Multi-attribute Decision-making Methodology Research Articles

An enhanced MADM with Lq* q-Rung orthopair multi-fuzzy soft set in healthcare supplier selection

The global healthcare systems have encountered unparalleled difficulties due to the COVID-19 pandemic, underscoring the crucial significance of effective management within healthcare supply chains. This research contributes to the field of healthcare supply chain management by presenting a robust MADM methodology called lattice ordered(Lq*) q-rung orthopair multi-fuzzy soft set(Lq* q-ROMFS-MADM) for supplier evaluation and ranking amidst the challenges posed by the COVID-19 pandemic. Taking inspiration from multi-fuzzy soft set and q-rung orthopair fuzzy set, the present research article proposes a novel framework known as Lq* q-rung orthopair multi-fuzzy soft set (Lq* qROMFSS), which incorporates lattice ordering in q-rung orthopair multi-fuzzy soft set. The effectiveness of the proposed model is confirmed through successful experimentation on various important operations, including union, intersection, complement, restricted union and intersection. Moreover, the verification of De Morgan’s laws for Lq* qROMFSS is carried out specifically for these operations mentioned above. To highlight the significance of the proposed Lq* qROMFSS, a multi-attribute decision-making (MADM) problem is presented, showcasing its application in the domain of healthcare supply chain management. Furthermore, a comparative analysis is conducted to elucidate the advantages of this model in comparison to existing models.

A novel cross-entropy framework for pattern design quality evaluation based on interval-valued intuitionistic fuzzy multi-attribute decision making

Paper packaging is favored by manufacturers in various industries due to its advantages such as economy and environmental friendliness, and the application of paper packaging in various types of products is also becoming increasingly popular. However, with the growth of the national economy and the increasing purchasing power of the people, consumers not only have higher requirements for the functionality of products, but also have higher requirements for the visual effect of product packaging. Based on the potential consumption trend of “looking for things”, enterprises need to further strengthen the artistic design effect when using paper packaging, promoting the unity of practicality and aesthetics of paper packaging. Pattern design art, as a component of graphic design art, is mainly based on pattern modeling to convey artistic concepts and aesthetic temperament. Integrating pattern design art into the process of paper packaging design can effectively enhance the artistic value of paper packaging, thereby adding color to products. The pattern design quality evaluation is a classical multi-attribute decision making (MADM). In this paper, the MADM methodology is investigated based on cross-entropy (CE) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for pattern design quality evaluation. First of all, this paper extends the CE method under the IVIFSs to propose the interval-valued intuitionistic fuzzy number cross-entropy (IVIFN-CE) method, it enlarges the application range of the CE method. Secondly, a new MADM model for pattern design quality evaluation based on IVIFN-CE algorithm is proposed. In this algorithm, attribute weight is calculated by CRITIC method which is a well-known objective method. At the end of this study, we noticed to a comparison between the proposed IVIFN-CE approach with other existing methods to verify the effectiveness of the algorithm.

Assessing the sustainable energy storage technologies using single-valued neutrosophic decision-making framework with divergence measure

Energy storage technologies (ESTs) facilitate to handle intermittency of energy resources by storage surplus energy to utilize when it is required. Due to influence of numerous quantitative and qualitative attributes, evaluation of ESTs can be treated as a critical and vague multi-attribute decision-making (MADM) problem. As a type of neutrosophic set, single-valued neutrosophic set (SVNS) has been proven as one of the valuable and flexible ways to handle the imprecise, indeterminate and inconsistent information arises in real applications. Considering the uncertainty in ESTs selection process, this paper aims to propose an integrated MADM methodology by combining the MEthod based on the Removal Effects of Criteria (MEREC) and Evaluation based on Distance from Average Solution (EDAS) techniques with SVNSs. In this methodology, the score function and divergence measure-based procedure is proposed to derive the decision experts’ weights. In this respect, an innovative single-valued neutrosophic (SVN)-divergence measure is introduced with its desirable characteristics. Furthermore, the MEREC technique is extended under SVNS context to determine objective weights of the attributes. Later, an extended EDAS technique is introduced with the combination of SVN-divergence measure and MEREC model to assess and prioritize the alternatives under SVNS environment. To confirm the capability of introduced MADM methodology, a case study of ESTs selection is presented within the context of SVNS. Moreover, the merits of the proposed framework in terms of flexibility and robustness are shown by comparative analysis. Finally, the sensitivity analysis proves the stability of our obtained results.

An integrated cross entropy methodology for planning scheme evaluation of highway transportation hub with interval-valued intuitionistic fuzzy information

After entering the 21st century, China’s national economy has shown a rapid growth momentum, the comprehensive transportation system has been continuously improved, the road traffic infrastructure has made remarkable achievements, and the modern logistics industry has also risen rapidly and grown rapidly, which has greatly changed the market demand for road transport hubs. The road transport hub is the main node of the road transport network, the hub of passenger and freight distribution of road transport, and the organizational center for the interconnection of road transport and other transport modes and the development of comprehensive transport. Highway transportation hub is an important part of highway transportation infrastructure and plays an important role in highway transportation. The planning scheme evaluation of highway transportation hub is a multi-attribute decision making (MADM). This paper intends to propose a MADM methodology based on cross-entropy (CE) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for planning scheme evaluation of highway transportation hub. First of all, this paper extends the cross entropy method under the IVIFSs to propose the interval-valued intuitionistic fuzzy number CE(IVIFN-CE) method, it enlarges the application range of the CE method. Secondly, a new MADM model for planning scheme evaluation of highway transportation hub based on IVIFN-CE algorithm is proposed.

User-focused Trip Planner with an Expanded Suite of Features: Riga Case Study

The research aims to develop a sustainable and personalised trip planner concept for Riga city, which will add information covering safety, service quality, and environmental aspects and provide recommendations for building an optimal travel plan based on the ranking results. The set of route attributes, which cover transportation service quality indicators, was identified and includes time, price, accessibility, reliability, comfort, safety, and environmental aspects. One of the user-focused interactions within the trip planning exercise is to introduce the possibility for travellers to choose weights for selected route attributes. Then the different possible route realisation rates using the multi-attribute decision-making methodology. The Technique for Order Preferences by Similarity to an Ideal Solution method was used for this case study.

Generalized triangular Pythagorean fuzzy weighted Bonferroni operators and their application in multi-attribute decision-making

<abstract><p>The consolidation of evaluations from various decision-makers within a group, concerning multiple attributes of limited schemes, seeks to unify or compromise collective preferences according to specific rules. The superior characteristics of Possibility Fuzzy Sets (PFS) in membership endow it with enhanced capabilities in depicting ambiguous information. The Bonferroni operator proficiently mitigates the influences of interrelations between attributes in decision-making dilemmas. To address the Multi-Attribute Decision Making (MADM) conundrum wherein attribute values are associative Triangular Pythagorean Fuzzy Numbers (TPFNs), a novel methodology leveraging the Generalized Triangular Pythagorean Fuzzy Weighted Bonferroni Mean (GTPFWBM) operator and the Generalized Triangular Pythagorean Fuzzy Weighted Bonferroni Geometric Mean (GTPFWBGM) operator is advanced. Initiating with the foundational Triangular Pythagorean Fuzzy Set and the Generalized Bonferroni Mean (GBM) operator, both the GTPFWBM and GTPFWBGM operators are delineated. Subsequent exploration dives into the intrinsic properties of these pioneering operators, encompassing facets like reducibility, permutation invariance, idempotency, monotonicity and boundedness. Building upon this foundation, a MADM methodology predicated on the GTPFWBM and GTPFWBGM operators is conceptualized. The culmination of this research underscores the method's rationality and practicality, illustrated through a venture capital investment exemplar.</p></abstract>

Performance Evaluation of Construction Sub‐contractors using Ordinal Priority Approach

The construction industry has been recognized as one of the crucial industries of a country. In large-scale public projects, it is frequently observed that the client, who serves the public, seeks the services of several sub-contractors (project outsourcing firms) to execute projects. It also seeks the consulting firms' services to monitor the progress and performance of these sub-contractors. However, performance evaluation of a large number of sub-contractors is not only a challenging phase but also a source of conflict and mistrust between the evaluators and evaluatees because of the subjectivity in the evaluation process. The current study classifies perceived organizational performance into two streams, arguing that the one involving the opinions from the independent evaluators is more objective than the self-evaluation one. The study also makes a pioneering attempt in post-qualification performance evaluation of sub-contractors through the Ordinal Priority Approach (OPA), a promising multi-attribute decision-making methodology. The deployment of the OPA allows the decision-makers to estimate the weights of the evaluation criteria, the sub-contractors to be evaluated, and the experts who evaluated them simultaneously. Thus, the methodology can minimize the causes of mistrust by uncovering unreliable experts and inappropriate criteria. Also, a novel Relative Performance Index (RPI) has been proposed to standardize the performance evaluation system. The results show that evaluation of the firms does not end at their evaluation as without evaluating the evaluators, such an evaluation is only partially effective.

EXPERIMENTAL INVESTIGATION AND PARAMETRIC OPTIMIZATION OF NEUROSURGICAL BONE GRINDING UNDER BIO-MIMIC ENVIRONMENT

Bone grinding is a craniotomy procedure which is used to remove a bone flap from the skull to expose and create an access for the dissection of tumors. In this study, a computer-controlled neurosurgical bone grinding has been used to explore the effect of various neurosurgical bone grinding parameters, such as cutting forces, torque, grinding force ratio, and temperature generated during bone grinding have been investigated. Bone samples after grinding have been assessed for morphological analysis. Based on the outcomes, a multi-attribute decision-making methodology based on grey relational analysis has been adopted. Regression models have been developed and then validated to ensure the adequacy of the developed models. Subsequently, a comparative analysis of experimental and predicted results have been presented. It is revealed that grinding forces and torque decreased with the escalation of rotational speed from 35,000 revolutions per minute (rpm) to 55,000[Formula: see text]rpm. The optimum combination of process parameters found as rotational speed of 55,000[Formula: see text]rpm, feed rate of 20[Formula: see text]mm/min, and depth of cut of 0.50[Formula: see text]mm.

LCTC Ships Concept Design in the North Europe- Mediterranean Transport Scenario Focusing on Intact Stability Issues

In late years, the size of RoRo cargo ships has continuously increased, leading to the so-called Large Car Truck Carriers (LCTC). The design of these vessels introduced new challenges that shall be considered during the ship design since the conceptual stage, which has a very strong impact on the technical and economic performances of the vessel during all its life-cycle. In this work, the concept design of an LCTC is presented based on Multi-Attribute Decision Making (MADM). A large set of design alternatives have been generated and compared in order to find out the most promising feasible designs. The proposed approach is based on a Mathematical Design Model (MDM) capable to assess all the main technical and economic characteristics for each design. Among the others, here focus has been done on the ship stability to assure the compliance with statutory rules within the MDM. A new stability metamodel has been developed capable to define the cross curves of stability at the concept design stage. The proposed MADM methodology has been applied to North Europe-Mediterranean transport scenario highlighting the impact of main particulars describing hull geometry on the technical and economic performances of an LCTC ship.

An integrated parallel big data decision support tool using the W-CLUS-MCDA: A multi-scenario personnel assessment

One of the most primary issues that organizations have to deal with is incorporating massive structured data problems, simultaneously. Additionally, a vital division in any organization is the department of human resources (HR), which is in charge of the recruitment and personnel selection procedures. Due to the nature of the personnel assessment problems, which include multiple candidates as alternatives along with various complex evaluating criteria, these types of problems can be tackled by the aid of multi-attribute decision making (MADM) techniques. Moreover, in mega-structured organizations, the procedure of personnel selection contains massive structures of data due to the number of potential candidates for job positions in various sub-divisions and departments. Therefore, the personnel selection problem in such environments can be subjected as a big data problem which should be handled prudently to save time and cost. The main objective of the current study is to extend the CLUS-MCDA approach (CLUSter analysis for improving Multiple Criteria Decision Analysis) and integrate it with the Best–Worst Method (BWM) and a specific structure to solve multi-scenario big data decision-making problems. In this study, to validate the practicality and reliability of the W-CLUS-MCDA approach, multiple personnel selection and risk assessment problems have been investigated with various scenarios within several departments, simultaneously. This study has also introduced the concept of multi-scenario parallel decision making (PDM) within the context of MADM methodology using a data-driven decision-making approach solving various big data problems.

A multi-attribute modelling approach to evaluate the efficient implementation of ICT in schools

Comprehensive implementation of Information and Communication Technologies in schools is a key factor in empowering students in the European Union (EU) for their future roles. The framework, DigCompOrg, was proposed in 2015 under the direction of the European Commission to encourage self-assessment within EU schools and to update the level of digitalization. This article presents a computer-supported model based on this framework and a multi-attribute decision-making methodology named, DEX. The model was built by a group of experts and tested on selected schools in Slovenia. The main advantages of the model are: the use of qualitative value scales for attributes which do not have exact values; the use of a hierarchical structure for attributes; a transparent presentation of the interconnectedness of these attributes; and the use of simple if–then aggregation rules to allow the use of non-fixed weights. An application of our model to a selected school demonstrates the potential for knowledge modelling to facilitate upgrades of existing assessment tools and to provide a better understanding and analysis of assessment results.

Multiattribute decision making based on Shannon's information entropy, non-linear programming methodology, and interval-valued intuitionistic fuzzy values

In this paper, we propose a novel multiattribute decision making (MADM) methodology based on Shannon's information entropy, the non-linear programming (NLP) methodology and interval-valued intuitionistic fuzzy values (IVIFVs), where attributes’ weights and evaluating attributes’ values with respect to alternatives are expressed by IVIFVs. Several examples are used to illustrate that the proposed MADM methodology can conquer the drawbacks of Wang and Chen's MADM methodology (2018) in interval-valued intuitionistic fuzzy (IVIF) environments.

Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

Due to the superiority in expressing the uncertain and vague information, the hesitant fuzzy set (HFS) is regarded as an important tool to deal with multiattribute decision-making (MADM) problems. Quantitative and qualitative fuzzy measures have been proposed to solve such problems from different points. However, most of the existing information measures for HFSs are related to such fuzzy measures as distance, similarity, entropy, and correlation coefficients. The grey relational analysis is omitted. Besides, the existing grey relational analysis for HFSs only considers the range or distance between HFSs data which is only a partial measure of the HFSs. Therefore, in this paper, we improve the grey relational analysis for HFSs and explore a novel slope grey relational degree by considering another factor of HFSs data: the slope. Further, we combine both the distance and slope factors of HFSs data to construct a synthetic grey relational degree that describes the closeness and variation tendency of HFSs simultaneously, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs MADM problems. Finally, combining with two practical MADM examples about energy policy selection and multisensor target recognition, we obtain the most desirable decision results. Compared with the previous methods, the validity, comprehensiveness, and discrimination of the proposed synthetic grey relational degree for HFSs are demonstrated in detail.

A multi-objective decision-making framework using preference selection index for assembly job shop scheduling problem

Assembly job shop scheduling problem (AJSSP) is an important NP-hard scheduling problem very difficult to solve because of its complex precedence relationship between jobs and product structures. In the present work, a systematic multi-attribute decision-making methodology namely, preference selection index method is adopted for ranking priority dispatching rules for scheduling an assembly job shop. A simulation model of an assembly job shop with seven work stations and one assembly station is developed for the purpose of experimentation. Eight priority dispatching rules from literature are identified and incorporated in the simulation model. Performance measures considered for analysis include mean flow time, maximum flow time, mean tardiness, maximum tardiness and machine utilisation. Products with single level assembly structure, two level assembly structure and three level assembly structure are examined to demonstrate and to check the applicability of the proposed methodology. The best performing priority dispatching rules are determined in a multi-objective environment.

A multi-attribute decision-making approach to the selection of point-of-use water treatment

Despite the recent major accomplishment in meeting our global water target in the UN Millennium Development Goal 7, a significant number of people, mostly in low-income regions, are still without access to reliable and safe water sources. To address the health burden of inadequate water in these regions, several point-of-use (POU) treatment technologies have been developed and are now available in the market. This outcome has created both an opportunity and a challenge in selecting technologies that are suitable in poor communities. In the current work, we present an application of the multi-attribute decision-making (MADM) methodology to rank and select options for POU water treatment technologies that are appropriate for low-income communities. The paper makes three significant research contributions. Firstly, we review the features, efficacy, and operation of currently available POU water treatment technologies to identify challenges in their implementation. Secondly, we propose a set of evaluation criteria, categorized into four general themes, for assessing the efficacy and suitability of a POU water treatment technology in a given area. We also examine the application of MADM methodology in prioritizing alternatives using these criteria as attributes. Finally, we present a case application of our proposed methodology in a rural municipality in Quezon Province, Philippines. Six common POU water treatment alternatives were considered: solar disinfection, boiling, chlorination, combined disinfection and flocculation with Moringa oleifera, ceramic filtration and biosand filtration. Results of the analysis show that water treatment with M. oleifera and ceramic filters are the most preferred treatment alternatives in the municipality, while chlorination is the least preferred. The most important factors for selecting water treatment methods are initial costs, by-products of water treatment, throughput, and energy input. The proposed MADM model can be applied to other areas where a centralized water treatment system is not available, and a suitable POU water treatment is needed. The inclusion of multi criteria considerations in the selection process ensures that the chosen POU water treatment provides the optimal health, economic, and environmental benefits to the community.

Assessment of trading partners for China's rare earth exports using a decision analytic approach.

Chinese rare earth export policies currently result in accelerating its depletion. Thus adopting an optimal export trade selection strategy is crucial to determining and ultimately identifying the ideal trading partners. This paper introduces a multi-attribute decision-making methodology which is then used to select the optimal trading partner. In the method, an evaluation criteria system is established to assess the seven top trading partners based on three dimensions: political relationships, economic benefits and industrial security. Specifically, a simple additive weighing model derived from an additive utility function is utilized to calculate, rank and select alternatives. Results show that Japan would be the optimal trading partner for Chinese rare earths. The criteria evaluation method of trading partners for China's rare earth exports provides the Chinese government with a tool to enhance rare earth industrial policies.

An Integrated Fuzzy Multi-Attribute Decision-Making Methodology for Evaluation of Mechanical Product

The concept evaluation of mechanical product is essentially a multi-attribute decision making (MADM) problem in the fuzzy environment. In order to reduce the adverse impact of preference or judgments of decision makers on the final evaluation results, this paper attempts to propose an integrated fuzzy multi-attribute decision making methodology that combines the fuzzy TOPSIS technique and the objective weighting to evaluate mechanical product. The fuzzy TOPSIS technique is applied to rank the design alternatives, and the objective weighting method is integrated into the fuzzy TOPSIS technique to determine the appropriate criteria weights. Finally, a real application to pan mechanism selection for a cooking robot is demonstrated.

On the efficacy of modeling multi-attribute decision problems using AHP and Sinarchy

AHP is a multi-attribute decision-making methodology widely used by both practitioners and researchers. In the 1980s, critics had raised questions regarding its proper use. There were quite a few suggested modifications to overcome the supposed limitations of AHP. These modifications are themselves limited as they typically impede the applicability of AHP. In this paper, we revisit some of the earlier criticisms. We have two objectives (1) to articulate the proper use of AHP by highlighting the assumptions and implications underlying AHP, and (2) to show that Sinarchy can be used to address the earlier criticisms while maintaining the applicability of the AHP framework. We identify that in AHP, tradeoffs between criteria vary amongst individual alternatives and are dependent on the alternative’s proportion of contribution towards each criterion. For problems where tradeoffs between criteria are in terms of their relative measurements, Sinarchy should be used. It is also shown that Sinarchy can prevent rank reversal. Illustrative examples are included throughout.

An interval-based MADM approach to the identification of candidate alternatives in strategic resource planning

Multi-attribute decision making (MADM) methods have been widely used in the strategic planning of electric utilities for selecting the best resource strategy with regard to the chosen attributes. In many MADM problems, however, the information available to the decision-maker (DM) is often imprecise due to inaccurate attribute measurements and inconsistent priority judgments. This paper presents an interval-based MADM approach in support of the decision making process with imprecise information. The proposed MADM methodology is based on the model of lineal additive utility function but extends the problem formulation with the measure of composite utility variance. Its main contribution is to help the DM gain insight into how the imprecise data may affect the choice toward the best solution and how a set of acceptable alternatives may be identified with certain confidence. Sample case studies are provided to illustrate the concepts introduced in this paper.

Knowledge acquisition for knowledge based decision systems

Knowledge-based decision systems support the decision analysis process with decision analytic knowledge as well as domain-specific knowledge. The knowledge in the knowledgebased decision system is usually represented by influence diagrams. In the development of knowledge-based decision systems, knowledge acquisition is one of the most difficult problems. This article suggests a knowledge acquisition process using verbal protocol analysis and multi-attribute decision-making methodology. An environmental decision-making problem is used as an illustrative example to explain the developed knowledge acquisition process. The suggested process is so flexible that it can be expanded or applied to other problems with ease. Some guidelines for the knowledge-based decision system are also suggested.