Complex Proportional Assessment Research Articles

CSsEv: Modelling QoS Metrics in Tree Soft Toward Cloud Services Evaluator based on Uncertainty Environment

Cloud computing (ClC) has become a more popular computer paradigm in the preceding few years. Quality of Service (QoS) is becoming a crucial issue in service alteration because of the rapid growth in the number of cloud services. When evaluating cloud service functioning using several performance measures, the issue becomes more complex and non-trivial. It is therefore quite difficult and crucial for consumers to choose the best cloud service. The user's choices are provided in a quantifiable manner in the current methods for choosing cloud services. Hence, this study attempts to achieve this objective through construction. decision-making framework so-called cloud services evaluator (CSsEv). The main indicator and its sub-indicators are formed as nodes at levels(n) in tree soft sets (TSSs). Thereafter Single Value Neutrosophic Sets (SVNSs) as branch of neutrosophic sets which conjunction with the Multi-Criteria Decision Making (MCDM) technique to facilitate analysis and evaluation process for the available Cloud services providers. Hence, entropy is employed to obtain indicators and sub_indicators’ weights and Complex Proportional Assessment utilizes these weights to facilitate the decision process of selecting optimal ClSPs.

Impacto da COVID-19 nas PMEs no Brasil e drivers de percepção gerencial: um novo modelo neural baseado em funções de utilidade ponderadas pela entropia

Abstract Departing from the inconclusive results of the scant literature on the COVID-19 impact on Small and Medium Enterprises (SMEs), this paper proposes a novel evaluation model for addressing this issue through managerial perceptions. Over 6000 SMEs responded to twelve rounds of surveys from 2020 to 2021 during the pandemic, allowing to track the evolution over time of the perceived impact of the pandemic on small businesses. A novel entropy-weighted utility function approach is proposed here, followed by artificial neural network regression to map the variables related to the SME’s businesses that most foster the perceived utility of each business criterion during the pandemic. First, weights of business-related criteria were computed using Stepwise Weight Assessment Ratio Analysis (SWARA), sorting their relative importance - or perceptions - based on information entropy ranks derived from questionnaires collected. Transfer entropy measurements also helped in unveiling the hidden cause-effect relationships among criteria. Second, business utility functions for each criterion were computed using Complex Proportional Assessment based on SWARA weights. Third, neural network regressions were used to explain the managerial perceptions on each business criterion during the pandemic, considering each business variable. Our expected and unexpected results suggest that more resilient SMEs in Brazil are 5-10 years old and operating in the services and construction sectors. Moreover, loan success is the second most impactful criterion, deeply impacting the continuity of economic activity levels, and it is not impacted by any other business criteria. Implications for policymakers and governmental actions are highlighted.

Utilizing an integrated AHP-COPRAS approach for battery selection in electric vehicles

Internal combustion engine vehicles provide better performance and longer-range using fossil fuels such as gasoline and diesel. However, fossil fuels are non-renewable and cause environmental pollution, alternative fuels such as blends of ethanol and biodiesel, hydrogen etc have been sought for these vehicles. On the other hand, some researchers prefer to design alternative vehicles such as hybrid and electrical vehicles, instead of changing the fuel type. Among the studied topics for alternative vehicles, the battery is one of the most important components, especially in electrical vehicles. Batteries are diversified with different criteria such as battery life, nominal voltage, energy density, volumetric energy density, specific power, operating temperature, and production cost. In this study, the expert perspective was utilized when selecting the battery type to be employed for the energy source through utilizing the Integrated Analytical Hierarchy Process (AHP) - Complex Proportional Assessment (COPRAS), a multi-criteria decision-making approach. Various batteries such as Lead-acid (Pb-acid), Nickel-cadmium (Ni-Cd), Ni-MH, Sodium Nickel Chloride (Zero Emission Battery Research Activity-ZEBRA), Lithium –Ion (Li-Ion) Battery were evaluated in terms of different criterion. Among the alternatives the Li-ion battery type is chosen as the best option and the Ni-Cd battery is the least chosen alternative.

A NEW METHODOLOGY FOR TREATING PROBLEMS IN THE FIELD OF TRAFFIC SAFETY: CASE STUDY OF LIBYAN CITIES

Traffic safety is an area of great importance, since there are many traffic accidents every day in which a significant number of people are killed. Defining certain strategies and identifying potentially the most dangerous towns and cities regarding this area are, on the one hand, a necessity, and, on the other hand, a challenge. In this paper, integrated Multi-Criteria Decision-Making (MCDM) model for ranking cities in Libya from the aspect of traffic safety has been proposed. The model implies a set of 8 criteria on the basis of which 5 decision-makers rated the 10 most deprived cities in Libya. The Full Consistency Model (FUCOM) in combination with the rough Dombi aggregator is used to determine the significance of the criteria. The Rough Simple Additive Weighting (R-SAW) method is used to rank the alternatives. The rough Dombi aggregator is also used for averaging in group decision-making while evaluating the alternatives. The stability of the model and the obtained results has been verified by the sensitivity analysis, which implies a 2-phase procedure. In the 1st phase, rough Additive Ratio Assessment (R-ARAS), Rough Weighted Aggregated Sum Product Assessment (R-WASPAS), Rough Complex Proportional Assessment (R-COPRAS) and Rough Multi-Attributive Border Approximation-area Comparison (R-MABAC) methods are applied. The 2nd phase implies changing the parameter ρ in the procedure of rough Dombi aggregator, while the 3rd phase includes the calculation of Spearman’s Correlation Coefficient (SCC) that shows a high correlation of ranks.

Rise of blockchain technology in supply chain transformation in the post-pandemic era

Purpose Though academic study on the subject is still in its early stages, there is growing interest in using blockchain technology for transforming the supply chain. The academic literature is divided and yet only includes studies evaluating how the supply chain has changed organizations. To comprehend the new phenomena, this study aims to investigate the factors of blockchain technology in driving supply chain transformation. To be more precise, the authors developed from the literature the most prevalent criteria for determining if supply chain transformations are ready to be scaled up. Design/methodology/approach This study followed a combination of two multi-criteria decision making methods evaluation based on distance from average solution and complex proportional assessment) methodology in this research: planning, investigating, executing out, establishing a rating of the criteria and evaluating it. Findings The study shows that the “organizational driver” and the “technology driver” are the factors most important to the transformation of the supply chain, whereas the “financial driver” and the “regulatory driver” are less important. This study also makes some managerial recommendations to address the factors impeding the supply chain’s transformation. Each factor’s significance was explored, and a proposed study agenda was also presented. Research limitations/implications Although the main forces behind the transformation of the supply chain have been recognized, further research into statistical correlation is required to confirm how the various elements interact. Practical implications This research aids decision-makers in comprehending the key forces behind supply chain transformation. Managers and decision-makers might better predict and allocate the necessary resources to start the road toward digitization and make well-informed choices once these aspects have been investigated and understood. Originality/value In light of the pandemic’s effects on the world and the increase in businesses embracing the digital economy, the supply chain transformation is more important than ever. Beyond blockchain deployment and the pilot studies on digital transformation, there is a gap. The topics and factors this study uncovered will operate as a framework and recommendations for more theoretical investigation and practical applications.

Uncertainty modelling in the selection of climate control system manufacturers for preconstruction of hydroelectric power plants

Heating, ventilation, air conditioning, and refrigeration systems, the critical intersection point of low environmental impact, neutral thermal comfort and acceptable indoor air quality are technologies that directly affect the electricity generation of hydroelectric power plants on the axis of sustainability. Addressing challenges in decision-making for pre-construction system selection, this study employs fuzzy set theory, specifically Pythagorean and q-Rung Orthopair fuzzy sets, to model uncertainty in hydroelectric power plant specific considerations. Utilizing Analytic Hierarchy Process, Technique for Order of Preference by Similarity to Ideal Solution and the Complex Proportional Assessment the model evaluates ten global climate control system manufacturers. Sensitivity and comparative analysis ensure result validity, offering valuable insights for industry professionals and decision-makers, streamlining the complex process of selecting the most suitable manufacturer.

Parametric optimization of an impingement jet solar air heater for active green heating in buildings using hybrid CRITIC-COPRAS approach

This work aimed to optimize the parameters of discrete multi-arc shaped ribs (DMASRs) in a solar air heating system (SAHS) through multi-criteria decision-making techniques. In the experiment, the roughness parameters of DMASRs were varied to find the best parameter combination for optimal SAHS performance. The relative rib height (Hr/H) was varied from 0.025 to 0.047 , and the relative rib pitch (Pr/H) was varied from 0.58 to 3.1. The results obtained for the Nusselt number and friction factor, which determine the performance of the SAHS system, depend on the geometrical parameters of the roughness. The parameters of DMASRs did not show any discernible trend. Hence, a multi-decision criteria approach that uses criteria importance through inter-criteria correlation (CRITIC) and complex proportional assessment (COPRAS) hybrid techniques was employed to determine the best parameter combination for optimal performance. The novel aspect of this study includes the use of a hybrid method (experimental and analytical) to optimize the performance of SAHS roughened with DMASRs hindrance promoters and predictions of outcomes using a hybrid CRITIC-COPRAS approach. The experimental and analytical examination through the use of the hybrid CRITIC-COPRAS approach is an essential component of this research that contributes to the optimization of the design parameters of such SAHS. The finding demonstrated that when Re = 19000, Pr/H = 1.7, and Hr/H = 0.047 were reached, the SAHS obtained an optimal thermohydraulic performance of 4.1.

A Probabilistic Hesitant Fuzzy MCDM Approach to Selecting Treatment Policy For COVID-19

The global significant rise in the number of sick individuals and fatalities has made the ongoing struggle against the severe and lethal COVID-19 pandemic a global effort. There are several ongoing therapies for COVID-19, and more are being developed. However, selecting the best therapy option for COVID-19 patients is still needed. Patients may easily choose from the available COVID-19 therapies using the multi-criteria decision-making method. As a result, the present study provides an MCDM method that is created to determine COVID-19 therapies. Probabilistic Hesitant Fuzzy Set numbers, values, and ambiguity are introduced. Theorems and characteristics of PHFS numbers are also investigated in depth. The Complex Proportional Assessment technique is used, based on the PHFS, for dealing with ambiguity issues. This study uses ten criteria and three treatment methods: antibacterial medication and plasma treatment, vaccinations, as well as quarantine and in-house isolation. The study results reveal that quarantine and isolation at home mark the most effective treatment, followed by vaccinations with antibiotics and plasma therapy.

Multi-Criteria Decision-Making Technique for Optimal Material Selection of AA7075/SiC Composite Foam using COPRAS Technique

Aluminium foams have been manufactured and discovered to have a variety of uses in automotive and structural applications. However, due to their varied characteristics, it is difficult to choose an appropriate material. In this context, the selection of material for good properties is a challenging task. This study attempted to identify materials from various combinations employing the Multiple Attribute Decision Making (MADM) technique based on their mechanical and physical properties. Complex Proportional Assessment (COPRAS) is a Multi-Criteria Decision Making (MCDM) technique employed for evaluating the ranking order of the aluminium composite foam’s formulations based on performance measures. The composite foam with 2.5 wt.% of Calcium carbonate as foaming agent demonstrated the best combination of mechanical properties.

Framework for Lean Implementation Through Fuzzy AHP-COPRAS Integrated Approach

Manufacturing industries strive hard to improve their productivity to meet the highly demanding business market. Productivity can be improved by effective use of resources, elimination of wastes, process flow enhancement, and continuous improvement. There are strategies and tools to improve productivity but industries face the problem of selecting appropriate strategies. Lean manufacturing philosophy helps the manufacturing industries to enhance their productivity aspects by identifying and eliminating the wastes using specific tools/techniques. This article presents an integrated framework for the lean implementation process in the steel processing industry. The value stream mapping (VSM) tool is used to identify the wastes at each stage of the production process. Lean tools are identified from literature based on the wastes identified from the current state. For prioritizing the lean tools based on the positive correlation on one another, fuzzy integrated analytic hierarchy process and complex proportional assessment of alternatives are used. A future state value stream map is developed to illustrate the improvements achieved due to the implementation of selected lean tools. Comparison of present and future state VSM shows a great reduction of wastes, such as scale waste (50%), miss roll defects (72.7%), size variation defect (62%), corrosion defect, and improvements in talk time and cleanliness.

A novel fuzzy SECA model based on fuzzy standard deviation and correlation coefficients for resilient-sustainable supplier selection

Selecting the best out of a pool of potential suppliers is one of the most significant challenges in supply chain management. Although numerous works have studied supplier selection (SS) as a function of individual criterion (e.g., sustainability or resiliency), few studies have explored it as an all-in-one. To address this gap, this study proposes a novel fuzzy multi-criteria decision-making (MCDM) framework for a real-world color and glaze case study. First, resilient-sustainable criteria are identified for evaluating the performance of the suppliers. Expert opinions and literature review are then utilized to establish a set of criteria in two different categories: sustainability and resiliency. This two-pronged approach ensured that important criteria are considered in the evaluation process. Next, a new fuzzy simultaneous evaluation of criteria and alternatives (FSECA) model is developed to highlight the most crucial criteria and the best supplier. The proposed model utilizes a predetermined set of criteria reference weights as benchmarks to calculate the final weights that closely align with these reference points. Moreover, the consideration of fuzzy standard deviation and correlation coefficients of fuzzy numbers are incorporated to take into account the inherent variability and uncertainty in the data, resulting in a more accurate and robust analysis of the data. To validate the proposed method, the findings are compared to the state-of-the-art fuzzy approaches for SECA method, and other MCDM techniques such as fuzzy weighted aggregated sum product assessment (FWASPAS), fuzzy technique for order performance by similarity to ideal solution (FTOPSIS), fuzzy Multi-Objective Optimization on the basis of Ratio Analysis (FMOORA), fuzzy simple additive weighting (FSAW), and fuzzy complex proportional assessment (FCOPRAS). Moreover, by applying the geometrical analysis for interactive aid (GAIA), a visualization approach, the results demonstrate that the performance of the proposed framework is satisfactory and highlight the most crucial criteria, such as the supplier responsiveness in resiliency and delivery in economic impact of sustainability categories. Since choosing supplier is a strategic decision, the proposed work can help managers to choose the right suppliers.

An integrated hybrid MCDM approach to evaluate countries’ COVID-19 risks

To enable planning and early reaction efforts, it is important to identify the nations most at risk from COVID-19's health and humanitarian effects, which could exceed present national response capabilities. INFORM COVID-19 Risk Index developed by European Commission is one of the initiatives to meet the aforementioned need. However, the Index has 40 conflicting and comprehensive indicators (e.g., health system capacity, hygiene, vulnerability) and different countries that lead to a need for multicriteria decision-making (MCDM) approaches. This paper offers an integrated MCDM approach with two phases to re-assess the countries' COVID-19 risks based on INFORM COVID-19 Risk Index. In the first phase, the 40 indicators are prioritized using the Entropy-based multi-choice best-worst method (MC-BWM) to provide multiple viewpoints for the preferences values. In the second phase, 29 countries from different geographical regions are ranked using the COPRAS (Complex Proportional Assessment) method. Applying these three methods together on the COVID-19 risk assessment of countries is the most significant novelty for the relevant literature. The proposed approach has undergone a thorough comparative evaluation with the released INFORM COVID-19 Risk Index, and recommendations are provided based on different scenario analyses.

MULTI-CRITERIA DECISION ANALYSIS USING COMPLEX PROPORTIONAL ASSESSMENTS AND RANK ORDER CENTROID METHODS IN THE SELECTION SYSTEM FOR TUTORING INSTITUTIONS

Tutoring can help increase students' self-confidence, reduce anxiety about tests or assignments, and overcome learning barriers. The large number of tutoring institutions that offer various programs makes parents or students have to be observant when choosing them. To choose a tutoring institution, parents or students must know all the profiles and programs of the institution to be selected. This creates a difficult and long time to come up with a choice. The purpose of this study is to use the Multi Criteria Decision Analysis (MCDA) approach through the Complex Proportional Assessment (COPRAS) method and the Rank Order Centroid (ROC) method to create a Decision Support System (DSS) that will make it easier to choose a tutoring institution. The ROC approach serves to determine the weight based on the order of importance of the criteria. The COPRAS method is used because this method takes utility into account by assessing the usefulness of each alternative. This research produced a web-based tutoring institution selection DSS that can provide alternative recommendations based on criteria determined by decision-makers. The results of system calculations and manual calculations do not show a different value, which shows that the system produces a valid COPRAS approach value. Based on the results of usability testing, the built DSS scored 89.17%; in other words, the system is feasible to use.

Penerapan Metode Copras dalam Pemilihan Kepala Pengendalian Mutu (Head Of Quality Control)

In the selection of the current Head of Quality Control is appointed manually based solely on the information provided in the application and the decisions that have been raised are inaccurate and cause delays causing the assessment not to be completed. In addition, when recruiting the Head of Quality Control manually, there are several things that can cause problems, such as hiring a Head of Quality Control who does not meet the criteria set by the company, carrying out a slow selection process, and others. The methodology used is the Complex Proportional Assessment (COPRAS) method, which is an approach used to collect data and develop systems and applications to assist companies in making decisions based on common problems. The calculation results with COPRAS were obtained from 10 selected employees, the highest UI score of 100 and the lowest of 52.18. Companies can find qualified managers who meet organizational standards by implementing a decision support system based on this COPRAS methodology.

Site suitability analysis using MacBeth and COPRAS in a case study of the cement industry under SCM implementation

In the past two years, 2020-2022, the developing construction industry has been a huge issue according to the negative effect of Covid-19 with the increasing pandemic situation in cities and areas. In Covid-19 pandemic situation, the cement manufacturing industry has been crucial and needed more scrutiny. As cement is the second significant component after water in concrete and construction industry. Meanwhile, locating a cement plant in a special area of the city is challenging and affecting more by local communities and other involved environmental factors. The location selection decisions need to grow by environmental, economic, technical and social attributes. This study aims to present the site suitability decisions through a case study of locating a new manufacturing plant for cement production in Tehran surrounding, Iran. In this process, some required technical and tactical criteria are deserved for evaluating and suitability of the plant through decision-makers for cement manufacturing. All the feasible industrial alternative locations were evaluated under various criteria and regarding the Covid-19 pandemic’s negative impact to identify the most appropriate location for the cement industry. The authors proposed two Multi-Criteria Decision Attributes (MCDA) methods of MacBeth and COmplex PRoportional ASsessment (COPRAS) to evaluate and select the most suitable location for site suitability of the cement plant in this problem. Though the MacBeth method does not need to calculate weights of the Geographical Information System (GIS) criteria, the COPRAS method determined and used BWM (Best-Worst Method) as the weighing method. In sum, the comparison of the two methods was obtained according to the given results and ranks of volunteer cement suppliers for site suitability of the cement plant.

Enhanced structural integrity of Laser Powder Bed Fusion based AlSi10Mg parts by attaining defect free melt pool formations

This research aims to fabricate an AlSi10Mg parts using Laser Powder Bed Fusion technique with enhanced structural integrity. The prime novelty of this research work is eliminating the balling and sparring effects, keyhole and cavity formation by attaining effective melt pool formation. Modelling of the Laser Powder Bed Fusion process parameters such as Laser power, scanning speed, layer thickness and hatch spacing is carried out through Complex Proportional Assessment technique to optimize the parts' surface attributes and to overcome the defects based on the output responses such as surface roughness on scanning and building side, hardness and porosity. The laser power of 350 W, layer thickness of 30 µm, scan speed of 1133 mm/s, and hatch spacing of 0.1 mm produces significantly desirable results to achieve maximum hardness and minimum surface roughness and holding the porosity of < 1%. The obtained optimal setting from this research improves the structural integrity of the printed AlSi10Mg parts.

Transition Supply Chain 4.0 to Supply Chain 5.0: Innovations of Industry 5.0 Technologies Toward Smart Supply Chain Partners

&#x0D; Industry 4.0 provides businesses with the tools they need to meet difficulties such as fluctuating demand and unstable markets. Additionally, Industry 4.0 refers to the connectivity of computers, various materials, and artificial intelligence (AI) with minimum involvement from humans in the decision-making process. Although Industry 4.0 has a significant potential for the expansion of the industrial sector, it faces several hurdles, including integration of technology, problems with human resources, problems with supply chains, and data security concerns. The human-centered approach that Industry 5.0 took meant that many of the problems that plagued Industry 4.0 could finally be solved. In the previous generation, known as Industry 4.0, the emphasis was placed on scalability and volume of production; however, in the next generation, known as Industry 5.0, human centricity is the key focus. We have included a list of the different technical improvements that are part of Industry 5.0 as well as the technological advancements that are part of Industry 4.0. The problems that plagued Industry 4.0 have been addressed head-on in Industry 5.0, including concerns over data protection and the integration of new technologies. This study would serve as a foundation for academics and companies to learn about the technologies of Industry 4.0, their obstacles, the technical advancements, and the methods by which Industry 5.0 addressed the issues of Industry 4.0. Also, we constructed an appraiser model to appraise manufacturers as partners in the supply chain. We selected manufacturers that are interested in deploying Industry 5.0 in their operations. Analytic Hierarchy Process (AHP) and Complex Proportional Assessment (COPRAS) are contributed to construct an appraiser model under the authority of uncertainty theory entailed in single value neutrosophic sets (SVNSs) to support AHP and COPRAS in ambiguity situations.&#x0D;

Decision Making for the Best Hospital Evaluation Using the AHP-COPRAS Method

The assessment of the Hospital is very important in providing a rating as a measure of accreditation. Hospitals should provide the best service to visiting patients. The purpose of this study is to provide a ranking of hospitals that should be a measure of the appropriateness of a hospital as seen from a number of criteria as a true assessment. The criteria used as a barometer for the assessment consist of ten criteria, namely Administrative Services, Doctor Services, Pharmaceutical Installation Services, Cleanliness, Convenience, Security, Number of Administrative Personnel, Number of Pharmaceutical Personnel, Administrative Complaints, and Pharmacy Installation Complaints. The scoring system that will be applied uses the Analytical Hierarchy Process (AHP) method as a determinant of the weight of each criterion and the Fuzzy assessment uses the Complex Proportional Assessment (COPRAS) method. The two methods will be collaborated as a determinant in the process of giving a rating system to the ten hospitals that are included in the priority assessment. The results obtained from the ranking process of the two AHP and COPRAS methods are seen from the acquisition of the utility value produced, the largest value obtained from the utility gives the best rating. The utility value is obtained from the total assessment of a number of criteria for each hospital and the largest utility rating does not exceed one. The highest value of the utility generated by the RS10 alternative with a utility scale of 0.098 as the best value.

Optimizing abrasive wear loss of Aluminum/Fly Ash and Aluminum/Rice Husk Ash metal matrix composites using AHP-EDAS and AHP-COPRAS techniques for enhanced performance

Lightweight aluminum metal matrix composites (MMCs), Aluminum/Fly Ash and Aluminum/Rice Husk Ash were developed using the stir casting route for automotive applications. As a result of the above consideration, automotive parts such as brake rotor and connecting rod, etc. have been the subject of research in a variety of areas. Finding the right material for the design under consideration requires an effective approach to material selection. Multi-criteria decision-making (MCDM) processes are helpful when dealing with ambiguity in material selection. To rank aluminum-Fly Ash (FA) and Aluminum-Rice Husk Ash (RHA) composites, integrated MCDM methodologies such as Analytical Hierarchy Process (AHP)–Evaluation Based on Distance from Average Solution (EDAS) and AHP–Complex Proportional Assessment (COPRAS) are utilized. The weights for each criterion are determined using the AHP approach, and the EDAS and COPRAS techniques are then used to rank the items. Three different particle sizes (0–53, 54–74, and 75–105 µm) of the reinforcement have been chosen for the selection criteria. Density, hardness, ultimate tensile strength, and compressive strength, and specific wear rate, and coefficient of friction of the manufactured composites evaluated. A thorough examination of the MCDM methods found that Al-FA composites with particle sizes of 54–74 µm were the most effective.

Investigation on 1-Propanol Electronic mode of fumigation on diesel engine performance and emission Fueled with diesel and lemongrass biodiesel blend using AHP- COPRAS

The current experimental work investigated the engine performance and emissions when a compression ignition engine was powered by 1-propanol Electronic Mode (EM) fumigation with biodiesel and diesel fuel.The biodiesel (20 %) was blended with the reference diesel (80 %). The reference diesel and biodiesel blend values were notified to compare different timings of 1-propanol EM fumigation. 1 ms, 3 ms, and 5 ms injected fuel into the inlet manifold with varying loads. The test engine was powered by the mentioned biodiesel mixture with different timing of 1-propanol EM fumigation and reported the engine operating parameters. The test engine was subjected to additional brake power and fumigation timings. Initially, the experiments were conducted at extra engine brake power to identify the engine performance and emission characteristics, and later on, this investigation deals with the use of multi-criteria decision-making (MCDM) based Analytic Hierarchy Process (AHP) and Complex proportional assessment method (COPRAS) to identify the weights and Rank of the various tested blends. The experiment results were compared to the effects of reference diesel and biodiesel. The 5 ms 1-propanol EM fumigation produced good results in all engine parameters. The 3 ms EM fumigation produced intermediate results in all engine parameters.In comparison to 1-propanol EM fumigation timings, 5 ms produced maximum efficiency in peak load conditions and produced low carbon dioxide (CO2), nitric oxide (NO), and smoke at all load conditions.