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The purpose of this paper is to consider compromise solutions of multiple attribute decision-making methods (TOPSIS, VIKOR, and similarity-based approach) in order to evaluate and rank mutual funds and to compare the capabilities of different approaches based on the different traditional indices of mutual funds assessment. In addition, a new algorithm for ranking mutual funds was proposed subsequently.

In this research, three groups of indices including general, risk-modified performance evaluation, and risk-modified performance evaluation indices using semivariance were used in the mutual funds assessment, which led to the comparison between selected mutual funds, using three mentioned methods and three different groups of criteria. The results of this comparison were compiled and synthesized with linear assignment method. At the end, an algorithm for decision making and investing in mutual funds for professional and unprofessional investors was proposed.

Using different methods and different criteria proved that the results of similarity-based approach as a MADM technique have the ability to rank and evaluate mutual funds regardless of the criteria used compared to TOPSIS and VIKOR. Furthermore, the authors propose the algorithm of this research as a new model of mutual funds evaluation which considers a wide range of variables with respect to amateur and professional points of view.

The originality of this paper is threefold: first, different criteria were considered to make the evaluation more comprehensive. Second, four different approaches were used to make the results more authentic. Third, a holistic algorithm with its implication was proposed.

The rapid intensification of the internet and electronic commerce diffusion has given rise to electronic business process management (e‐BPM) which enhances the overall connectivity of the business processes. However, when confronted by the range of e‐BPM best practices (e‐BPM^BPs), organizations struggle to identify the one most appropriate to their needs. The paper aims to address these issues.

The paper proposes a novel fuzzy group multi‐objective method for e‐BPM^BP evaluation and selection. First, a fuzzy group linear assignment method is used to rank the e‐BPM^BPs drawing on the four perspectives of a balanced scorecard (BSC). Second, a fuzzy group real options approach is used to estimate the financial values of the ranked e‐BPM^BPs. Third, a four‐objective assignment model is used to select the optimal e‐BPM^BP in deferral time with respect to their rankings, financial values, and a series of pertinent constraints.

The contribution of the proposed method is threefold: it is grounded in the four perspectives of a BSC, it considers imprecise or vague judgments which lead to ambiguity in the decision process, and it uses a meaningful and robust multi‐objective model to aggregate both qualitative judgments and quantitative data. A case study is presented to demonstrate the applicability of the proposed framework and to exhibit the efficacy of the procedures and algorithms.

The novel fuzzy group multi‐objective framework for e‐BPM^BP evaluation and selection proposed in the paper takes into consideration (1) the qualitative and quantitative criteria and their respective value judgments; (2) the verbal expressions and linguistic variables for qualitative judgments which lead to ambiguity in the decision process; and (3) imprecise or vague judgments.

This paper aims to propose an intuitive shared control strategy to control a humanoid manipulator that can fully combine the advantages of humans and machines to produce a stronger intelligent form.

The working space of an operator’s arm and that of a manipulator are matched, and a genetic algorithm that limits the position of the manipulator’s elbow joint is used to find the optimal solution. Then, the mapping of the operator’s action to that of manipulators is realized. The controls of the human and robot are integrated. First, the current action of the operator is input. Second, the target object is predicted according to the maximum entropy hypothesis. Third, the joint angle of the manipulator is interpolated based on time. Finally, the confidence and weight of the current moment are calculated.

The modified weight adjustment method is the optimal way to adjust the weight during the task. In terms of time and accuracy, the experimental results of single target obstacle avoidance grabbing and multi-target predictive grabbing show that the shared control mode can provide full play to the advantages of humans and robots to accomplish the target task faster and more accurately than the control merely by a human or robot on its own.

A flexible and highly anthropomorphic human–robot action mapping method is proposed, which provides operator decisions in the shared control process. The shared control between human and the robot is realized, and it enhances the rapidity and intelligence, paving a new way for a novel human–robot collaboration.

The purpose of this paper is to present a reliability centered maintenance (RCM) embedded integer linear programming approach (suited to the budget monetary resources allocation task) to the maintenance strategies mix selection for an industrial plant equipment.

The developed approach allows to determine the optimal maintenance strategies mix for a set of equipment in a more quantitative way than the classic RCM approach. The proposed model takes into account, for each potential failure determined using the FMECA and for each admissible strategy, the costs and the potential risk priority number (RPN) reduction. Finally, an industrial case concerning an Italian paper-mill plant is reported to demonstrate the effectiveness of the approach presented.

The paper finds that the application of the proposed approach allows to optimally allocate the budget monetary resources, determining which suitable maintenance practice apply to each failure, taking into account the costs of each strategy and the potential reduction of the RPN.

The proposed model permits to assign (during the budget monetary resources allocation task) to each failure the optimal strategy, among a set of suitable maintenance practices, considering the costs and the estimated RPN reduction.

The paper proposes a completely new RCM embedded approach to the maintenance strategies selection, in order to optimally allocate the budget monetary resources. This model overcomes the limits of the traditional RCM approach, taking into account quantitative aspects, i.e. the compatibility constraint between failures and policies, the maintenance strategies costs, and the RPN estimated reduction.

This paper proposes a new multi-criteria decision-making method, called the vital-immaterial-mediocre method (VIMM), to determine the weight of multiple conflicting and subjective criteria in a decision-making problem.

The novel method utilizes pairwise comparisons, vector-based procedures and a scoring approach to determine weights of criteria. The VIMM compares alternatives by the three crucial components, namely the vital, immaterial and mediocre criteria. The vital criterion has the largest effect on the final results, followed by the mediocre criterion and then the immaterial criterion, which is the least impactful on the prioritization of alternatives. VIMM is developed in two forms where the first scenario is designed to solve one-goal decision-making problems, while the second scenario embraces multiple goals.

To validate the method’s performance and applicability, VIMM is applied to a problem of sustainable supplier selection. Comparisons between VIMM, analytic hierarchy process (AHP) and best-worst method (BWM) reveal that VIMM significantly requires fewer comparisons. Moreover, VIMM works well with both fractional and integer numbers in its comparison procedures.

As an implication for research, we have added the development of the VIMM under fuzzy and grey environments as the direction for optimization of the method.

As managerial implications, VIMM not only provides less complex process for the evaluation of the criteria in the managerial decision-making process, but it also generates consistent results, which make VIMM a reliable tool to apply to a large number of potential decision-making problems.

As a novel subjective weighting method, there exist five major values that VIMM brings over AHP and BWM methods: VIMM requires fewer comparisons compared with AHP and BWM; it is not sensitive to the number of criteria; as a goal-oriented method, it exclusively takes the decision-making goals into account; it keeps the validity and reliability of the Decision-Makers’ (DMs’) opinions and works well with integer and fractional numbers.

The purpose of this study is to evaluate maintenance strategies based on fuzzy decision-making trial evaluation and laboratory (DEMATEL) and fuzzy analytic network process (ANP) in the petrochemical industry.

This study proposes a hybrid-structured multi-criteria decision-making (MCDM) method based on fuzzy Delphi, fuzzy DEMATEL and fuzzy ANP as a structured methodology to assist decision makers in strategic maintenance. The fuzzy Delphi method (FDM) is applied to refine the effective criteria, fuzzy DEMATEL is applied for defining the direction and relationships between criteria and Fuzzy ANP is used for the selection of optimized maintenance strategy.

The results identify “strategic management complexity” as the top criterion. The predictive maintenance (PdM) with the highest priority is the best strategy. It is followed by reliability-centered (RCM), condition-based (CBM), total productive (TPM), predictive (PM) and corrective maintenance (CM).

Today, companies act in an atmosphere that is known with the features of uncertainty. In this atmosphere, only those companies can survive that have a strategy based on presenting the quality services and products to their customers. Similarly, maintenance as a system plays a vital role in availability and the quality of products, which creates value for customers. The selection of maintenance strategy is a kind of MCDM problem, which includes consideration of different factors. This article considers a broad category of alternates, including CM, PM, TPM, CBM, RCM and PdM.

This paper aims to introduce a practical expert decision support system (EDSS) for performing location analysis and making real estate location decisions in the organization’s facility and real estate management (FREM) department in presence of several decision criteria, under risk and uncertainty. This tool is particularly useful for making strategic decisions in facility planning, portfolio management, investment appraisal, development project evaluations and deciding on usage possibilities in an unbiased, objective manner.

The proposed EDSS uses fuzzy logic and uncertainty theory as two of the most useful tools to deal with uncertainties involved in the problem environment. The system performs an unbiased mathematical analysis on the input data provided by the decision-maker, using a combination of Analytical Hierarchy Process (AHP) and Global Criterion Method; determines a suitable compromise level between the objectives; and delivers a set of locations that complies best with the outlined desires of the management as the final solution. The application of the system is tested on a real case and has delivered satisfactory results.

The proposed EDSS took the defined objectives, the list of alternative locations, and their attributes as the required input for problem-solving, and used a combination of AHP, Possibilistic approach, and global criterion method to solve the problem. The delivered outcome was a set of proper locations with the right attributes to meet all objectives of the organization at a satisfactory level, confirmed by the problem owners.

The application of such a system with such a degree of preciseness and complexity has been very limited in the literature. The system designed in this study is an Industry 4.0 decision making tool. For designing this system several body of knowledge are used. The present study is particularly useful for making strategic decisions in the domains of portfolio management, investment appraisal, project development evaluations and deciding on property usage possibilities. The proposed EDSS takes the information provided by the experts in the field (through qualitative and quantitative data collecting) as the inputs and operates as an objective decision-making tool using several bodies of knowledge considering the trends and developments in the world of FREM. The strong scientific method used in the core of the proposed EDSS guarantees a highly accurate result.

The purpose of this paper is to determine which criteria should be taken into account while choosing face masks for pandemic times and to what extent their effects are.

Nine face mask alternatives were evaluated based on the assessments of their performance with respect to twelve attributes. Seven experts were asked to evaluate the mask alternatives and the influences among attributes. In gathering expert judgments, spherical fuzzy number-based linguistic terms were utilized in the study to provide a more comprehensive representation domain to them.

According to the results, the most important attributes are found as material type, cost and bacteria–virus protection level. The best face mask is N95, which is followed by respirators and surgical masks.

The implication of the research is to evaluate face masks in terms of criteria such as physical, performance, protection and cost to decide on what basis they were selected as a personal protective equipment (PPE) based on expert assessments. This is useful in selection of the right face mask with optimum performance and provides guidance to the general public and profession specific groups for this purpose. The face mask companies might be also benefitted from the implications of the present study in their design and research and development (R&D) operations.

The preference ranking of the face mask alternatives has not been studied in detail yet in the literature. Focusing on this issue, the present study provides a comprehensive assessment of the selection criteria of face masks in the pandemic era.

Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of an optimal policy for maintenance can be a good solution for industrial units. In fact, by managing constraints such as costs, working hours and human workforce causing sudden equipment failure, production and performance can increase.

Therefore, in this research a model was presented to select the best maintenance strategy at Kaghaz Kar Kasra Co of Iran. In this study, it was tried to integrate the two techniques of goal programming and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize maintenance strategies. First, all factors affecting maintenance were identified, and based on the Best Worst Method (BWM) the degree of their importance was determined.

After the evaluation, only 14 criteria in the 4 dimensions of cost, added value, safety and feasibility were selected. The highest points were given to the criteria of equipment cost and depreciation, equipment and personnel performance, equipment installation time and technical feasibility, respectively. In the next stage, using the TOPSIS method the item of maintenance strategy was ranked, and the 3 strategies of preventive maintenance (PM), predictive maintenance (PDM) and corrective maintenance (CM) were chosen. Modeling was performed utilizing a goal programming approach to select the optimal maintenance strategy for 13 devices. All the technical specifications, cost limits and the device time were extracted. After the model was finished and solved the best item for each device was specified.

1. Developing a goal programming model and decision-making dashboard. 2. Identifying the criteria and factors affecting the selection of the maintenance strategy for paper production Industry

This paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.

The critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.

For a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.

Only three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.

The proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.

This research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.