Multi-criteria Decision-making Problem Research Articles

Автоматизація побудови, порівняння та вибору маршрутів та схем доставки в системі мультимодальних перевезень


 The article proposes the application of a methodical approach to choosing the optimal route of multimodal transportation based on a module integrated into the information system of a freight forwarding company. The functions of the module are to build an optimal delivery scheme and meet all customer requirements, both domestic and international multimodal transportation, taking into account the changing circumstances in the conditions of instability caused by the state of war and the need to reorient the usual delivery routes.
 In the conditions of military operations, this method of automated search and comparison of alternative routes allows taking into account changing conditions that develop in real time, while the cargo flow should be accompanied by an information flow that would also be available in real time. The proposed structure of the database, the structures of requests for development routes., developed forms of source documents. The criteria for comparing routes of different dimensions are defined: the difference in the distance of cargo transportation, the costs of changing the existing system of cargo transportation, increasing the level of transportation safety, delivery time, reducing the cost of delivering a batch of goods, the difference in delivery time. A solution to the multi-criteria decision-making problem by normalizing the criteria and the additive optimization method is proposed. The method of determining the best route and supply scheme is implemented in the "decision-making" module, which is integrated into the information system of the transport forwarding company.
 The result of the practical implementation of the proposed method of construction, comparison and selection of routes and delivery schemes in the system of multimodal transportation based on the integrated module in the information system of the transport and forwarding company is the possibility of quick response to changes in external circumstances, warnings of emergency and conflict situations, control of the transportation process along the entire route and , if necessary, making adjustments to the route and delivery scheme, choosing a safe and effective option by the operator of multimodal transportation.

Cervical cancer risk assessment using machine learning integrated fuzzy MCDM methodology

Cervical cancer is entirely preventable if diagnosed at an early stage; however, the current rate of cervical cancer screening participation is not very adequate, and early detection approaches are still open and demanding. Evaluating the risk levels of potential patients in a practical and economic way is crucial to direct risky candidates to screening and establishing potential treatments to conquer the disease. In this study, a machine learning-integrated fuzzy multi-criteria decision-making (MCDM) methodology is proposed to assess the cervical cancer risk levels of patients. In this context, based on behavioral criteria obtained from the publicly accessible cervical cancer behavior risk data set from the UCI repository, the risk levels of patients are evaluated. The proposed methodology is established in three stages: In the first stage, using a machine learning technique, i.e., feature selection, the most effective criteria for predicting cervical cancer risk are selected. In the second stage, the criteria for importance through intercriteria correlation (CRITIC) method is used to assign objective importance levels to the criteria. In the third stage, the cervical cancer risk levels of candidate patients are prioritized using the technique for order preference by similarity to the ideal solution (TOPSIS) and, alternatively, the evaluation based on distance from the average solution (EDAS) techniques. The proposed methodology is developed in an interval-valued Pythagorean fuzzy atmosphere for quantifying the uncertainty in the nature of the problem. This study demonstrates that the feature selection algorithm can be efficiently utilized to determine the fundamental criteria of an MCDM problem and to aid in the early identification of cervical cancer.

An Effective Selection of Memory Technologies for TCAM to Improve the Search Operations: Demonstration of Memory Efficiency in SDN Recovery

Ternary Content-Addressable Memory (TCAM) is used for storing the flow tables in software-defined networking (SDN)-based OpenFlow switches. However, the TCAM can store only a certain number of flow tables (8000). Moreover, when the switch flow tables need to be updated due to the link failure in the SDN, further updates may be lost due to the flow tables limit of the TCAM space. Hence, to resolve this issue, other memories need to be used in conjunction with TCAM to enhance the memory operations of TCAM. When considering which flash memory technology is to be used in conjunction with TCAM, we need to balance several factors to ensure optimal performance, speed, endurance, reliability, integration complexity, and cost-effectiveness. Hence, it leads to a multi-criteria decision-making problem regarding the selection of other memory technologies such as 3D XPoint, Magnetoresistive RAM, Resistive RAM, and Ferroelectric RAM. In this paper, we use the analytical network process (ANP) method to select the suitable technology in conjunction with TCAM, considering the features of the memory technologies for Software-Defined Internet-of-Things (SD-IoT). We provide a comprehensive numerical model leveraging the ANP to rank the memory technologies regarding their weights. The highest weights identify the most suitable technology for TCAM. We perform simulations to show the effectiveness of the mathematical model utilizing the ANP. The results show that the suggested methodology reduces the recovery delay, improves the packets received ratio (PRR), decreases the jitter, and increases the throughput.

Comparative Ranking Preferences Decision Analysis through a Novel Fuzzy TOPSIS Technique for Vehicle Selection

Acquiring a vehicle or financing its purchase is often considered a luxury, particularly for middle-class households. In addition to their primary concern, consumers prioritize distinguishing elements such as vehicle type, size, capacity, engine power, fuel efficiency, safety features, and life-cycle costs. Furthermore, the choice of vehicle has multiple factors and is contingent upon the financial prosperity of the household, allowing for precise expression. The decision-making process is complex and entails selecting the most appropriate alternatives. This study proposes a novel fuzzy technique for order of preference by similarity to the ideal solution (FTOPSIS) method to solve vehicle selection multi-criteria decision-making (MCDM) problems. Decision-makers express their opinions on each alternative and criterion in linguistic terms, in terms of generalized interval type-2 trapezoidal fuzzy numbers (GIT2TrFNs). The FTOPSIS process involved utilizing the defuzzification of GIT2TrFNs to acquire the normalization matrix. Finally, the proposed approach prioritizes the options and chooses the best vehicle when faced with conflicting criteria, as illustrated through a numerical illustration.

Threshold-Based Value-Driven Method to Support Consensus Reaching in Multicriteria Group Sorting Problems: A Minimum Adjustment Perspective

In multicriteria group decision-making (MCGDM) problems, there exist the situations that alternatives need to be assigned to several predefined ordered categories rather than be ranked from the most preferred to the least preferred, which is called multicriteria group sorting problems. To address multicriteria group sorting problems, it is necessary to implement a consensus reaching process to fully consider the opinion of each decision maker and reduce the conflict among them. To do so, this article proposes a consensus reaching model for multicriteria group sorting problems based on the threshold-based value-driven sorting method from the perspective of minimum adjustment. Specifically, we first define the consensus measure to calculate the agreement degree among decision makers by considering the ordinal information and cardinal information at the same time. On this basis, we construct a minimum adjustment optimization model in terms of the threshold-based value-driven sorting method to assist decision makers in modifying their decision matrices and further promote consensus. Followed by this, an optimization model that aims to minimize the distance between all decision makers and the group with respect to alternatives’ comprehensive values and sorting results is developed to determine the group sorting result for alternatives. Moreover, a numerical application of urban park management, some sensitivity analysis, and simulation experiments are provided to justify the proposed method. Experimental results reveal that the proposed method is effective in promoting consensus.

A Novel Fuzzy Modified RAFSI Method and its Applications in Multi-Criteria Decision-Making Problems

A Novel Fuzzy Modified RAFSI Method and its Applications in Multi-Criteria Decision-Making Problems

A new exponential knowledge and similarity measure with application in multi-criteria decision-making

The knowledge measure of fuzzy sets (FSs) has drawn greater attention and is still unresolved since it is the complementary idea to fuzzy entropy. The amount of knowledge is important in evaluating fuzzy information. This study proposes an exponential entropy-based knowledge measure for fuzzy sets to quantify the knowledge amount conveyed by fuzzy sets. Intuitive analysis of the characteristics of the knowledge amount in fuzzy sets is introduced, and its value and validity are demonstrated with numerical examples. In addition, a new measures is proposed from the exponential knowledge measure (i.e., exponential similarity measure). These measure are examined for validity, and their characteristics and put forward. Last, a modified Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) strategy based on the suggested similarity measure is suggested to handle a multi-criteria decision-making problem in a fuzzy environment. The effectiveness of the proposed approach is shown By utilizing a numerical example. A comparative analysis is also provided to evaluate the viability of the proposed approach.

ITERATIVE ADAPTATION ALGORITHMS IN MULTICRITERIA TASKS

This article examines using of iterative adaptation algorithms to solve the problem of determining measurement location of the carotid artery intima-media complex. The formulation of a multi-criteria decision-making problem, as the basis for determining correct criterion for proper selection and successful recognizing of the required object in an ultrasound image. The work discusses principles of constructing cascade classifiers as well as, the use of the cascade Haar classifier and the cascade LBP classifier, for which Haar primitives and local binary templates are used as a basis. The results of experimental studies in order to determine effectivity of different boosting algorithms to solve this problem are presented. The best results were shown by the Haar cascade classifier, developed using an iterative adaptation algorithm, which manages solving a multicriteria problem on a given training set more successfully and determines the most suitable areas for measuring the thickness of the common carotid artery intimate media complex.

A novel Multi-Criteria Decision Making framework based on Evidential Reasoning dealing with missing information from online reviews

Online reviews are a valuable tool for Multi-Criteria Decision Making (MCDM) because they provide insightful information on several aspects. However, not all reviews yield complete information on all the necessary criteria for the MCDM problem. In practice, users might not effectively analyze all of them due to a lack of knowledge or time constraints. Despite this shortcoming, these online reviews still offer useful information that would be lost if neglected. Therefore, this paper proposes a framework that offers a new approach to using online reviews in MCDM and effectively addresses missing information on a given criterion or criteria. This new approach first establishes an information processing mechanism to transform online reviews into Probabilistic Linguistic Term Sets (PLTSs) with interval uncertainty. Afterward, a weight determination model is defined to calculate the criteria weights, which are given as interval values to reflect the uncertainty of the criteria and consider information from online reviews to better capture the opinions of reviewers. Next, the Evidential Reasoning (ER) algorithm is extended to the PLTSs with interval uncertainty environment to fuse the evaluation information and generate the interval-valued expected utilities of the alternatives by considering the uncertainty related to the missing information. Finally, a case study is conducted to illustrate the implementation process of the proposed framework, and a comparative analysis is carried out to evaluate its performance.

Revolutionizing Multi-Criteria Decision Making with the Triangular Fuzzy Geometry Bonferroni Mean Operator (TFGBM)

This study investigates the Multi-Criteria Decision Making (MCDM) topic to address the complexities of decision processes involving ambiguous information. We introduce the Triangular Fuzzy Geometric Bonferroni Mean (TFGBM) operator, a novel aggregation technique inspired by the Geometric Bonferroni Mean (GBM) concept. This operator is intended to aggregate triangular fuzzy numbers within MCDM problems effectively. We thoroughly investigate the properties of TFGBM and its distinct forms to ensure its practical utility. We introduce the Triangular Fuzzy Geometric Weighted Bonferroni Mean (TFGWBM) operator to accommodate situations where input factors have variable degrees of significance. Based on this foundation, we present a comprehensive framework for decision-making involving multiple attributes in ambiguous triangular fuzzy environments. A relevant case study regarding selecting an optimal location for a Halal center demonstrates the efficacy and applicability of our methodology. We emphasize the tangibility and efficiency of the suggested methodology in improving decision-making processes by emphasizing this real-world application.

Selection the optimal technology for electricity generation of 5 MW using a fuzzy multi-criteria decision-making approach

Today's challenges towards a sustainable energy development in most European nations are crucial and highly complicated. We find the need to choose between several technologies generation, whose task is very laborious. Multiple factors that affect the success of a Renewable Energy (RE) project must be analyzed and taken into account. The evaluation and comparison of possible technologies generation is a multi criteria decision problem because not only economic aspects must be considered but also technical, environmental and social aspects. This paper presents the case for selecting the optimal technology for electricity generation of 5 MW for a region located in the southeast of Spain. As most of the real-world multicriteria decision-making (MCDM) problems contain a mixture of quantitative and qualitative criteria; therefore discrete MCDM methods are inadequate for handling this type of decision problems.

A novel hybrid grey-BCM approach in multi-criteria decision making: An application in OTT platform

Real world decision making is like a puzzle having complex, uncertain and vague information and this fact portray the wide range applicability of grey system theory in decision making procedure as grey system theory deals with the systems having information with uncertainty. In order to extend the base-criterion method to uncertain conditions, grey information may be a better way to solve a lot of multi-criteria decision-making problems. In this paper, we proposed a novel approach ‘grey base-criterion method’ (GBCM) based on the linguistic variables extended to the grey information. Weights of criteria have been calculated using GBCM. Numerical examples are illustrated and then the results are compared by the grey best-worst method (GBWM). Results of comparison show the high reliability of GBCM method with less consistency ratio over GBWM. A real case study of the fastest growing OTT (Over the Top) platforms in India has been taken to bestow the robustness of the proposed method.

A computational sustainable approach for energy storage systems performance evaluation based on spherical-fuzzy MCDM with considering uncertainty

Incorporating energy storage systems (ESSs) can mitigate the intermittency of renewable energy sources. There are a variety of ESSs for renewable energy with vastly different characteristics. The problem of diversity of characteristics in selecting the most appropriate ESS can be approached as a multi-criteria decision-making (MCDM) problem. This research evaluates sustainable ESSs through a case study in Egypt. A sustainable computational approach is presented through which experts can use verbal expressions to express their opinions in determining the priorities of the dimensions that affect the selection of ESSs. Determining the appropriate energy storage system requires consideration of several main dimensions such as the technology dimension, environmental dimension, economic dimension, and social-political dimension and, in addition to the sub-indicators. Hence, this research applies a hybrid MCDM approach that deals with different indicators and characteristics. Also, uncertainty in applying the proposed approach was dealt with by a spherical fuzzy (SF) environment and by using the spherical fuzzy numbers (SFNs). At first, the SF analytical hierarchy process (SF-AHP) method was used to assess the priorities of the four main dimensions and their sub-indicators. Then, the SF mixed aggregation by comprehensive normalization technique (SF-MACONT) was applied to evaluate and rank the ESSs selected for analysis through research. An illustrative case study was presented that included seven ESSs out of the eighteen systems listed in the research to confirm the feasibility of the developed approach. Sensitivity analysis was carried out by changing some parameters like λ, μ, δ, and ϑ based on the SF-MACONT method and changing the weights of some main dimensions. A comparative analysis with some MCDM approaches was conducted to show the advantages of the developed approach through its flexibility and built-in parameters. The findings show that the technology dimension is the most influential in choosing a sustainable ESS, while the economic dimension is the least influential. Also, the results of the evaluation and ranking of the seven selected ESSs indicate that the "Pumped Hydro" system is the most suitable system for energy storage in Egypt.

Pioneering Heat Exchanger Network Synthesis: A TOPSIS Driven Paradigm for Optimal Solutions

Multi-Criteria Decision Making (MCDM) is a significant challenge across various domains, requiring adept resolution of conflicts arising from diverse objectives and criteria. This study proposes an innovative approach aimed at optimizing controllability, minimizing irreversibility, and maximizing overall effectiveness in control system design to address this challenge. The primary objectives of this study are to introduce a novel methodology for selecting Heat Exchanger Networks (HEN) using the well-established Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Additionally, a closeness coefficient is introduced to rank alternatives networks based on their proximity to the ideal solution. Two illustrative case studies are presented to showcase the methodology's effectiveness, adaptability, and robustness in discrete multi-criteria decision-making problems, particularly in the context of HEN selection. Consistently identifying HEN configurations that fulfill controllability objectives, the methodology demonstrates its effectiveness and potential for broader applications beyond HEN optimization. The case study results affirm the adaptability and robustness of the proposed approach. In summary, this paper introduces an original and versatile approach to address the complexities of multi-criteria decision-making, specifically in the context of HEN selection. Rooted in the TOPSIS method and fortified by the closeness coefficient, the methodology holds promise for intricate decision-making processes and offers transformative possibilities for control system design. The study concludes by inviting further exploration of the proposed methodology, emphasizing its significant contribution to the field and its potential for widespread impact. Researchers and practitioners are encouraged to investigate and apply this innovative approach in diverse decision-making scenarios. The ranking results reveal that alternatives M and K is the optimum one among all the alternatives for both cases with a closeness coefficient equal to 0.651 and 0.971.

Estimation of missing data in intuitionistic fuzzy soft matrix (IFSM) with application in MCDM

The theory of fuzzy soft as well as intuitionistic soft set generally depends on complete information. But there are situations in which the complete information is not available due to various different reasons like data erroneous, in sufficiency of data, lacking of evidence and illegibility of data. Missing data restrict fuzzy soft matrix and intuitionistic fuzzy soft matrix and that causes more ambiguous uncertainty in the process of decision making. Thus, estimation of incomplete information data plays an important role in multi criteria decision making process which has been developed recently. Here, a novel method of missing data estimation in intuitionistic fuzzy soft matrix is described. Firstly, we estimate the missing values using the information given in data. After estimation the missing values in IFSM thus obtained is applied in a multi-criteria decision-making problem with illustration by an example.

Lean management approach in health sector: MCDM model proposal to prevention of waste

Lean manufacturing in the manufacturing sector corresponds to lean management in the service sector. When health services are considered, it is seen that the concepts that are important in the manufacturing sector have the same importance for health services. Because waste is the main problem of every sector. Lean management approach in the health sector enables improvements by developing solutions to many other problems of healthcare professionals, patients and hospitals, such as reducing transaction costs, ensuring patient safety, and reducing processing times. This study was carried out with the aim of establishing a lean health system by selecting the most appropriate lean management methodology in order to identify and prevent waste in health processes and to increase efficiency and service quality. In the study, the evaluation of lean methodologies that can be applied to prevent waste in health services is considered as a multi-criteria decision-making problem and a model has been proposed. In the proposed model, wastes were determined as research criteria and lean methodologies were determined as research alternatives. The solution phase of the problem was carried out in two stages with multi-criteria decision-making techniques. In the solution phase, the importance weights of 24 decision criteria were calculated with the SWARA method, while 5 alternatives were evaluated based on the decision criteria with the WASPAS method and the most suitable alternative was selected.

Computational Proposal for the SAPEVO-M Method: Technological Framework for Complex Decision-Making

This paper introduces a computational framework based on the SAPEVO-M method to enhance decision-making processes in complex scenarios. By integrating technological advancements with the SAPEVO-M methodology, the framework facilitates a more efficient, accurate analysis based on multi-criteria decision-making problems. The paper presents a literature background of the methodology and its axiomatic structure, highlighting its capability for decision-making for a group of stakeholders, thus offering favorable improvements as a technological aid for decision-making analysis. The results demonstrate the potential of the framework, providing decision-makers with robust and reliable insights, and making it an invaluable tool in fields requiring complex decision analysis. This study not only contributes a novel and free computational approach to the decision-making literature but also paves the way for future research and development, applying the axiomatic methodology for problem-solving in diverse areas of science, searching for increase and transparency concerning decision-making processes worldwide.

Multiple-criteria decision analysis techniques for anaerobic digester technology assessment

Effective decision-making requires the evaluation of several criteria rather than a single, preferred criterion. The best decision options (alternatives) are recommended to decision-makers when a multi-criteria decision problem is addressed. This study develops a multi-criteria selection method for the assessment of small-scale anaerobic digester technology by combining two existing methods. The Simple Multi-Attribute Rating Technique (SMART) and the Analytical Hierarchy Process (AHP) approaches of multiple-criteria decision analysis were used as a decision support tool, and the preferred anaerobic digester technology was selected from a list of eleven potential small-scale digester technologies used in low to middle-income countries. These techniques were applied under two scenarios for a case study in the South African smallholder farmers. Scenario 1 involves a subsistence smallholder farming context, while scenario 2 involves a commercially oriented smallholder farming context. The overall results revealed that the DIY Biobag and Puxin digester design models achieved 82.1 and 73.7 % preference in comparison to other digester technologies for scenarios 1 and 2, respectively. The Biobag digester technology achieved the highest ranking, which is consistent with the significant cost advantage and technical characteristics of the technology. However, for those households with sufficient access to funds for the initial expenditure, the method identifies the Puxin digester as the most appropriate alternative, excluding cases where underground construction is not possible. The AGAMA BiogasPro digester was ranked in the second position in both scenarios. A sensitivity analysis was conducted to determine the effect of changes in the assessment criteria weights and found the selected alternatives stable and robust. Finally, it can be concluded that the developed technology selection model contributed a knowledge-based framework to be used in various situations by different decision-makers, thereby providing a method applicable to particular local conditions to identify the most suitable technology choices.

A Novel Fuzzy Best-Worst Multicriteria Decision-Making Method Based on the Dual Interval Algorithm for Environmental Decision Support Systems

Considering the double uncertainty caused by the ambiguity of statistical data and the ambiguity produced by the subjective assessment of decision makers, the crisp values of criteria may be insufficient to model the multi-criteria decision-making (MCDM) problem in the real world. This paper proposes a fuzzy best-worst method (FBWM) based on a dual-interval solution algorithm to extend the best-worst method (the most recent MCDM method) to fuzzy environments. The reference comparisons for the best criteria and for the worst criteria are represented by fuzzy numbers. Then, according to the BWM method, a nonlinear constrained optimization problem with fuzzy parameters is formulated. We decompose the membership function in fuzzy numbers into several interval numbers of special form and solve the aforementioned fuzzy BWM problem by compound interval algorithm to obtain fuzzy weights of different criteria. Meanwhile, an integral type-reduced method is proposed for determining the fuzzy consistency ratio in order to assess the reliability of the FBWM results. The viability of the new algorithm to expand the BWM method into fuzzy environments has been validated through three numerical examples. In contrast to the existing FBWM method, the proposed method avoids the arithmetic operation between fuzzy numbers during the solution process, directly transfers the uncertain information in the membership function corresponding to the fuzzy comparison vector to the result, and generates fuzzy weight value, which indicates that the proposed algorithm is able to obtain accurate BWM results in fuzzy environments. The results of the study provide new solution ideas for multi-criteria optimization problems under uncertainty.

A Multi-Criteria Group Decision-Making Approach For Robot Selection Using Interval-Valued Intuitionistic Fuzzy Information And Aczel-Alsina Bonferroni Means

The process of identifying the most appropriate robot for a particular industrial task has grown challenging and more difficult in the fast-paced environment. It is merely driven by the complex evolution and continuous integration of modern characteristics and advanced features by various suppliers. Industrial robots are now widely available in the marketplace, each possessing a distinctive collection of skills, attributes, and requirements. However, the selection of optimal robots is heavily influenced by factors such as the manufacturing environment, product design, production system, and overall cost considerations. These factors directly impact the decision-making process. The ultimate goal for the decision maker is to pinpoint and choose the most suitable robot, capable of delivering the desired output while minimizing costs and catering to the specific requirements of the industry. So, to consider it, in this paper, the hybrid structure of the Aczel-Alsina (AA) and Bonferroni mean (BM) operators for interval-valued intuitionistic fuzzy (IVIF) environment has been proposed, which can show the interrelationship between the multiple criteria and assist the expertise in decision-making (DM) process. Moreover, the algorithm and methodology for the multi-criteria group decision-making (MCGDM) problem have been defined which is further utilized by solving a real-world problem to demonstrate the effectiveness and validity of the proposed method. At last, the comparison analysis between prior and proposed studies has been displayed, and then followed by the conclusion of the result.