Multi-criteria Decision-making Issue Research Articles

Evaluating Enterprise Resource Planning Adoption Using the EDAS Method: A Strategic Approach

Adoption of enterprise resource planning (ERP) systems is the process of putting ERP systems into use inside an organisation. It entails the fusion of diverse corporate operations into a centralised software platform, including finance, human resources, and supply chain management. By enabling real-time utilisation of integrated data and automated processes, ERP adoption attempts to improve decision-making, increase efficiency, and streamline operations. To achieve successful installation and user acceptance, rigorous planning, arrangement, and instruction are needed. For businesses looking to streamline operations and obtain a competitive edge in today's digital environment, ERP adoption is essential. The implementation of enterprise resource planning (ERP) is significant for research since it can help to better understand and manage organisational operations. Scientists can learn more about the elements that contribute to successful implementation, pinpoint best practises, and address issues that organisations have throughout the adoption process by researching ERP adoption. By optimising resource allocation, increasing operational efficiency, and implementing ERP, this research will assist organisations in making knowledgeable judgements. Additionally, research on ERP adoption aids in the creation of frameworks and theories in the discipline of information technology and offers advice to practitioners and decision-makers working on ERP projects. A technique for making decisions that takes into account an alternative's distance from the mean answer is called evaluation based on distance from the average solution (EDAS). It gauges the performance of the alternatives using the distance between them and the typical solution. When evaluating options, EDAS takes both positive and negative distances into account. To rank and choose the best alternative in multi-criteria decision-making issues, this method is frequently utilised. Decision-makers can use EDAS to make well-informed decisions by taking into account how far away various options are from the typical solution. The Shipping took the first place while Tracking took the Last place in the Enterprise resource planning adoption. In this paper Enterprise resource planning adoption, The Shipping took the first place while Tracking took the Last plac

Analysis of multi-criteria recommendation system based on fuzzy algorithm

There is a gap in defining the multi-criteria decision-making issues and with recommendation techniques and theories that can help develop the modulation coefficient recommenders. The main objective of this research is to identify an in-depth examination of the category of multiple variables recommendation systems. The methodology that is used in the current study is fuzzy multi-critical decision-making to enhance the precision and appropriateness of the recommendations provided to users, and make recommendations by representing an individual's performance for the product as an ordered collection of rankings in addition to different parameters. The techniques used to make forecasts and produce recommendations using multi-criteria rankings are reviewed. In addition, we propose the multiple-criteria ranking algorithms. Experimental evaluations demonstrated that our proposed algorithms can solve the multi-criteria issues. Furthermore, the research considers unresolved problems and upcoming difficulties for the category of recommendations for multiple variables ratings.

Application of SAW method for finding best input factors for CBN grinding Al6106 T6

The outcomes of a multi-criteria decision-making (MCDM) study in CBN grinding Al6061 T6 to determine the optimal input process parameters to achieve both low surface roughness (Ra) and maximum material removal speed (MRS) are described in this study. The MCDM problem was addressed using the simple additive weighting (SAW) method, and the weights of the criterion were computed using the MEREC (Method based on the Removal Effects of criterion) technique. In addition, four process input factors were investigated: wheel speed (WS), feed rate (Fe), depth of cut (aed), and down feed (Df). The experiment was designed with the 2-level 1/2 fraction type in mind. The experimental output outcomes, including the SR and MRS, were measured and entered into the MCDM issue. Based on the study's findings, the ideal input parameters for CBN grinding Al6061 T6 were identified.

Using discrete-event simulation to assess an AHP-based dynamic patient prioritisation policy for elective surgery

ABSTRACTPatient prioritisation in elective surgery is a critical multi-criteria decision-making issue. Elective patients should be ranked on the waiting list based on various criteria related to their health conditions and their evolution over time. This study suggests a dynamic prioritisation where elective patients are ranked considering their evolving health conditions, waiting time, and patient loss, i.e., the likelihood of patients abandoning the waiting list. We proposed deploying the Analytic Hierarchy Process (AHP) to weigh prioritisation criteria. The AHP-derived weights are integrated into a discrete-event simulation model, which periodically ranks patients considering their health status, waiting time, and patient loss. The proposed dynamic prioritisation is compared with the first-come, first-served (FCFS) policy and the static prioritisation, which ranks patients according to their initial priority. Results showed that dynamic prioritisation outperforms the FCFS and static prioritisation policies in preventing health deterioration, reducing weighted waiting time, and reducing the ratio of lost patients.

Heterogeneity issues in IoT-driven devices and services

<p>Internet of Things (IoT), which connects billions of devices and services to the Internet, is viewed as the future industrial and intellectual revolution in technology. These connected devices are available in a variety of types. Different technologies and standards use various protocols to interact with each other. Due to these difficulties with heterogeneity, the application of IoT on a broad scale is difficult. This inspired us to identify the problems from the literature and offer solutions to solve the IoT scalability problem. This study is based on the systematic literature review (SLR) to identify the diverse problems and their solutions. We chose 81 primary sources in total. We found 14 distinct IoT heterogeneity concerns after extracting and interpreting the data. The following issues have been noted as potential obstacles: heterogeneity in data formats, heterogeneity of devices, heterogeneity in communication, and interoperability difficulty because of heterogeneity. From the perspectives of digital libraries and timeframes, the stated challenges have been addressed. Additionally, we have discovered 81 solutions in total for these problems, with at least 5 different answers for every issue. In the future, we will use a multi-criteria decision-making issue to classify the problems and evaluate the solutions.</p>

Selection of Car Models with a Classic and Alternative Drive to the Car-Sharing Services from the System’s Rare Users Perspective

Short-term, automated car rental services, i.e., car sharing, are a solution that has been improving in urban transportation systems over the past few years. Due to the intensive expansion of the systems, service providers face increasing challenges in their competitiveness. One of them is to meet the customer expectations for the fleet of vehicles offered in the system. Although this aspect is noted primarily in the literature review on fleet optimization and management, there is a gap in research on the appropriate selection of vehicle models. In response, the article aimed to identify the vehicles best suited for car-sharing systems from the customer’s point of view. The selection of suitable vehicles was treated as a multi-criteria decision-making issue; therefore, the study used ELECTRE III—one of the multi-criteria decision-making methods. The work focuses on researching the opinions of users who rarely use car-sharing services in Poland. The most popular car models in 2021, equipped with internal combustion, hybrid, and electric engines, were selected for the analysis. The results indicate that the best suited cars are relatively large, spacious, and equipped with electric drive and represent the D segment of vehicles in Europe. In addition, these vehicles are to be equipped with a powerful engine, a spacious boot, and a fast battery charging time. Interestingly, small city cars, so far associated with car sharing, ranked the worst in the classification method. In addition, factors such as the warranty period associated with the quality of the vehicles, or the number of car doors, are not very important to users. The results support car-sharing operators in the process of selecting or modernizing a fleet of vehicles.

Smart mining policy: Integrating fuzzy-VIKOR technique and the Z-number concept to implement industry 4.0 strategies in mining engineering

New technologies have significant effects on increasing efficiency and improving the productivity of industries, especially the mining industry. Nevertheless, selecting and applying new technologies is a multi-criteria decision-making issue. As mines constitute an important part of a country's economy with a considerable impact on socio-economic development, mining smartening should be carried out to increase efficiency. This research proposes a novel framework for assessing and prioritizing smart mining strategies by integrating the Z-number theory and fuzzy-VIKOR technique. Eight copies of a questionnaire containing five strategies (alternatives) and 11 criteria were presented to an eight-member team of experts. The strategies comprised “A1 provision of government incentives to mines to use up-to-date technologies”, “A2 development of mining cooperation with research centers and R&D to develop new technologies required by mines”, “A3 investment of mine owners in providing the required infrastructure”, “A4 development of green mining and safe operations”, and “A5 training and improving active human resources”. Moreover, the criteria consisted of “C1 employment”, “C2 energy consumption”, “C3 hazards”, “C4 trained human resources”, “C5 marketing and sales”, “C6 environment”, “C7 risk”, “C8 culture”, “C9 technology”, “C10 quality of mineral products”, and “C11 legislations”. Furthermore, a parametric sensitivity analysis was performed to determine the effectiveness and robustness of the five suggested strategies. The results of the proposed hybrid model confirm that the third strategy (A3) is the most appropriate alternative for implementing Industry 4.0 technologies in the large-scale mines of Iran. The results revealed that the presented approach provided a stationary ranking mechanism for smart mining engineering strategies and that the weight of the maximum group utility measure did not substantially affect ranking strategies. An expert group confirmed the obtained results and the evaluation and validation made of the influential factors and conditions of the study area.

A single-valued neutrosophic decision framework for the assessment of sustainable transport investment projects based on discrimination measure

PurposeThe study aims at evaluating the most appropriate transport project which is one of the critical concerns of transport infrastructure scheduling. This process will be applied considering a set of criteria and discussed alternatives with sustainable perspectives.Design/methodology/approachIn this paper, a complex proportional assessment (COPRAS) framework is discussed to handle the sustainable transport investment project (STIP) assessment problem within a single-valued neutrosophic set (SVNSs). To form the procedure more useful in handling with uncertain features, a SVNS is applied as a valuable procedure to handle uncertainty. First, a new discrimination measure for SVNSs is introduced and discussed some elegant properties to determine the significance degree or weight values of criteria with the sustainabality perspectives. Second, an integrated approach is introduced based on the discrimination measure and the COPRAS method on SVNSs and named as SVN-COPRAS.FindingsA case study of an STIP evaluation problem is used to confirm the practicality and effectiveness of the SVN-COPRAS framework. Lastly, comparative discussion and sensitivity investigation are illustrated to prove the strength and solidity of the proposed framework.Originality/valueThe SVNSs enrich the essence of linguistic information when a decision expert (DE) vacillates among different linguistic values (LVs) to measure a sustainable transport project alternative problem. The utilization of SVNSs provides a more stable procedure to describe DEs' evaluations. So, an elegant methodology is developed to incorporate the DEs' awareness and experience for electing the desired STIPs. The introduced methodology has higher operability than the single-valued neutrosophic set technique for order preference by similarity to an ideal solution (SVN-TOPSIS) procedure during the larger numbers of attribute(s) or option(s). For the SVN-COPRAS methodology, there is no need to estimate the single-valued neutrosophic ideal solution (SVN-IS) and single-valued neutrosophic anti-ideal solution (SVNA-IS). The outcomes are calculated with handling the realistic data, which elucidates that the introduced model can tackle more intricate and realistic multi-criteria decision-making issues.

Pythagorean fuzzy prioritized aggregation operators with priority degrees for multi-criteria decision-making

PurposeThe authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.Design/methodology/approachIn real-world situations, Pythagorean fuzzy numbers are exceptionally useful for representing ambiguous data. The authors look at multi-criteria decision-making issues in which the parameters have a prioritization relationship. The idea of a priority degree is introduced. The aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees.FindingsThe authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined. Further, based on these operators, a decision-making approach is presented under the Pythagorean fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to demonstrate the efficiency of the proposed approach.Originality/valueThe aggregation operators are formed by awarding non-negative real numbers known as priority degrees among strict priority levels. Consequently, the authors develop some prioritized operators named Pythagorean fuzzy prioritized averaging operator with priority degrees and Pythagorean fuzzy prioritized geometric operator with priority degrees. The properties of the existing method are routinely compared to those of other current approaches, emphasizing the superiority of the presented work over currently used methods. Furthermore, the impact of priority degrees on the aggregate outcome is thoroughly examined.

Economy-environment-energy performance evaluation of CCHP microgrid system: A hybrid multi-criteria decision-making method

The combined cooling, heating and power micro-grid (CCHP-MG) system has significant economy-environment-energy (3E) characteristics, making it valuable to evaluating its 3E performance in promoting the 3E coordinated development. Based on the energy supply structure of CCHP-MG system, this paper constructs a 3E performance evaluation index system for the CCHP-MG system from economy, environment and energy dimensions. Then, a hybrid multi-criteria decision-making (MCDM) framework is designed, involving integrated weighting method based on anti-entropy weight method and grey decision-making trial and evaluation laboratory (grey-DEMATEL), and improved technique for order preference by similarity to an ideal solution (TOPSIS) with difference and quotient grey relation analysis (DQGRA). 8 building-typed CCHP-MG systems are taken as examples for analysis. The results show that the benefits of system energy supply, equivalent emission reduction of pollutants, energy utilization and equivalent energy saving are important manifestations of CCHP-MG's 3E performance, and raising energy supply scale and renewable energy proportion are the key ways to improve CCHP-MG's 3E performance. Comparing with 4 other MCDM models, the proposed hybrid MCDM model has good applicability and effectiveness for the 3E performance evaluation of CCHP-MG system, but it is necessary to carry out targeted adjustment when applying it to other MCDM issues.

Use of (R, S)-Norm concept and TOPSIS approach under picture fuzzy environment for application in multi criteria decision making issues

The picture fuzzy sets (PFSs) are more appropriate in handling the vague, imprecise and uncertain information measure for the MCDM (Multi criteria decision making) issues and therefore, this assertion is designed to introduce new information measure under the PFSs known as (R, S)-norm picture fuzzy information measure. In the present research work, a set of conditions are introduced as a basis for the new introduced information measure under the picture fuzzy environment and besides this, the practical efficacy of introduced (R, S)-norm picture fuzzy information measure along with certain significant properties are taken into practice. Under the application section of introduced work, the new information measure is utilized for electing the chief minister of Himachal Pradesh, State of India based on opinion polls.

Using Analytical Hierarchy Process (AHP) to Introduce Weights to Social Life Cycle Assessment of Mobility Services

Decisions in social sustainability assessment of mobility services often pose a multi-criteria decision-making issue, as trade-offs can occur between multiple alternatives and a participatory process should be used. Thus, the goal of this research is to support decision-making through determining weightings for different criteria and indicators for social sustainability assessment of mobility services. For the weighting process, the analytical hierarchy process (AHP) method was used. A participatory analysis was chosen to identify those weightings. In total, 48 experts in the field of sustainable urban mobility were questioned from three different groups: academic institutions, city authorities, and mobility service providers in order to analyze differences and similarities between these groups. While some indicators resulted in clear prioritization, other indicators revealed large differences between the expert groups. For the majority of indicators, the weighting results were similar for all three groups of experts. Consequently, the results provide clear guidance for decision-makers in the field of sustainable urban mobility.

Method for lake eutrophication levels evaluation: TOPSIS-MCS

Monte Carlo simulation (MCS) is applied in the engineering with great fuzziness and uncertainty. Technique for order preference by similarity to an ideal solution (TOPSIS) method is used to deal with multi-criteria decision-making issue. Membership function is used to determine the membership degree of evaluated index. This paper presents the method for lake eutrophication level evaluation. The developed approach merges MCS method, TOPSIS method and membership function. The evaluated results are consistent with real eutrophication level in Lake Erhai, China. Global sensitivity analysis (GSA) is conducted. Results show that potassium permanganate index (CODMn) displays the highest negative correlation with the evaluated results and Secchi disc (SD) performs the highest positive correlation under different errors in measured data. The novelty of this work are: (1) the application of TOPSIS considers Surface water environmental quality standards and measured data. Besides, the Monte Carlo simulation method is applied to generate a normal distributed dataset to overcome the errors caused by human and equipment in data collection. The approach is utilized in the article, titled “Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels” (Lin et al., 2020) [1].•Developed approach merges TOPSIS and MCS method.•It can increase the reliability of evaluated result.

Using the Analytic Hierarchy Process (AHP) method for selection of phase change materials for solar energy storage applications

Selection of a suitable phase change material (PCM) for thermal energy storage systems is important. There are an enormous number of available PCM and hence choosing a particular PCM for a certain application is difficult. Many researchers rely on their expertise or the material available to use PCM for a specific application. The purpose of the selection of a certain PCM is to choose the best material from the commercially available PCM for a given application. An effective evaluation approach is essential to enhance the quality of the decisions. The issue of choosing a material for different applications can be managed as a multi-criteria decision-making issue. In this paper, the Analytic Hierarchy Process (AHP) method is used to select between five PCM for solar heating systems considering both the technical specification and the criteria of the materials. The selected materials have a melting temperature in the required working temperature range. Seven criteria are used in this study and these include the latent heat of fusion, thermal conductivity of the material, specific heat, density, thermal stability, cost, and corrosion. The results show that the paraffin is the best material from the selected PCM using the AHP method for this solar heating system using all known thermo-physical properties and the weights of these criteria to achieve the objective.

Extensions of Dombi Aggregation Operators for Decision Making under m-Polar Fuzzy Information

An m-polar fuzzy set is a powerful mathematical model to analyze multipolar, multiattribute, and multi-index data. The m-polar fuzzy sets have appeared as a useful tool to portray uncertainty in multiattribute decision making. The purpose of this article is to analyze the aggregation operators under the m-polar fuzzy environment with the help of Dombi norm operations. In this article, we develop some averaging and geometric aggregation operators using Dombi t-norm and t-conorm to handle uncertainty in m-polar fuzzy (mF, henceforth) information, which are mF Dombi weighted averaging (mFDWA) operator, mF Dombi ordered weighted averaging (mFDOWA) operator, mF Dombi hybrid averaging (mFDHA) operator, mF Dombi weighted geometric (mFDWG) operator, mF Dombi weighted ordered geometric operator, and mF Dombi hybrid geometric (mFDHG) operator. We investigate properties, namely, idempotency, monotonicity, and boundedness, for the proposed operators. Moreover, we give an algorithm to solve multicriteria decision-making issues which involve mF information with mFDWA and mFDWG operators. To prove the validity and feasibility of the proposed model, we solve two numerical examples with our proposed models and give comparison with mF-ELECTRE-I approach (Akram et al. 2019) and mF Hamacher aggregation operators (Waseem et al. 2019). Finally, we check the effectiveness of the developed operators by a validity test.

Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market

Selecting an appropriate contract price between electric vehicle aggregators and electric vehicle owners is an uncertain, multi-criteria decision-making issue. In addition, the results can cause strong conflict due to different aims: the optimal value for increasing electric vehicle aggregator (EVA) profit negatively affects the cost for owners. The value of the contract price can change the optimal scheduling of EVAs in the day-ahead market. Taking into consideration this context, the current paper proposes to solve the multi-objective scheduling problem of an aggregator with a goal programming approach. The presented approach sets a satisfaction level for each goal according to decision-makers’ preference. Numerical results illustrate the validity of this approach to balance different performance measures. Furthermore, optimal scheduling of electric vehicle aggregators in the day-ahead market is created.

Sentiment-Enhanced Content-Based System for Online Recommendations and Rating Prediction

The scarcity of dependable product descriptions and limited emotion unmasking capabilities of user-ratings compromise the accuracy of content-based filtering (CBF) systems. This work puts forward a sentiment-enhanced content-based recommender system (SEC-Rec). The model has four modules, namely key feature extraction module, feature sentiment analysis module, recommendation module, and rating prediction module. Key feature extraction module uses hybrid of RAKE and TextRank to uncover key product features. The authors propose a hybridized model HSVADER (Hybrid SVM and VADER) for feature sentiment evaluation. The recommendation module combines sentiment and similarity for robust product ranking strategy. The practical benefits of SEC-Rec are demonstrated using Amazon Camera dataset, and the results are compared to the state of the art. The rating prediction module uses key feature sentiment score to estimate the overall user-rating resolving the multi-criteria decision-making issue. The RMSE value obtained ascertains the effectiveness of the approach compared to recent models.

Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province, Iran

This study is aimed at producing an improved ranking method by coupling the technique for the order of preference by similarity to ideal solution (TOPSIS) and Mahalanobis distance (MD) to prioritize the districts of Golestan Province, northeast of Iran, with respect to the prevailed natural hazards. The main idea of this work is underpinned by introducing a method that is: (1) in accordance with holistic thinking by engaging different threatening natural hazards in order to rank different threatened targets and (2) harmonized by the probabilistic context of the natural hazards. Therefore, maximum entropy (MaxEnt), a well-known data mining model, was used to model the spatial pattern of flood inundation and landslide occurrence over the study area. The area under the receiver operating characteristic (AUROC) was used to assess the goodness of fit and prediction power of the used models. As a result, the MaxEnt model showed an outstanding predictive performance with the AUROC values of 0.889 and 0.903 for landslide and flood inundation modelling, respectively. Afterwards, the revised universal soil loss equation (RUSLE) was employed to model soil erosion. This model successfully estimated the average soil loss of the study area with a value of about 33 ton/ha/yr which is in the range reported by provincial natural resources organization (10-35 ton/ha/yr). Results revealed that highly susceptible areas to the landslide, flood inundation, and water erosion potentially account for about 11, 9.6, and 6.6% of the Golestan Province surface area. Lastly, the TOPSIS–MD, TOPSIS, and Simple Additive Weight (SAW) methods were used to prioritize the districts of the Golestan Province with respect to all three susceptibility maps. As a result, TOPSIS–MD was chosen as the well-performing ranking method for further environmental managerial actions due mainly to considering the strong correlations among the criteria. According to TOPSIS–MD results, Minoodasht, Ramian, and Gorgan districts were recognized as the most threatened districts, while Gomishan, Aq Qala, and Gonbad-e Kavous districts are located at a safe zone with respect to the studied susceptibility indices. The proposed TOPSIS–MD framework merits more studies and is applicable to any multi-criteria decision-making issue in any branch of science.

A Hybrid Neutrosophic Group ANP-TOPSIS Framework for Supplier Selection Problems

One of the most significant competitive strategies for organizations is sustainable supply chain management (SSCM). The vital part in the administration of a sustainable supply chain is the sustainable supplier selection, which is a multi-criteria decision-making issue, including many conflicting criteria.

A Hybrid Neutrosophic Group ANP-TOPSIS Framework for Supplier Selection Problems

One of the most significant competitive strategies for organizations is sustainable supply chain management (SSCM). The vital part in the administration of a sustainable supply chain is the sustainable supplier selection, which is a multi-criteria decision-making issue, including many conflicting criteria. The valuation and selection of sustainable suppliers are difficult problems due to vague, inconsistent and imprecise knowledge of decision makers. In the literature on supply chain management for measuring green performance, the requirement for methodological analysis of how sustainable variables affect each other, and how to consider vague, imprecise and inconsistent knowledge, is still unresolved. This research provides an incorporated multi-criteria decision-making procedure for sustainable supplier selection problems (SSSPs). An integrated framework is presented via interval-valued neutrosophic sets to deal with vague, imprecise and inconsistent information that exists usually in real world. The analytic network process (ANP) is employed to calculate weights of selected criteria by considering their interdependencies. For ranking alternatives and avoiding additional comparisons of analytic network processes, the technique for order preference by similarity to ideal solution (TOPSIS) is used. The proposed framework is turned to account for analyzing and selecting the optimal supplier. An actual case study of a dairy company in Egypt is examined within the proposed framework. Comparison with other existing methods is implemented to confirm the effectiveness and efficiency of the proposed approach.