Multi-attribute Decision-making Model Research Articles

An integrated dynamic intuitionistic fuzzy MADM approach for personnel promotion problem

Purpose – One of the key success factors for an organization is the promotion of qualified personnel for vacant positions. Especially, the promotion of middle and senior managers play an important role in terms of organization’s success. In personnel promotion problem in which the candidates are nominated within the organization and they have been working for a specific period of time and are known in their organization, the candidates should be evaluated based on their recent as well as past performances to make right selection for the vacant position. For this reason, the purpose of this paper is to propose an integrated dynamic multi-attribute decision-making (MADM) model based on intuitionistic fuzzy set for solving personnel promotion problem. Design/methodology/approach – The proposed model integrates analytic hierarchy process (AHP) technique and the dynamic evaluation by intuitionistic fuzzy operator for personnel promotion. AHP is employed to determine the weight of attributes based on decision maker’s opinions, and the dynamic operator is utilized to aggregate evaluations of candidates for different years. Atanassov’s intuitionistic fuzzy set theory is utilized to represent uncertainty and vagueness in MADM process. Findings – A numerical example is presented to show the applicability of the proposed method for personnel promotion problem and a sensitivity analysis is conducted to demonstrate efficiency of dynamic evaluation. The findings indicate that the varying weights of years employed determined the best candidate for promotion. Originality/value – The novelty of this study is defining personnel promotion as a MADM problem in the literature for the first time and proposing an integrated dynamic intuitionistic fuzzy MADM approach for the solution, in which the candidates are evaluated at different years.

Auxiliary transportation mode in a fully-mechanized face in a nearly horizontal thin coal seam

On fully-mechanized faces in nearly horizontal thin coal seams (NHTCS), the selection of the auxiliary transportation mode is difficult. Generally, auxiliary transportation mainly includes trackless or rail transportation. Combined with a familiar NHTCS fully-mechanized face, a multi-attribute decision-making model was set up for the decision. The index weight was objectively determined with the fuzzy number and entropy method. The priority order of auxiliary transportation modes was obtained from the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE). The results show that: the net flow of the mode can be expressed by the function of the surrounding rock deformation of the roadway, the dimension of equipment and the thickness of the coal seam; Based on the cost type index, there is a positive correlation between the net flow with the height and width of the trackless auxiliary transportation equipment, respectively. The trackless auxiliary transportation equipment selection principle should be “height first then width”. Combined with the field application of the trackless auxiliary transportation in Liangshuijing coal mine, the proper method to achieve the safe and high-efficient exploitation of a NHTCS fully-mechanized face is trackless tyred vehicle auxiliary transportation.

Supplier selection using grey theory: a case study from Indian banking industry

Purpose – As the suppliers of any organization are considered as the strategic partners and taken as the integral part of the supply chain network, hence it is very crucial decision to select the suppliers in order to get the competitive edge. Whenever, any organization select its suppliers then organization evaluates every supplier with respect to certain criteria, which are already listed in the organization’s policies. Since supplier selection is based on the evaluation of various attributes of each alternative; therefore this problem is a multi-attribute decision-making (MADM) problem. The purpose of this paper is to propose a model for selecting the best supplier. Design/methodology/approach – This paper reports the implementation of grey theory to choose the appropriate supplier with uncertain information. Grey values have been used to give the ratings and weightage to various criteria which are being used to evaluate the different supplier alternatives. And finally the grey possibility degree has been calculated to rate the various alternatives. Findings – This paper proposed a MADM model based on grey theory to select the optimal supplier and finally, the proposed model has been applied to select the best supplier for “optimizing digital banking” in the Indian context. Research limitations/implications – The selection criteria selected here through literature review may not fit the whole industry and moreover this paper can be extended into the field where multiple-supplier selection is required, like in manufacturing sector where the authors cannot rely always on single supplier. So, in that case the authors need to pick more than one best alternatives. Practical implications – The grey theory approach can be applied almost all the services industries, where the exact information about the suppliers in quantitative terms is very difficult to find. So the authors can use the grey numbers to rate the suppliers. Social implications – Since, the coming generation is more dependent on the internet banking, so it becomes very much necessary for the banking sector to update the existing system. Due to lack in technical skills, outsourcing the information technology and software development is the best option. Hence, Grey theory based model can be used in selecting the best supplier under uncertain information. Originality/value – Very few studies have been done in India using grey theory approach for supplier selection and moreover, to the best knowledge of the authors’ this is the first study which employs grey theory for selecting the best supplier in banking industry. Moreover the digital banking system is the future of banking industry, so every player should provide the digital banking structure in order to survive in the market.

A multi-attribute decision-making model for the robust classification of multiple inputs and outputs datasets with uncertainty

Many multiple-criteria decision-making (MCDM) methods have been proposed for decision-making environments. However, the performance of these methods is degraded by the uncertainty and inaccuracy which characterizes most practical decision-making environments as a result of the inherent prejudices and preferences of the decision-makers or experts and an insufficient volume of multiple inputs and outputs (MIO) information. Accordingly, the present study proposes an enhanced MIO classification method to address these limitations of existing MCDM methods. The proposed MIO classification method designated as the FVM-index method integrates fuzzy set theory (FST), variable precision rough set (VPRS) theory, and a modified cluster validity index (MCVI) function, and is designed specifically to filter out the uncertainty and inaccuracy inherent in the surveyed MIO real-valued dataset; thereby improving the classification performance. The effectiveness of the proposed approach is first demonstrated by comparing the MIO classification results obtained for three relating UCI datasets: (1) the original dataset; (2) a dataset with a large amount of inaccurate instances; and (3) an FVM-index filtered dataset extracted from the original dataset using a statistical approach. Then, the validity of the proposed approach is illustrated by using an Augmented Reality product design and a hospital related datasets. The results confirm that the proposed FVM-index method provides a good classification performance even in the presence of inaccuracy and uncertainty. As a result, it provides a robust approach for the extraction of reliable decision-making rules.

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

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

Development of a Measurement Instrument for Website Design Utilizing Analytic Hierarchy Process (AHP) Multi-Attribute Decision Modeling

For many organizations their websites are the first impression customers have of their companies. The impact and importance of web design on organizations continues to dramatically increase. Yet many organizations continue to struggle to find tools to strategically analyze their websites and overall online presence. While there have been numerous studies offering “best practices” for website design, most of these are dated and do not take into consideration new applications and social media tools that come into the market. In our research over 900 surveys were conducted on Inc. Magazine's Top 500 list (2011-13) of fastest growing companies in the United States. The analysis of these surveys resulted in a list of shared elements (best practices) common to the websites surveyed. Through the use of the Analytic Hierarchy Process (AHP) Multi-attribute Decision Model, we developed a measure by which companies can assess their web presence in comparison to this best practices model. This model provides an internally consistent, robust model against which to measure an organization's website.

A Content Analysis in Reverse Logistics: A review

The purpose of this paper is to provide a comprehensive review in the various publications on the concept of Reverse Logistics (RL) and the related areas within the period 1998-2012. The content analysis approach has been opted to collect the relevant information from different books, journals, and conferences. A broad review of literature in RL from its emergence until the recent discussions have been analyzed and compared in this research. The findings show that, the theoretical construct in RL has been initiated from the conjunction features in the waste management and logistics activities. This idea had been developed by introducing the new term as RL and its definitions and contents such as the activities; key drivers; barriers to use; material flow, and networks in RL. Furthermore, the findings present the various modeling in different aspects of RL, for instance, the mathematical modeling by applying the existence methods in Multi Attribute Decision-Making Models (MADM). In addition, the environmental concerns and governmental legislatives matters and impacts, which have been highlighted, recently, on RL have been deliberated. Hence, this paper would assist the researchers and practitioners to obtain a broad review of RL in the last decade and, also provide an agenda for the future researches.

Selection of Waste Disposal Firms Using Grey Theory Based Multi-criteria Decision Making Technique

In developing countries, very few methods of medical disposal and treatments are used which lead to adverse impacts to public health. In healthcare facilities, the issue of infectious waste generated has become the big concern today for hospital authority. To ensure the proper disposal of the medical wastes has become the big challenge for the hospital waste management and they think that to outsource the waste disposal process is the best option. Hence, to ensure the proper hygiene environment and to minimize the cost of operations, the trends in outsourcing the waste disposal process is increasing. But, outsourcing process is not easy for the hospitals authorities as it is very difficult to select the best waste disposal firm, which takes care of all the government regulations and at the same time is cost effective.Since selection of healthcare waste disposal firms is based on the evaluation of various attributes of each alternative, therefore this problem is a multi-attribute decision-making (MADM) problem. Till now, so many methods like analytical hierarchy process (AHP); analytical network process (ANP); data envelopment analysis (DEA); fuzzy theory; mathematical programming, etc. have been used by various researchers to select the best alternate out of the available choices. The above-mentioned techniques either used the precise values of the attributes or in terms of membership functions. However, in case of uncertainty these techniques failed to implement. Hence, this paper reports the implementation of grey theory to choose the appropriate healthcare waste disposal firm with uncertain information. This paper proposed a MADM model based on grey theory to select the optimal waste disposal firm and finally, the proposed model has been applied to select the best healthcare waste disposal firm.

Analytical Hierarchy Process/Technique for Order Preference by Similarity to Ideal Solution-based approach to the generation of environmental improvement options for painting process e Results from an industrial case study

The paper, based on an industrial case study, explores possibilities for environmental improvements in the painting process of the manufacture of central heating boilers. The goal is to identify opportunities to prevent or limit the environmental impacts of the painting process. The evaluation problem was formulated as a multi-attribute decision-making model and solved using a combined approached of analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) approach. AHP was used to determine the essential weighting factors. Alternatives ranking was conducted using TOPSIS. This evaluation summarized benefits of a clean, non-polluting technology that effectively eliminates the use of hazardous solvents and prevents the generation of volatile organic emissions and hazardous solid waste. Results show that the most appropriate improvement option to be implemented in the considered company is a paint shop equipped with its own air supply, exhaust fans and heating system.

A Multi-Attribute Decision-Making Model for Construction Enterprise Resource Planning System Selection

ABSTRACTEnterprise resource planning (ERP) systems are increasingly becoming essential business technologies. Selection of the optimal ERP system among numerous alternatives is regarded as a major challenge for the decision-maker. This choice is complicated by multiple attributes that the decision-maker needs to consider when evaluating different ERP options. The purpose of this article is to propose a multi-attribute decision-making model for ERP system selection based on data envelopment analysis (DEA). The strengths of the proposed DEA model include the input-output framework, which allows decision-makers to incorporate multiple attributes when evaluating different ERP options. These attributes are not required to be congruent. They can be measured by such criteria as monetary sums, time, or subjective score. Additionally, DEA can accommodate a large number of options (i.e., ERP systems) and variables (i.e., attributes). The proposed approach was utilized by a construction contractor to select the optimal ERP option out of eleven alternatives. During the evaluation process, the decision-maker incorporated six attributes to make the decision. These considered attributes are total cost, implementation schedule, functionality, user friendliness, customization capability, and service and support quality. Sensitivity analysis proved that the proposed model is stable and robust.

Fuzzy Multiple Attributes Decision-Making Hybrid Model for Selecting Aircraft Baseline Scenario

Expertise or certain performance metrics information was often separately considered to model the Aircraft baseline scenario preferential problem in a traditional way, with one-sidedness and subjective. This paper focus on baseline scenario preferential problem in aircraft conceptual design, proposed a hybrid fuzzy decision merit model with synthesizing the description and numerical fuzzy indicators and using interval theory to model the fuzziness of each attribute. Proposed the preference information method based on multi-level expertise interval scale to model description fuzzy indicators and grey incidence analysis to model numerical fuzzy indicators, induced objective information entropy to increase the discrimination degree among group information, and then established a hybrid fuzzy interval multi-attribute decision model based on expertise, grey scale and objective information entropy, by constructing the Lagrangian function to solve each attribute weights. Applied it to the aircraft baseline scenario preferred example, to verify its feasibility and effectiveness.

Discrete task switching in overload: A meta-analyses and a model

We describe a computational multi-attribute decision model that predicts the decision aspect of sequential multitasking. We investigate how people choose to switch tasks or continue performing an ongoing task when they are in overload conditions where concurrent performance of tasks is impossible. The model is based on a meta-analytic integration of 31 experiments from the literature on applied task switching. Consistent trends from the meta-analysis, to avoid switching, and to switch to tasks lower difficulty, along with greater salience, priority and interest are used to set polarity parameters in the mathematical model.

Assessment of synergies for selecting a project portfolio in the petroleum industry based on a multi-attribute utility function

Abstract The oil and gas industry engages in risk activities: companies in this line of business are routinely faced with decision making situations under risk and uncertainty. This article focuses on evaluating Exploration and Production (E&P) projects which are in the development phase. Three key aspects that should be considered in selecting projects that aim to develop the production of oil and gas are as follows: the stochastic and multi-objective nature of this decision context, an assessment of a project׳s synergies, and the influence that these aspects have on the decision maker׳s preference structure. The contribution of this paper is to put forward a multi-attribute decision model to aid selecting oil and gas development projects in order to assign them to an appropriate portfolio. For this purpose, the proposed model takes into account the aspects described above. A multi-attribute decision model is proposed to aid selecting such projects and a numerical application is conducted. Questions related to assessing uncertainty and considering multiple objectives are tackled within the axiomatic structures of MAUT (Multi-attribute Utility Theory). Assessing synergies between projects is particularized in the context of selecting an E&P project portfolio and, in addition, the consequences of these synergistic considerations in value- or utility-based decision models are discussed. Finally, a numerical application is set out in order to visualize and discuss the steps of the model, as well as to demonstrate and discuss its usefulness and some practical issues that arise.

Designing a computational tool for supplier selection using analytical hierarchy process

With increasingly competitive global world markets, companies are under intense pressure to find ways to cut production and material costs to survive and sustain their competitive position. Since a qualified supplier is a key element and a good resource for a buyer in reducing such costs at optimum material quality, evaluation and selection of the right suppliers is critical to maintain competitiveness. This decision becomes more complicated in case of multiple suppliers, multiple conflicting criteria, and imprecise parameters. Most supplier selection models consider the buyer's viewpoints and maximise only the buyer's advantages and thus do not necessarily lead to an optimal situation for business competitiveness. This paper deals with a multi attribute decision model using AHP for the justification of supplier selection, explore the factors influencing the supplier selection, the relationship between the identified factors and the firm's performance with reference to manufacturing industries in Bhubaneswar City.

An Application of the Integrated IBA-TOPSIS Model in Supplier Selection

The supplier selection process attracted a lot of attention in the business management literature. This process takes into consideration several quantitative and qualitative variables and is usually modeled as a multi-attribute decision making (MADM) problem. A recognized shortcoming in the literature of classical MADM methods is that they don't permit the identification of interdependencies among attributes. Therefore, the aim of this study is to propose a model for selecting suppliers of telecommunications equipment that includes the interaction between attributes. This interaction can model the hidden knowledge needed for efficient decision-making. To model interdependencies among attributes the authors use a recently proposed consistent fuzzy logic, i.e. interpolative Boolean algebra (IBA). For alternatives ranking they use the classical MADM method TOPSIS. The proposed model was evaluated on a real-life application. The conclusion is that decision makers were able to integrate their reasoning into the MADM model using interpolative Boolean algebra.

A multi-objective approach to supply chain risk management: Integrating visibility with supply and demand risk

This paper investigates the relationship among supply chain visibility (SCV), supply chain risk (SCR), and supply chain cost of new and seasonal products. We assume that demand is probabilistic and comes from different scenarios such as forecasting, benchmarking, and market analysis data. For utilizing multi-attribute decision modeling, we build a model to maximize SCV and minimize both SCR and supply chain cost from an operational perspective. A heuristic algorithm based on a relaxation method on decision variables is proposed to solve an NP-hard model, and to show how a multi-objective approach provides near-optimum solutions. The results show that more visibility is desirable, because it increases efficiency in a supply chain and decreases both cost and risk.

Interval grey numbers based multi-attribute decision making method for supplier selection

Purpose – The purpose of this paper is to study a novel grey possibility degree approach, which is combined with multi-attribute decision making (MADM) and applied MADM model for solving supplier selection problem under uncertainty information. Design/methodology/approach – The supplier selection problem is a typical MADM problem, in which information of a series of indexes should be aggregated. However, it is relatively easy for decision makers to define information in uncertainty, sometimes as a grey number, rather than a precise number. By transforming linguistic scale of rating supplier selection attributes into interval grey numbers, a novel grey MADM method is developed. Steps of proposed model were provided, and a novel grey possibility degree approach was proposed. Finally, a numerical example of supplier selection is utilized to demonstrate the proposed approach. Findings – The results show that the proposed approach could solve the uncertainty decision-making problem. A numerical example of supplier selection is utilized to demonstrate the proposed approach. The results show that the proposed method is useful to aggregate decision makers’ information so as to select the potential supplier. Practical implications – The approach constructed in the paper can be used to solving uncertainty decision-making problems that the certain value of the decision information could not collect while the interval value set could be defined. Obviously it can be utilized for other MADM problem. Originality/value – The paper succeeded in redefining interval grey number, constructing a novel interval grey number based MADM approach and providing the solution of the proposed approach. It is very useful to solving system forecasting problem and it contributed undoubtedly to improve grey decision-making models.

Investor Selection Model under Uncertain Information Environment

In this paper, the problem of selecting investor is studied under dual uncertainty information, i.e. fuzzy information and grey information. Concretely, a new index system of evaluating the investors which includes sales capacity, management capacity, throughput, technology capacity, fund capacity, risk capacity and uniformity of enterprise strategy is designed, and then a model of dual uncertainty multi-attribute decision making is proposed. Moreover an optimization algorithm of fuzzy grey deviation degree is present to solve this model.

Transportation Infrastructure Impacts Evaluation: The Case of Egnatia Motorway in Greece

To expand GIS abilities to the consideration of decision criteria, OR/MS researchers strongly pronounce in favor of developing synergies between GIS and multicriteria decision making tools. The rationale of this integration is the GIS ability to store and manage and visualize geographically referenced data and the efficiency of Operational Research tools for modeling decision problems. As a result, MultiCriteria Spatial Decision Support Systems (MC-SDSS) provide a consistent framework that allows alternatives’ ranking combining both spatial data and DMs preferences according to a selected decision rule. Regarding to their applicability in situations that involve classification, multiattribute decision models are considered as a very attractive procedure in urban and regional planning concerning the appraisal of transportation infrastructure construction. In the present a spatial multicriteria evaluation of the impacts derived by the realization of Egnatia Motorway is performed. Egnatia Motorway is considered one of the most significant interventions that have taken place in Greece during the early pre-Olympic Games period and up to the year 2007. With a length of 670 km, it crosses 12 prefectures starting from the Igoumenitsa Port, which provides links by boat to Italy, ending to Kipi in Evros (Greek-Turkish borders). It is a dual carriageway with two traffic lanes per direction with an overall construction cost of about 6b€. Aiming to enrich Northern Greece’s potential in transport industry and tourism, European Union has heavily invested in its construction. In the present paper an integration among GIS functionalities and multi-attribute decision making models such as Analytic Hierarchy Process (AHP) and Ideal Point Methods is proposed in order to estimate the impacts provoked by the construction and operation of Egnatia Motorway in regional level.

A multi-attribute decision model for intrusion response system

The objective of this paper is to present a multi-attribute decision model for network intrusion response. The proposed technique uses a weighted linear combination (WLC) model to standardize the different attributes to a common numeric range before applying a genetic algorithm to determine the appropriate alternative. The model is characterized by a new implementation of individual structure based on the response-resource matrix and a fitness function based on cost-benefit approach. The decision model parameters are represented by vectors modeling the different attributes that are respectively: financial cost, reputation loss, and processing resources. The paper presents a new approach for handling a multi-attribute decision problem in intrusion response context which has not been addressed in previous studies. Finally, this paper will discuss the findings of simulation experiments and will conclude by discussing the effectiveness of the proposed approach.