative, distributive, idempotent, bounded, commutative and monotonic properties. In this study, we discuss the mathematical properties of the score and accuracy values. We provide an example of how enhanced score and accuracy values are used in the real world. Machine tool technology and computer science play essential roles in robots. To evaluate robotic systems, four factors must be considered such as tasks, precision, speed and completion of the work. Consequently, it is evident that the models are significantly influenced by the natural number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\nabla$$\end{document}. To further demonstrate the effectiveness of the suggested approach, flowchart based multi-criteria decision-making is provided and applied to a numerical example. Additionally, a comparative study has been carried out to demonstrate the better results that the proposed approach provides when compared to current approaches.

We introduce the concept of Diophantine spherical vague set approach to multiple-attribute decision-making. The Spherical vague set is a novel expansion of the vague set and interval valued spherical fuzzy set. Three new concepts have been introduce such as Diophantine spherical vague weighted averaging operator, Diophantine spherical vague weighted geometric operator, generalized Diophantine spherical vague weighted averaging operator and generalized Diophantine spherical vague weighted geometric operator. We provide a numerical example to show how Euclidean distance and Hamming distance interact. Applications of the Diophantine spherical vague number include idempotency, boundedness, commutativity and monotonicity in algebraic operations. They can determine the optimal option and are more well-known and reasonable. Our goal was to identify the optimal choice by comparing expert opinions with the criteria. As a result, the model’s output was more accurate as well as in the range of the natural number . The weighted averaging distance and weighted geometric distance operators are distance measure that is based on aggregating model. By comparing the models under discussion with those suggested in the literature, we hoped to show their worth and reliability. It is possible to find a better solution more quickly, simply, and practically. Our objective was to compare the expert evaluations with the criteria and determine which option was the most suitable. Because they yield more precise solutions, these models are more accurate and more related to models with . To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. This investigation yielded some intriguing results.

This research focused on the use of multi-criteria decision-making (MCDM) methods in fruit production, which requires the evaluation of numerous economic, biological, and technological factors. The complexity of decision-making in fruit production stems from the need to assess both quantitative and qualitative criteria. To address this, we applied the IMF SWARA method and the Fuzzy Bonferroni operator to determine the weight coefficients of key criteria. The research was conducted in multiple stages, utilizing a combination of different methods. In the first stage, the research team identified 11 criteria to be evaluated by various experts. In the second stage, a survey was developed, and 17 experts in the field of fruit production assessed the weight of the parameters. Finally, the determination of the criteria weights, using the IMF SWARA method and the Fuzzy Bonferroni operator, was based on the previous evaluation provided by four experts who identified themselves as belonging to the group of "University Professors". Out of a total of eleven criteria, two (C4 and C7) were identified as the most important.

: With the increasing complexity of hotel selection, traditional decision-making models often struggle to account for uncertainty and interrelated criteria. Multi-criteria decision-making (MCDM) techniques, particularly those based on fuzzy logic, provide a robust framework for handling such challenges. This paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets (C-IFS) by combining three distinct methodologies: Weighted Aggregated Sum Product Assessment (WASPAS), an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN), and the CRITIC method (Criteria Importance Through Inter-criteria Correlation). To address the dynamic nature of traveler preferences in hotel selection, the study employs a comprehensive set of criteria encompassing aspects such as location proximity, amenities, pricing, customer reviews, environmental impact, safety, booking flexibility, and cultural experiences. The CRITIC method is used to determine the importance of each criterion by assessing intercriteria correlations. AROMAN is employed for the systematic evaluation of alternatives, considering their additive relationships and providing a weighted assessment. WASPAS further analyzes the results obtained from AROMAN, incorporating both positive and negative aspects for a comprehensive evaluation. The integration of C-IFS enhances the model’s ability to manage uncertainty and imprecision in the decision-making process. Through a case study, we demonstrate the effectiveness of this integrated approach, offering decision-makers valuable insights for selecting the most suitable hotel option in alignment with the diverse preferences of contemporary travelers. This research contributes to the evolving field of decision science by showcasing the practical applicability of these methodologies within a C-IFS framework for complex decision scenarios.

The aim of this work was to rank and select the most favorable of six sweet cherry cultivars using the web-oriented FRUITrank application, which incorporates the MARCOS (the Measurement of Alternatives and Ranking according to Compromise Solution) multi-criteria decision-making algorithm. During the ranking, 10 criteria were taken into account, one of which relates to the harvest start time, three to the physical characteristics of the fruit (including the pedicel), four to the chemical properties of the fruit, and one to the organoleptic characteristics. Alternative A3 (Kordia cultivar) was ranked as the best, the Izabela cultivar (A4) as second place, while Summit (A5) was placed at the bottom of the ranking. Considering the fact that the complexity of algorithms of various MCDM (Multi-Criteria Decision Making) methods often limits their application, based on the results obtained, it can be concluded that the mentioned and similar digital solutions solve the mentioned problem, i.e., enable wider application of MCDM techniques both in fruit production and in agriculture in general.

This study explores the trend of in-store fulfillment in omnichannel retail, where nearby store inventories are utilized for online order fulfillment. This model presents advantages such as faster delivery, cost reduction, improved stock management, increased sales, and enhanced customer satisfaction. Operational challenges include determining the optimal fulfillment location for online orders. Investigating a retailer with online and brick-and-mortar stores, this paper addresses dynamic order fulfillment decisions considering customer demand and shipping cost uncertainties. The study employs Stochastic Dynamic Programming for shorter periods and the Genetic Algorithm for longer periods, revealing that the Genetic Algorithm provides solutions closely approximating optimal.

ABSTRACT

Sensitivity analyses are frequently performed to determine the robustness of MCDM methods, of which there are more than 200 types. In the past, rankings were compared to each other rather than to an external ranking. Thus, the direction and meaning of sensitivity can become unclear and complex. In addition, sensitivity analysis is usually performed only based on weight coefficients, but the effect of the normalization type is neglected. In this study, the most appropriate data conversion technique was investigated through an innovative sensitivity procedure to select the e-Small Van, which is an environmentally friendly logistics and transportation vehicle. Seven different normalization types based on the PROBID method (and two additional alternative MCDM methods) were used as parameters, resulting in 105 different MCDM rankings. According to the findings, MCDM rankings, which have low sensitivity, were also the performing methods that produced the highest correlation with price. What is striking is that careless choice of normalization type can be so effective as to manipulate the results. Although the most appropriate technique may vary depending on the data type, the fixed gold standard we recommend offers a flexible solution for all applications. A suitable data converter will result in the choice of a reliable electric vehicle.