Search

Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.

Abstract A large number of materials and various criteria fashion material selection problems as complex multi-criteria decision-making (MCDM) problems. This paper proposes a new decision-making method called the simple ranking process (SRP) to solve complex material selection problems. The accuracy of the criteria weights has a direct impact on the outcomes of the new method. In contrast to current MCDM methods, the normalization step has been eliminated from the SRP method as a potential source of producing incorrect results. The application of the method is appropriate for situations with high levels of complexity in material selection because it only considers the ranks of alternatives in each criterion. The first scenario of vital-immaterial mediocre method (VIMM) is used as a tool to derive criteria weights based on expert assessment. The result of SRP is compared with a number of MCDM methods. In order to evaluate the findings of analytical comparison, a novel statistical measure known as compromise decision index (CDI) is proposed in this paper. CDI revealed that the MCDM methods’ outputs for solving the material selection could not be theoretically proven and requires to be evaluated through practice. As a result, the dependency analysis-an additional innovative statistical measure is introduced to demonstrate the reliability of MCDM methods by assessing its dependency on criteria weights. The findings demonstrated that SRP is extremely reliant on criteria weights and its reliability rises with the number of criteria, making it a perfect tool for solving challenging MCDM problems.

The e-commerce industry has seen significant growth over the past decade as it focuses on convenience and accessibility, leading to a surge in online shopping with more and more consumers opting for it. At the same time, the e-commerce industry faces various challenges. In order to fully harness the potential of this industry, it is important to identify its benefits and challenges and focus on pathways to mitigate the challenges and harness its growth. This study utilizes the Delphi approach and involves experts from the e-commerce domain to get their opinions to identify the top ten benefits, challenges, and pathways for the e-commerce industry. Analytic Hierarchy Process (AHP) and Criteria Importance Through Intercriteria Correlation (CRITIC) methods are subsequently, employed to prioritize the identified factors. Results of the study revealed that factors such as affordable advertising & marketing; availability and product variety; and Global reachability are the most important benefits, while technological upgradation; returns or refunds; and counterfeit products posed the greatest challenges for the industry. Government compliance check; better relationship with delivery partners; and strong data privacy and online security policies emerged as the best pathways. This study also provides valuable insights to businesses, policymakers, and researchers in the e-commerce industry on how to navigate the benefits, challenges, and pathways of this rapidly growing sector.

Supply chain management (SCM) has gradually evolved beyond the straightforward logic of benefits and economic viewpoints. Supplier selection and performance evaluation are the crucial strategic components of any SCM system with a substantial economic impact and risk reduction. Several conflicting factors make supplier selection a challenging multi-criteria decision-making problem. This paper introduces a method called alternative ranking with the elected nominee (ARWEN) to select suppliers in Iran’s dairy product chain store. The primary principle of ARWEN is to choose the best alternative based on the lowest change rate rather than the elected nominee. Four extensions of the ARWEN method are proposed depending upon the nature and level of information available to the decision-makers. A fifth extended version termed E-ARWEN is also recommended to consider the negative form of the elected nominee. Two novel statistical tools, the ranking performance index and the Zakeri-Konstantas distance product correlation coefficient, are also put forth to validate the ARWEN extensions’ outcomes. The results and verification of this new method are carried out through two supplier selection case examples. Comprehensive comparisons were carried out to explore the new methods’ behaviors, indicating ARWEN III and E-ARWEN have similar behavior to VIKOR, SAW, and EDAS in generating rankings.

Selecting the best supplier is a recurrent organizational challenge that occurs in a supply chain (SC) as a result of the presence of complex variables, restrictive criteria, and conflicting priorities. Since an SC network is often developed with ambiguous conditions and information due to the industrialization of society and the intricacy of market competitiveness, fuzzy decision-making models are more effective. This paper proposes a two-stage decision-making model to select suppliers and to estimate cost-effective order numbers per supplier. The initial stage of the proposed model involves identifying fuzzy linguistic variables, interpreting appropriate decision criteria for evaluating suppliers, and modelling fuzzy technique for order preference and similarity to ideal solution (TOPSIS) method. The goal of fuzzy TOPSIS method is to attenuate the ambiguous expert inputs. In the second stage, economic order quantity is determined and assigned to each supplier using TOPSIS scores as inputs for a linear programming (LP) model. Different constraints, including demand, density qualification, acidity qualification, price, and capacity are formulated using the LP model. The mathematical model seeks to optimize total value of purchasing. The model is implemented in a dairy company to show its applicability and effectiveness. It has been found that supplier A1 and supplier A4 need to deliver 8000 kg of dry milk to the company, while supplier A5 needs to supply only 3500 kg. It is expected that the obtained results will assist organizations in developing a methodical strategy for addressing order allocation and supplier selection problems in more a realistic context.

Introduction/purpose: The paper presents a model for the selection of a route for the transport of hazardous materials using fuzzy logic systems, as a type of artificial intelligence systems. The system presented in the paper is a system for assistance in the decisionmaking process of the traffic service authorities when choosing one of several possible routes on a particular path when transporting hazardous materials. Methods: The route evaluation is performed on the basis of five criteria. Each input variable is represented by three membership functions, and the output variable is defined by five membership functions. All rules in a fuzzy logic system are determined by applying the method of weight premise aggregation (ATPP), which allows the formation of a database based on experience and intuition. Based on the number of input variables and the number of their membership functions, the basic base of 243 rules is defined. Three experts from the Ministry of Defense were interviewed to determine the weighting coefficients of the membership functions, and the values of the coefficients were determined using the Full Consistency Method (FUCOM). Results: A user program which enables the practical application of this model has been created for the developed fuzzy logic system. Conclusion: The user platform was developed in the Matlab 2008b software package.

There has been a paradigm shift in the automobile industry due to e-mobility which reduces green-house gas emission and air pollution. In this context, selection of the most feasible automotive passenger vehicle is a complex decision-making problem due to the use of different power source, technology, specification and price. In this paper, five alternative vehicles based on fuel cell, hybrid electric, battery electric, plug in hybrid electric and compressed natural gas bi-fuel are evaluated using an integrated criteria importance through inter-criteria correlation (CRITIC) - Combined Compromise Solution (CoCoSo) method. CRITIC method is used to obtain the weights of the vehicle selection criteria, whereas, CoCoSo method is employed to rank the vehicles considering different technical and operational criteria such as greenhouse gas emission, fuel economy, vehicle range, accelerating time, annual fuel cost and vehicle base model cost. It is found that battery electric vehicle out performs all other considered alternatives. The validity of the results is verified by comparing with other well popular MCDM methods. Further, a sensitivity analysis is conducted by changing the criteria weights to establish the stability of the model.

To meet the requirements of the cotton spinning industry and achieve the best quality of ring-spun yarn, it becomes an imperative task to determine the technological values of cotton fibers. The availability of High Volume Instrument (HIV) data now makes it possible to evaluate the quality of cotton fiber with respect to some of its major physical properties. The fiber quality index (FQI), the spinning consistency index (SCI), and the multiplicative analytic hierarchy process (MAHP) are some of the popular approaches adopted by the spinning industry personnel to determine the quality values of cotton fibers. In this paper, while integrating the design of experiments (DoE) and the technique for order preference by similarity to ideal solution (TOPSIS), a regression meta-model is developed for determining the technological value of cotton fiber with respect to the TOPSIS score. This model identifies the statistically significant fiber properties and their interactions affecting the estimated TOPSIS score while fitting a polynomial to the experimental data in multiple linear regression analysis. It is observed that the uniformity index has no importance in quality value evaluation of the cotton fiber, although its interactions with other properties are statistically significant. A validation analysis shows an excellent degree of congruence of this meta-model with the existing models for cotton fiber quality determination.

Selection of the most appropriate non-traditional machining process (NTMP) for a definite machining requirement can be observed as a multi-criteria decision-making (MCDM) problem with conflicting criteria. This paper proposes a novel hybrid method encompassing factor relationship (FARE) and multi-attributive border approximation area comparison (MABAC) methods for selection and evaluation of NTMPs. The application of FARE method is pioneered in NTMP assessment domain to estimate criteria weights. It significantly condenses the problem of pairwise comparisons for estimating criteria weights in MCDM environment. In order to analyze and rank different NTMPs in accordance with their performance and technical properties, MABAC method is applied. Computational procedure of FARE-MABAC hybrid model is demonstrated while solving an NTMP selection problem for drilling cylindrical through holes on non-conductive ceramic materials. The results achieved by FARE-MABAC method exactly corroborate with those obtained by the past researchers which validate the usefulness of this method while solving complex NTMP selection problems.

Supplier selection is one of the most important issues in supply chain management (SCM) which greatly affects its performance and market competitiveness. In the recent years, supplier selection in SCM has become imperative to balance between the ordinal and cardinal criteria. This paper proposes a two-phase model which aims to evaluate and select suppliers using an integrated Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Ordering Preference by Similarity to Ideal Solution (FTOPSIS) methods. A fully developed model consisting of several evaluation criteria, both quantitative and qualitative in nature, as assessed by FAHP method to estimate the criteria weights, while FTOPSIS method is used to rank the potential suppliers that have been singled out through expert assessment. The proposed model is a support tool in the optimization of the purchasing process, and it provides the possibility of realizing additional savings by developing stronger cooperation with the optimal supplier.

The VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje which means multi-criteria optimization and compromise solution, in Serbian) method has already become a quite popular multi-criteria decision making tool for its computational simplicity and solution accuracy. This method focuses on selecting and ranking from a set of feasible alternatives, and determines compromise solution for a problem with conflicting criteria to help the decision maker in reaching a final course of action. It determines the compromise ranking list based on the particular measure of closeness to the ideal solution. Depending upon the type of decision problem and necessity of the decision maker, apart from VIKOR method, different variants of it, like comprehensive VIKOR, fuzzy VIKOR, regret theory-based VIKOR, modified VIKOR and interval VIKOR methods have also been subsequently developed. In this paper, the ranking performance of original VIKOR method and its five variants is analyzed based on two demonstrative examples. It is observed that interval VIKOR method performs unsatisfactorily and when the information in a decision problem is imprecise, fuzzy VIKOR method should always be preferred. But, for any decision problem, original VIKOR is the best method for solution without unnecessarily complicating the related mathematical computations.

Multi-attribute decision-making (MADM) methods represent reliable ways to solve real-world problems for various applications by providing rational and logical solutions. In reaching such a goal, it is expected that MADM methods would eliminate inconsistencies like rank reversal issues in a given solution. In this paper, an endeavor is taken to put forward a new MADM method, called RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval), which successfully eliminates the rank reversal problem. The developed RAFSI method has three major advantages that recommend it for further use: (i) its simple algorithm helps in solving complex real-world problems, (ii) RAFSI method has a new approach for data normalization, which transfers data from the starting decision-making matrix into any interval, suitable for making rational decisions, (iii) mathematical formulation of RAFSI method eliminates the rank reversal problem, which is one of the most significant shortcomings of existing MADM methods. A real-time case study that shows the advantages of RAFSI method is presented. Additional comprehensive analysis, including a comparison with other three traditional MADM methods that use different ways for data normalization and testing the resistance of RAFSI method and other MADM methods to rank the reversal problem, is also carried out.