Multiple Criteria Decision-making Problem Research Articles

Using CRITIC-TOPSIS and python to examine the effect of 1-Hepatnol on the performance and emission characteristics of CRDI CI engine with split injection

Recently, biofuels with higher alcohol content have become a promising alternative to diesel fuel. These fuels are appealing because they are sustainable, renewable, and possess attractive fuel properties. This study uses a split injection strategy to analyze the performance and emissions of a CRDI diesel engine fueled by 1-heptanol. The work involved testing different fuel blends, ranging from 10 % to 30 %, while maintaining a constant engine speed of 1500 rpm and varying the operating load between 0 kg and 12 kg in 4 kg increments. During the second stage, the CRITIC-TOPSIS method determines the objective weights and rankings of various criteria and alternatives. A Python approach based on machine learning was used to ensure the CRITIC-TOPSIS results were accurate. Seven criteria were evaluated to maximize BTE while minimizing BSFC, NOx, smoke opacity, HC, CO, and CO2. The experimental results showed a slight drop of 2.98 % in BTE and an increase of about 13.33 % in BSFC. NOx and smoke opacity were reduced by 7.13%–4.53 %, while there was a 12.12 % increase in HC, 6.45 % higher CO, and a 5.5 % increase in CO2 at full load. Adding 1-heptanol to diesel and using a split injection strategy significantly reduced NOx and smoke opacity. The final ranking and best blend are determined using CRITIC-TOPSIS and Python algorithms to estimate performance and emissions criteria. At a load of 4 kg, D100 ranks first with a relative closeness value of 0.642, while at a pack of 8 kg, the blend HP20D80 ranks first with a relative closeness value of 0.633. According to the rankings, the HP20D80 blend is the best option for achieving optimal performance and reduced emissions in CRDI diesel engines. A research paper has presented a unique approach to multiple criteria decision-making (MCDM) validated using a Python algorithm. This method can assist decision-makers in making better-informed choices when faced with MCDM problems that involve various criteria and alternatives.

A Fuzzy Ontology-Based Decision Tool for Concept Selection to Maintain Consistency Throughout Design Iterations

Abstract Concept selection is one of the most important activities in new product development processes in that it greatly influences the direction of subsequent design activities. As a complex multiple-criteria decision-making problem, it often requires iterations before reaching the final decision where each selection is based on previous selection results. Reusing key decision elements ensures decision consistency between iterations and improves decision efficiency. To support this reuse, this article proposes a fuzzy ontology-based decision tool for concept selection. It models the key decision elements and their relations in an ontological way and scores the concepts using weighted fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). By applying the tool to an example, this article demonstrates how the concepts, criteria, weights, and results generated for one decision can be reused in the next iteration.

Ranking Agility Factors to Reliably Sustain a Green Industrial Supply Chain Using the Fuzzy Analytic Network Process and Ordinal Priority Approach

Suppliers can achieve high levels of supply chain sustainability by improving the related factors. An agile supply chain can support sustainability. Identifying and ranking agility factors in the SAIPA company in Iran to reach a sustainable and green supply chain is the primary purpose of this study. SAIPA is an automotive company with an extensive supply chain. The data were quantitative, and the collection was completed by reviewing the literature and questioning experts. The FANP and the OPA methods were the tools used to analyze the data. These methods are proper for facing multiple-criteria decision-making problems, as in the case of this paper. We first identified the factors (capabilities, enablers, and attributes) using a literature review. After that, we gathered the data for ranking analysis by collecting the opinions of SAIPA’s organizational experts using a pairwise comparison questionnaire for the FANP and a prioritizing list for the OPA. Both methods showed that “Quickness” is the capability with the highest priority. “Customer Sensitivity” was the most critical enabler, and “Accurate customer-based measures” was the most significant attribute of the FANP analysis. The OPA results showed that “Information Management” was the first enabler, and “Efficient funds transfer” took first place among all the attributes. Managers should pay more attention to these factors to develop agile supply chains in the SAIPA company. The results also showed that the methods proposed for multi-attribute decision-making problems like the FANP have shortcomings, such as difficulties completing the pairwise comparison matrix due to burdensome data collection in cases similar to the one in this study with many factors.

An improvement to the interval type-2 fuzzy VIKOR method

Vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) method is one of the most popular multiple criteria decision making (MCDM) techniques in which, the optimal alternative is determined based on an agreement between maximum group utility and minimum individual regret. Despite VIKOR presents important advantages regarding other techniques for decision situations, it may result in a situation where an alternative with high measures for most criteria and low value for one criterion is not chosen as the optimal alternative. Thus, VIKOR may lead to unreasonable and inconsistent results and cannot present true situation of alternatives in indices’ list. The goal of this paper is to present an improved extended version of interval type-2 fuzzy VIKOR (IT2FVIKOR) method for relieving the drawbacks of usual IT2FVIKOR. The proposed indices present more continuous and logical values than prior one such that better ranking results obtain for the MCDM problems. In order to demonstrate effectuality of the suggested method, a case study and an illustrative example are performed. Eventually, the extracted ranking upshots are analyzed with the others.

Parsimoniously interactive multiple attribute rating (PIMAR) and its applications to large-scale inventory classification in the context of Industry 3.5

This study investigates parsimoniously interactive multi-attribute rating (PIMAR) methods suitable for intelligent manufacturing in Industry 3.5, where elicitation and preservation of human intelligence are essential to enable flexible decisions. The focus is on deterministic interactive multiple criteria decision-making (MCDM) problems with many alternatives and a small number of criteria. The aim is to reduce the number of interactions with decision-makers (DMs) as much as possible, as the low- or medium-level of automation in Industry 3.5 keeps DMs busy with routine jobs, and there may be a thousand or a million alternatives in the planning system. The study proposes a research framework for a comprehensive investigation of PIMAR methods, including method design and effectiveness examinations. Various conventional MAR methods are compared with the proposed PIMAR method using synthetic artificial decision-makers (ADMs) with specific preferences generated using various weight distributions. The results show that the proposed PIMAR method outperforms conventional methods in both linear and prospect-theory utilities across various weight distributions and permutations. Furthermore, the study suggests that interactions enhance the rating effectiveness, and PIMAR methods are suitable for MCDM problems with many alternatives and a small number of criteria.

A hybrid MCDM model with Monte Carlo simulation to improve decision-making stability and reliability

Employing an appropriate method to achieve a reliable decision remains a challenge for decision-makers (DMs) in the multiple-criteria decision-making (MCDM) process owing to its inherent model-related complexity, sensitivity, and uncertainty. In this context, this study proposes an innovative hybrid MCDM model that integrates criteria importance through intercriteria correlation (CRITIC), multi-attributive border approximation area comparison (MABAC), and k-means with Monte Carlo simulation (i.e., CRITIC–MABAC–Kmeans with Monte Carlo simulation), aiming to address MCDM problems with substantial stability and reliability. Specifically, MABAC attests to the stability of this method, as it is less affected by normalization and weighting schemes. In addition, the challenge of conflicting k-means clustering outcomes, owing to diverse initial centroid selections, is mitigated by a Monte Carlo simulation, which identifies the most probable type of result and compensates for small-sample size bias. The model performance is tested using a case study of observing transport safety accomplishments in the ASEAN region. Enhanced multiple comparisons of the experimental results verify the quality, efficiency, and adaptability of the proposed model, indicating its feasibility for DMs, policymakers, and practitioners as a practical tool for handling real-life MCDM activities in various domains under compounded sensitivity and uncertainty.

Domain-driven multiple-criteria decision-making for flight crew decision support tool

During the flight, the crew might consider modifying their planned trajectory, taking into account currently available information, such as an updated weather forecast report or the already accrued amount of delay. This modified planned trajectory translates into changes on expected fuel and flying time, which will impact the airline’s relevant performance indicators leading to a complex multiple-criteria decision-making problem. Pilot3, a project from the Clean Sky Joint Undertaking 2 under European Union’s Horizon 2020 research and innovation programme, aims to develop an objective optimisation engine to assist the crew on this process. This article presents a domain-driven approach for the selection of the most suitable multiple-criteria decision-making methods to be used for this optimisation framework. The most relevant performance indicators, based on airline’s objectives and policies, are identified as: meeting on-time performance, leading to a binary value in a deterministic scenario; and total cost, which can be disaggregated into sub-cost components. The optimisation process consists of two phases: first, Pareto optimal solutions are generated with a multi-objective optimisation method (lexicographic ordering); second, alternative trajectories are filtered and ranked using a combination of multi-criteria decision analysis methods (analytic hierarchy process and VIKOR). A realistic example of use shows the applicability of the process and studies the sensibility of the optimisation framework.

A New Approach to Multiple Criteria Decision-Making Using the Dice Similarity Measure under Fermatean Fuzzy Environments

<p>Many contemporary multiple criteria decision-making (MCDM) problems are rather complicated and uncertain to manage. MCDM problems can be complex because they involve making decisions based on multiple conflicting criteria, and they can be uncertain because they often involve incomplete or subjective information. This can make it difficult to determine the optimal solution to the problem. Over the last decades, tens of thousands MCDM methods have been proposed based on fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs). In this paper, we propose a new MCDM method based on Fermatean fuzzy sets (FFSs) and improved Dice similarity measure (DSM) and generalized Dice similarity measures (GDSM) between two FFSs with completely unknown weights of criteria. When a decision matrix is given, we calculate the weights of criteria using a normalized entropy measure while the weights of criteria are not given by the decision-maker. Then, we use the proposed improved DSM and GDSM between two FFSs that take the hesitancy degree of elements of FFSs into account and develop a new MCDM method. Finally, we use the values of the proposed improved DSM and GDSM between two FFSs to get the preference order of the alternatives. The proposed method can overcome the drawbacks and limitations of some existing methods that they cannot get the preference order of the alternatives under Fermatean fuzzy (FF) environments.</p> <p> </p>

The D numbers risk ranking based method by considering subjective weights and objective weights with incomplete linguistic information

Risk prediction, assessment, and control are key parts of the successful operation and sustainable development of any enterprise. During the process of product failure risk assessment, evaluated risk factors belong to the group of multiple-criteria decision-making (MCDM) problems, including severity, occurrence, and detection when failure occurs. However, the traditional risk ranking method does not consider the subjective and objective weights of the assessment factors, and during risk prediction, assessment, and control, some unknown information in many practical situations is included. These reasons may cause the risk assessment results to be biased. In order to effectively deal with the problem of risk assessment, this paper proposes a D numbers risk ranking method by considering subjective and objective weights between assessment factors under incomplete linguistic information. An illustrative example of screening unit failure risk assessment is used to explain and prove the rationality and correctness of the proposed method. Some risk ranking methods are compared with the proposed D numbers risk ranking method, and the simulation results present that the proposed ranking method handles the issue of incomplete information and provides more reasonable risk ranking results.

Embedding Best-Worst Method into Data Envelopment Analysis

In real-life applications, there generally exist Decision Makers (DMs) who have preferences over outputs and inputs. Choosing appropriate weights for different criteria by DMs often arises as a problem. The Best-Worst Method (BWM) in Multiple Criteria Decision-Making (MCDM) depends on very few pairwise comparisons and just needs DMs to identify the most desirable and the least desirable criteria. Unlike MCDM, Data Envelopment Analysis (DEA) does not generally assume a priority for an output (an input) over any other outputs (inputs). The link between DEA and MCDM can be introduced by considering Decision-Making Units (DMUs) as alternatives, outputs as criteria to be maximized, and inputs as criteria to be minimized. In this study, we propose a linear programming model to embed DEA and BWM appropriately. We first propose a modified BWM linear programming model to satisfy all conditions that DMs can assume. We then illustrate how a conventional DEA model can be developed to include the BWM conditions. From our approach, the MCDM problem to obtain the optimal weights of different criteria are measured. At the same time, the relative efficiency scores of DMUs corresponding to the MCDM criteria are also calculated. We provide the foundation of measuring the efficiency scores when most desirable and the least desirable inputs and outputs are known. To show the process of the proposed approach, a numerical example (including 17 DMUs with seven inputs and outputs) is also discussed.

Medical health resources allocation evaluation in public health emergencies by an improved ORESTE method with linguistic preference orderings

As an important major public health emergency, COVID-19 broke out more than two years. At present, China has entered the post-epidemic era. However, it is still necessary to study the medical health resource allocation in public health emergencies. Therefore, the evaluation of medical health resources allocation is important. Firstly, we use two kinds of linguistic preference orderings (LPOs) to represent experts’ opinions when evaluating the medical health resources allocation in public health emergencies. Then, a novel ORESTE method with LPOs is developed to solve multiple criteria decision-making (MCDM) problems. Additionally, we apply the proposed ORESTE method to solve a practical MCDM problem involving the medical health resources allocation in public health emergencies. Finally, some comparative analyses among the proposed ORESTE method and some existing methods under a double hierarchy linguistic environment are set up, and some discussions are summarized to show the validity and applicability of the proposed novel ORESTE method.

Flood vulnerability analysis using different aggregation frameworks across watersheds of Ardabil province, northwestern Iran

Flood-vulnerable-area selection could be interpreted as an intricate multiple-criteria decision-making (MCDM) problem. Lacking systematic flood vulnerability maps in Iran usually leaves the National Disaster Management Organization and provincial authorities to rely on limited knowledge to inform their emergency actions. Therefore, the present study was conducted as pioneer research in flood problematization for solving disaster issues that have not been adequately considered in the country. To obtain a more trustworthy and safer decision, a complex set of multivariate flood vulnerability assessments was done for 26 watersheds of Ardabil province using the novel-ensemble MCDM frameworks. Towards this, the complex proportional assessment of alternatives (COPRAS) and the UK Department for International Development (DFID) approach was integrated with widely used weighting methods of Shannon's entropy (Entropy) and analytical hierarchy process (AHP). Finally, four different aggregation frameworks of COPRAS-Entropy, COPRAS-AHP, DFID-Entropy, and DFID-AHP were developed and compared based on 19 criteria and three factors (exposure, sensitivity, and resilience). The flood vulnerability was obtained as 0.70 ± 0.13, 0.66 ± 0.18, 0.17 ± 0.10, and 0.20 ± 0.15, respectively, based on COPRAS-Entropy, COPRAS-AHP, DFID-Entropy, and DFID-AHP. As can be seen, higher values were estimated according to COPRAS-based frameworks rather than DFID-based frameworks, except for resilience. The top-ranked watersheds in terms of exposure (i.e., Mashiran) and sensitivity (i.e., Samian) produced by aggregation frameworks match entirely. However, the top-ranked watersheds based on resilience and flood vulnerability slightly mismatch among the used aggregation's frameworks. These findings help the responsible authorities for natural disaster risk reduction to promote sustainable development processes and equitable access to financial resources.

An Improved Rank Order Centroid Method (IROC) for Criteria Weight Estimation: An Application in the Engine/Vehicle Selection Problem

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.

Selection of Materials in Construction Industry with Multi-Criteria Decision Making Models

Construction companies play a central role in the development of a country. The construction products stimulate the growth of private physical structures and public infrastructure for many productive activities such as utilities, commerce, and services. A good material selection is an essential part that needs to be studied in order to come out with a product that is of high quality and safe to be used. As a result, materials selection in the field of construction has also become an interesting topic for many researchers. Based on the previous literature, several multiple criteria decision making (MCDM) models are proposed to choose the best materials for a particular application in recent years. With multiple decision criteria, material selection is treated as an MCDM problem. According to past studies, the researchers have emphasized the integration of MCDM models to tackle complicated decision making problems. MCDM model is a good tool and has been widely proposed in various fields as MCDM models are capable to tackle the decision making problems that are taking multiple decision criteria into consideration. The intended purpose of this paper aims to provide a literature review on the material selection in the field of construction with MCDM models.

A Lance Distance-Based MAIRCA Method for q-Rung Orthopair Fuzzy MCDM with Completely Unknown Weight Information

The purpose of this manuscript is to develop a novel MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method to solve the MCDM (Multiple Criteria Decision-Making) problems with completely unknown weights in the q-rung orthopair fuzzy (q-ROF) setting. Firstly, the new concepts of q-ROF Lance distance are defined and some related properties are discussed in this paper, from which we establish the maximizing deviation method (MDM) model for q-ROF numbers to determine the optimal criteria weight. Then, the Lance distance-based MAIRCA (MAIRCA-L) method is designed. In it, the preference, theoretical and real evaluation matrices are calculated considering the interaction relationship in q-ROF numbers, and the q-ROF Lance distance is applied to obtain the gap matrix. Finally, we manifest the effectiveness and advantage of the q-ROF MAIRCA-L method by two numerical examples.

A road maintenance management strategy evaluation and selection model by integrating Fuzzy AHP and Fuzzy TOPSIS methods: The case of Ethiopian Roads Authority

The construction industry is the most important for the social, economic, and political development of countries. The road construction sector is the largest in the construction industry. Inappropriate road maintenance leads to an inefficient and random way of spending maintenance costs. Therefore, road maintenance needs to prevent deterioration that shortens the service life of the road. Thus, the study was focused on a road maintenance strategy evaluation and selection model by integrating Fuzzy AHP and Fuzzy TOPSIS methods. The evaluation was a multiple-criteria decision-making problem. The fuzzy AHP technique was used to determine criterion weight. To deal with the uncertain judgment of decision makers, a fuzzy TOPSIS method is applied as an evaluation tool in a fuzzy environment where the uncertain, subjectivity, and imprecise judgments of decision makers are handled with linguistic values parameterized by triangular fuzzy numbers. To achieve the objective, the data were collected from primary and secondary sources. The method of data analysis for this study was made by integrated methodology, and the analysis was made by using Microsoft Excel. The study revealed that quality, maintenance time, environment, production cost, and profit are the most important selection criteria. The finding of the study revealed that periodic maintenance was the best relative closeness value among the considered alternatives. This study recommends that the analysis method can be implemented by the Ethiopian Roads Authority to demonstrate its practicality in selecting the appropriate strategy.

Power Muirhead mean operators of interval-valued intuitionistic fuzzy values in the framework of Dempster–Shafer theory for multiple criteria decision-making

Multiple criteria decision-making (MCDM) based on interval intuitionistic fuzzy value (IVIFV) is a process of aggregating decision criteria represented by multiple interval-valued intuitionistic fuzzy numbers to select the optimal alternative. Among them, an aggregation operator is an indispensable tool, and the properties of an aggregation operator directly affect the decision results. Existing aggregation operators based on IVIFV have satisfactory results in eliminating the correlation between criteria and removing the influence of outliers on the results. However, there are some unreasonable results due to some undesired properties of IVIFVs. In this paper, IVIFV operation under the Dempster-Shafer theory (DST) framework is applied to combine the power average and Muirhead mean operators and interval intuitionistic fuzzy power Muirhead mean operators under DST framework are presented. Then a method based on the presented operators for MCDM problems is proposed. Finally, a set of numerical experiments are conducted to demonstrate the proposed method. The experimental results suggest that the proposed method not only retains the robustness of the power average operator and the capability of the Muirhead mean operator, but also eliminates a shortcoming that existing interval intuitionistic fuzzy operators cannot handle the case where the weights are in IVIFVs.

SMAA–Bicapacity–Choquet–Regret Model for Heterogeneous Linguistic MCDM With Interactive Criteria With Bipolar Scale and 2-Tuple Aspirations

Decision making could be an extremely complicated process. Especially, in situations in which the diversity of backgrounds and experience of decision makers (DMs) may require various forms to model their opinions. DMs’ rational choice even may be distorted by their judgements due to limitations of human cognitive competence. Generally, the interactions between criteria, the bipolar scale with negative values, and linguistic-valued aspirations on criteria are also needed to be addressed. In addition, several types of input information may be stochastic or uncertain, such as criteria evaluations, capacities, and preference coefficients. Our aim is to address all previous issues simultaneously by proposing a novel model that incorporates Choquet integral, bicapacity, regret theory, and stochastic multiobjective acceptability analysis (SMAA), i.e., SMAA–bicapacity–Choquet–regret for heterogeneous linguistic multiple criteria decision-making (MCDM) problems with interactive criteria with bipolar scale and 2-tuple aspirations. The heterogeneous linguistic information can offer a highly flexible way to express DMs’ preference; a novel 2-tuple aspirations-based utility within regret can better reflect the subjective perceptions of DMs in MCDM problems with 2-tuple aspirations on criteria; bicapacity can weigh the criteria and their coalitions, and Choquet integral concerning bicapacity can aggregate interactive criteria with bipolar scale; and finally, an application to evaluate medical waste disposal technologies is given, and a comparative analysis is performed to verify the applicability and effectiveness of the proposed model.

Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Magnesium (Mg) alloys are extensively used in the automotive and aircraft industries due to their prominent properties. The selection of appropriate process parameters is an important decision to be made because of the cost reduction and quality improvement. This decision entails the selection of suitable process parameters concerning various conflicting factors, so it has to be addressed with the Multiple Criteria Decision Making (MCDM) method. Therefore, this work addresses the MCDM problem through the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and COPRAS (COmplex PRoportional ASsessment) methods. The assessment carried out in the material Mg AZ91 with the Solid Carbide (SC) drill bit. The dependent parameters like drilling time, burr height, burr thickness, and roughness are considered with the independent parameters like spindle speed and feed rate. Drilling alternatives are ranked using the above said two methods and the results are evaluated. The optimum combination was found on the basis of TOPSIS and COPRAS for simultaneous minimization of all the responses which is found with a spindle speed of 4540 rpm and a feed rate of 0.076 mm/rev. The identical sequencing order was observed in TOPSIS and COPRAS method. The empirical model was developed through Box-Behnken design for each response. Superior empirical model developed for drilling time which is 3.959 times accurate than the conventional equation. The trends of various dependents based on the heterogeneity of various independents are not identical, these complex mechanisms are identified and reported. The optimized results of the Desirability Function Approach are greater accordance with the TOPSIS and COPRAS top rank. The confirmation results are observed with lesser deviation suggesting the selection of the above independent parameters.

Fabric Selection Based on Sine Trigonometric Aggregation Operators Under Pythagorean Fuzzy Uncertainty

ABSTRACT In the textile industry, selecting the optimal fabric is crucial in the design and production process. The multiple-criteria decision-making (MCDM) technique has been applied to address fabric selection problems. Because the sine trigonometry function maintains the periodicity and symmetry of the origin in nature, it satisfies the preferences of experts over multiple parameters. Considering the advantages of the sine trigonometry function, we introduce sine trigonometry Pythagorean fuzzy sets (ST-PFSs) and weighted averaging (ST-PFWA) and geometric (ST-PFWG) aggregation operators (AOs). Additionally, we propose a novel method based on ST-PFWA and ST-PFWG AOs to solve two different types of fabric selection MCDM problems. The first problem is provided by Pythagorean fuzzy numbers (PFNs), and we apply the proposed method to address the issue and conduct a comparative analysis with other PFS AOs. The second problem is offered with crisp values, and we apply a PFS linguistic term transform system to convert the values into PFNs. Then, we apply ST-PFWA and ST-PFWG AOs to settle the problem and perform a comparative analysis among different PFS linguistic terms. The complete ranking results illustrate that our proposed methodology is more efficient and reliable than previous approaches and can be used in other textile fields.