Designing an integrated AHP based decision support system for supplier selection in automotive industry

PurposeThe purpose of this paper is to propose a decision support model for supplier selection based on analytic hierarchy process (AHP) using a case of automotive industry in a developing country of Pakistan and further performs sensitivity analysis to check the robustness of the supplier selection decision.MethodologyThe model starts by identifying the main criteria (price, quality, delivery and service) using literature review and ranking the main criteria based on experts’ opinions using AHP. The second stage in the adopted methodology is the identification of sub criteria and ranking them on the basis of main criteria. Lastly perform sensitivity analysis to check the robustness of the decision using Expert Choice^ۛ software.FindingsThe suppliers are selected and ranked based on sub criteria. Sensitivity analysis suggests the effects of changes in the main criteria on the suppliers ranking. The use of AHP in the supplier selection gives the decision maker the confidence of the consistency and the robustness throughout the process.Practical implicationsThe AHP methodology adopted in this study provides managers in automotive industry in Pakistan with the insights of the various factors that need to be considered while selecting suppliers for their organizations. The selected approach also aids them in prioritizing the criterion. Managers can utilize the hierarchical structure of adopted supplier selection methodology suggested in this study to rank the suppliers on the basis of various factors/criteria.Originality/valueThis study makes three novel contributions in supplier selection area. First, AHP is applied to automotive industry and use of AHP in the supplier selection gives decision maker the confidence of the consistency. Second, sensitivity analysis enables in understanding the effects of changes in the main criteria on the suppliers ranking and help decision maker to check the robustness throughout the process. Last, we find it important to come with a simple methodology for managers of automotive industry so that they can select the best suppliers. Moreover, this approach will also help managers in dividing the complex decision making problem into simpler hierarchy.     

The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems

Decision makers today are faced with a wide range of alternative options and a large set of conflicting criteria. How to make trade-off between these conflicting attributes and make a scientific decision is always a difficult task. Although a lot of multiple criteria decision making (MCDM) methods are available to deal with selection applications, it’s observed that in most of these methods the ranking results are very sensitive to the changes in the attribute weights. The calculation process is also ineffective when a new alternative is added or removed from the MCDM problem. This paper presents an improved TOPSIS method based on experimental design and Chebyshev orthogonal polynomial regression. A feature of this method is that it employs the experimental design technique to assign the attribute weights and uses Chebyshev regression to build a regression model. This model can help and guide a decision maker to make a reasonable judgment easily. The proposed methodology is particularized through an equipment selection problem in manufacturing environment. Two more illustrative examples are conducted to demonstrate the applicability of the proposed method. In all the cases, the results obtained using the proposed method almost corroborate with those derived by the earlier researchers which proves the validity, capability and potentiality of this method in solving real-life MCDM problems.     

A Problem-based Selection of Multi-attribute Decision-making Methods

Different multi-attribute decision-making (MADM) methods often produce different outcomes for selecting or ranking a set of decision alternatives involving multiple attributes. This paper presents a new approach to the selection of compensatory MADM methods for a specific cardinal ranking problem via sensitivity analysis of attribute weights. In line with the context-dependent concept of informational importance, the approach examines the consistency degree between the relative degree of sensitivity of individual attributes using an MADM method and the relative degree of influence of the corresponding attributes indicated by Shannon's entropy concept. The approach favors the method that has the highest consistency degree as it best reflects the decision information embedded in the problem data set. An empirical study of a scholarship student selection problem is used to illustrate how the approach can validate the ranking outcome produced by different MADM methods. The empirical study shows that different problem data sets may result in a different method being selected. This approach is particularly applicable to large-scale cardinal ranking problems where the ranking outcome of different methods differs significantly.     

Coding,evaluation and optimal selection of a mechatronic system

This paper presents Multi Attribute Decision Making (MADM) based methodology for evaluation and selection of a mechatronic system. An exhaustive list of attributes which influence the structure and performance of a mechatronic system are identified. An attribute based coding scheme for identification and differentiation of mechatronic systems is developed.A three stage selection procedure is proposed for optimal selection of a mechatronic system. In the first stage, large numbers of available mechatronic systems are converged to a manageable number using elimination search. Second stage, a matrix for storing all the information pertaining to a mechatronic system alternative is proposed. In the third stage, all the mechatronic systems are ranked according the Euclidian distance of mechatronic system from best possible and worst solutions. Two visual methods namely linear graph and spider chart are proposed for ranking of mechatronic systems. An illustrative example explaining the implementation procedure of proposed methodology is discussed at the end.This methodology is useful for industries in selection of a mechatronic system and also useful for any customer in selection of an optimal mechatronic product.     

An approach for pattern oriented software development based on a design handbook

In many mature engineering disciplines, reuse of available design knowledge is helped by the presence of handbooks. These handbooks record the details of existing system components and help in the process of evaluating design alternatives while building new systems. In recent times, design patterns have been identified as fundamental components of an object oriented design. However, they are presented in a format that may not be best suited for systematic selection and use while evaluating design alternatives. This paper provides a procedure to construct a handbook based on design patterns. This handbook helps designers in methodical selection of design patterns. The construction of the handbook is based on the identification of a set of key attributes of a design pattern and quantification of these attributes using the principles of measurement theory. A new methodology for object oriented design which consults the handbook during the design process is also introduced. The proposed methodology along with the handbook helps in evaluating different design alternatives. This revised version was published online in June 2006 with corrections to the Cover Date.     

A new integrated methodology using modified Delphi-fuzzy AHP-PROMETHEE for Geospatial Business Intelligence selection

Geospatial Business Intelligence (Geospatial BI) is a system that combines multidimensional analysis and cartographic visualization. It plays an important role in decision making process for enterprises. Adopting such a comprehensive solution may result in the great investment decision for them, so great deal of attention should be given in the selection of the optimal system. As there are many impacting factors in the selection of Geospatial BI system, the same process is considered as a complex multi-criteria decision making problem. In this paper, we explore the application of an integrated methodology for the evaluation of various Geospatial BI alternatives. The proposed methodology integrates the three well-known decision-making techniques, namely Modified Delphi, fuzzy analytic hierarchical process (fuzzy-AHP), and preference ranking organization method for enrichment evaluations (PROMETHEE). In this respect, the modified Delphi is used to select the most impacting factors by a few decision-makers. The fuzzy-AHP is employed to analyze the structure of the problem and to obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values. Then, the PROMETHEE technique is used for optimal ranking of the alternative system choices. A step-by-step, numerical study is illustrated by using the proposed methodology on the decision making problem of a company that is faced to five Geospatial BI solutions. The results demonstrate that the proposed methodology can successfully accomplish our goal of this study.     

Optimizing target selection complexity of a recommendation system by skyline query and multi-criteria decision analysis

Various factors related to user consideration cause a target selection problem that may lead users to receive unexpected or confusing results. Traditionally, the recommendation system is constructed to help the user filter out unrelated targets and recommend targets that may be of interest to the user. However, the complexity of target selection requires a more advanced decision-making analysis to offer support. Determining how to optimize the target selection complexity of a recommendation system has become a critical challenge. This study proposes a novel approach using skyline query and multi-criteria decision analysis to recommend Top-k targets for user selection. Skyline query domination reduces the complexity of target selection by filtering out non-dominant candidates and keeping the dominant candidates for multi-criteria decision analysis. After the skyline query processing, the multi-criteria decision analysis is optimized, producing a Top-k ranking order of the candidate targets. The experiment illustrates an empirical case study to verify the effectiveness of the proposed approach. The contribution is optimizing the target selecting complexity of the recommendation system to solve the target selection problem.

     

Dynamic adaptation of numerical attributes in a user profile

Recommender systems try to help users in their decisions by analyzing and ranking the available alternatives according to their preferences and interests, modeled in user profiles. The discovery and dynamic update of the users’ preferences are key issues in the development of these systems. In this work we propose to use the information provided by a user during his/her interaction with a recommender system to infer his/her preferences over the criteria used to define the decision alternatives. More specifically, this paper pays special attention on how to learn the user’s preferred value in the case of numerical attributes. A methodology to adapt the user profile in a dynamic and automatic way is presented. The adaptations in the profile are performed after each interaction of the user with the system and/or after the system has gathered enough information from several user selections. We have developed a framework for the automatic evaluation of the performance of the adaptation algorithm that permits to analyze the influence of different parameters. The obtained results show that the adaptation algorithm is able to learn a very accurate model of the user preferences after a certain amount of interactions with him/her, even if the preferences change dynamically over time.     

The dominance-based rough set approach to cylindrical plunge grinding process diagnosis

The dominance-based rough set approach is proposed as a methodology for plunge grinding process diagnosis. The process is analyzed and next its diagnosis is considered as a multi-criteria decision making problem based on the modelling of relationships between different process states and their symptoms using a set of rules induced from measured process data. The development of the diagnostic system is characterized by three phases. Firstly, the process experimental data is prepared in the form of a decision table. Using selected methods of signal processing, each process running is described by 17 process state features (condition attributes) and 5 criteria evaluating process state and results (decision attributes). The semantic correlation between all the attributes is modelled. Next, the phase of condition attributes selection and knowledge extraction are strictly integrated with the phase of the model evaluation using an iterative approach. After each loop of the iterative feature selection procedure the induction of rules is conducted using the VC-DomLEM algorithm. The classification capability of the induced rules is carried out using the leave-one-out method and a set of measures. The classification accuracy of individual models is in the range of 80.77–98.72 %. The induced set of rules constitutes a classifier for an assessment of new process run cases.     

A mathematical programming approach to multi-attribute decision making with interval-valued intuitionistic fuzzy assessment information

This article proposes an approach to handle multi-attribute decision making (MADM) problems under the interval-valued intuitionistic fuzzy environment, in which both assessments of alternatives on attributes (hereafter, referred to as attribute values) and attribute weights are provided as interval-valued intuitionistic fuzzy numbers (IVIFNs). The notion of relative closeness is extended to interval values to accommodate IVIFN decision data, and fractional programming models are developed based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine a relative closeness interval where attribute weights are independently determined for each alternative. By employing a series of optimization models, a quadratic program is established for obtaining a unified attribute weight vector, whereby the individual IVIFN attribute values are aggregated into relative closeness intervals to the ideal solution for final ranking. An illustrative supplier selection problem is employed to demonstrate how to apply the proposed procedure.     

Optimizing top-k selection queries over multimedia repositories

Repositories of multimedia objects having multiple types of attributes (e.g., image, text) are becoming increasingly common. A query on these attributes will typically, request not just a set of objects, as in the traditional relational query model (filtering), but also a grade of match associated with each object, which indicates how well the object matches the selection condition (ranking). Furthermore, unlike in the relational model, users may just want the k top-ranked objects for their selection queries for a relatively small k. In addition to the differences in the query model, another peculiarity of multimedia repositories is that they may allow access to the attributes of each object only through indexes. We investigate how to optimize the processing of top-k selection queries over multimedia repositories. The access characteristics of the repositories and the above query model lead to novel issues in query optimization. In particular, the choice of the indexes used to search the repository strongly influences the cost of processing the filtering condition. We define an execution space that is search-minimal, i.e., the set of indexes searched is minimal. Although the general problem of picking an optimal plan in the search-minimal execution space is NP-hard, we present an efficient algorithm that solves the problem optimally with respect to our cost model and execution space when the predicates in the query are independent. We also show that the problem of optimizing top-k selection queries can be viewed, in many cases, as that of evaluating more traditional selection conditions. Thus, both problems can be viewed together as an extended filtering problem to which techniques of query processing and optimization may be adapted.     

A new method selection approach for fuzzy group multicriteria decision making

Fuzzy multicriteria decision making (MCDM) has been widely used in ranking a finite number of decision alternatives characterized by fuzzy assessments with respect to multiple criteria. In group decision settings, different fuzzy group MCDM methods often produce inconsistent ranking outcomes for the same problem. To address the ranking inconsistency problem in fuzzy group MCDM, this paper develops a new method selection approach for selecting a fuzzy group MCDM method that produces the most preferred group ranking outcome for a given problem. Based on two group averaging methods, three aggregation procedures and three defuzzification methods, 18 fuzzy group MCDM methods are developed as an illustration to solve the general fuzzy MCDM problem that requires cardinal ranking of the decision alternatives. The approach selects the group ranking outcome of a fuzzy MCDM method which has the highest consistency degree with its corresponding ranking outcomes of individual decision makers. An empirical study on the green bus fuel technology selection problem is used to illustrate how the approach works. The approach is applicable to large-scale group multicriteria decision problems where inconsistent ranking outcomes often exist between different fuzzy MCDM methods.     

Linear programming method for multiattribute group decision making using IF sets

The purpose of this paper is to develop a linear programming methodology for solving multiattribute group decision making problems using intuitionistic fuzzy (IF) sets. In this methodology, IF sets are constructed to capture fuzziness in decision information and decision making process. The group consistency and inconsistency indices are defined on the basis of pairwise comparison preference relations on alternatives given by the decision makers. An IF positive ideal solution (IFPIS) and weights which are unknown a priori are estimated using a new auxiliary linear programming model, which minimizes the group inconsistency index under some constraints. The distances of alternatives from the IFPIS are calculated to determine their ranking order. Moreover, some properties of the auxiliary linear programming model and other generalizations or specializations are discussed in detail. Validity and applicability of the proposed methodology are illustrated with the extended air-fighter selection problem and the doctoral student selection problem.     

Accurate multi-criteria decision making methodology for recommending machine learning algorithm

ObjectiveManual evaluation of machine learning algorithms and selection of a suitable classifier from the list of available candidate classifiers, is highly time consuming and challenging task. If the selection is not carefully and accurately done, the resulting classification model will not be able to produce the expected performance results. In this study, we present an accurate multi-criteria decision making methodology (AMD) which empirically evaluates and ranks classifiers’ and allow end users or experts to choose the top ranked classifier for their applications to learn and build classification models for them.Methods and materialExisting classifiers performance analysis and recommendation methodologies lack (a) appropriate method for suitable evaluation criteria selection, (b) relative consistent weighting mechanism, (c) fitness assessment of the classifiers’ performances, and (d) satisfaction of various constraints during the analysis process. To assist machine learning practitioners in the selection of suitable classifier(s), AMD methodology is proposed that presents an expert group-based criteria selection method, relative consistent weighting scheme, a new ranking method, called optimum performance ranking criteria, based on multiple evaluation metrics, statistical significance and fitness assessment functions, and implicit and explicit constraints satisfaction at the time of analysis. For ranking the classifiers performance, the proposed ranking method integrates Wgt.Avg.F-score, CPUTimeTesting, CPUTimeTraining, and Consistency measures using the technique for order performance by similarity to ideal solution (TOPSIS). The final relative closeness score produced by TOPSIS, is ranked and the practitioners select the best performance (top-ranked) classifier for their problems in-hand.FindingsBased on the extensive experiments performed on 15 publically available UCI and OpenML datasets using 35 classification algorithms from heterogeneous families of classifiers, an average Spearman's rank correlation coefficient of 0.98 is observed. Similarly, the AMD method has showed improved performance of 0.98 average Spearman's rank correlation coefficient as compared to 0.83 and 0.045 correlation coefficient of the state-of-the-art ranking methods, performance of algorithms (PAlg) and adjusted ratio of ratio (ARR).Conclusion and implicationThe evaluation, empirical analysis of results and comparison with state-of-the-art methods demonstrate the feasibility of AMD methodology, especially the selection and weighting of right evaluation criteria, accurate ranking and selection of optimum performance classifier(s) for the user's application's data in hand. AMD reduces expert's time and efforts and improves system performance by designing suitable classifier recommended by AMD methodology.     

Sustainable supplier selection: A ranking model based on fuzzy inference system

In these days, considering the growth of knowledge about sustainability in enterprise, the sustainable supplier selection would be the central component in the management of a sustainable supply chain. In this paper the sustainable supplier selection criteria and sub-criteria are determined and based on those criteria and sub-criteria a methodology is proposed onto evaluation and ranking of a given set of suppliers. In the evaluation process, decision makers’ opinions on the importance of deciding the criteria and sub-criteria, in addition to their preference of the suppliers’ performance with respect to sub-criteria are considered in linguistic terms. To handle the subjectivity of decision makers’ assessments, fuzzy logic has been applied and a new ranking method on the basis of fuzzy inference system (FIS) is proposed for supplier selection problem. Finally, an illustrative example is utilized to show the feasibility of the proposed method.     

Fractional programming methodology for multi-attribute group decision-making using IFS

Owing to more vague concepts frequently represented in decision data, mathematical objects introduced by K.T. Atanassov and studied under the name “intuitionistic fuzzy set” (IFS) are more flexibly used to model real-life decision situations. The aim of this paper is to develop a new methodology for solving multi-attribute group decision-making problems using IFS, in which multiple attributes are explicitly considered. In this methodology, for each decision maker in the group two auxiliary fractional programming models are derived from the TOPSIS to determine the relative closeness coefficient intervals of alternatives, which are aggregated for the group to generate the ranking order of all alternatives by computing their optimal degrees of membership based on the ranking method of interval numbers. The implementation process of the method proposed in this paper is illustrated with a numerical example.     

A Fuzzy Collaborative Assessment Methodology for Partner Trust Evaluation

Trust level assessment within collaborative networks is an interesting issue in the partner evaluation and partner selection literature. This paper proposes a fuzzy collaborative assessment methodology for partner trust evaluation within horizontal collaborative networks. The proposed approach concerns a group evaluation context where a decision‐making comity associated with a manufacturer needs to evaluate its company's partners for their ranking purposes. Different expertise levels are attributed to the comity members. In this paper, trust level is evaluated based on information‐sharing attributes considered in the literature as critical influencing factors. Different weights are associated with these attributes with respect to their corresponding influence on trust. The semantic fuzzy partitioning method is considered for the collaborative trust assessment based on unbalanced linguistic term sets representing information‐sharing attributes. The developed approach is applied to a real case showing its effectiveness and its objectivity.     

A systematic approach to heterogeneous multiattribute group decision making

Heterogeneous multiattribute group decision making (MAGDM) problems which involve multi-granularity linguistic labels, fuzzy numbers, interval numbers and real numbers are very complex and important in practical applications of decision making theory. Hitherto, there exists no general theoretical inducement for solving such problems. The purpose of this paper is to develop a systematic methodology for solving the heterogeneous MAGDM problems by introducing the multiattribute ranking index based on the particular measure of closeness to the positive ideal solution (PIS) and using the weighted Minkowski distance to measure differences between each alternative and the PIS as well as the negative ideal solution (NIS). The proposed methodology is shown to have some advantages over the fuzzy TOPSIS. Validity and applicability of the methodology proposed in this paper is illustrated with a real example of the missile weapon system selection problem.     

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these methods still have their problems: (1) depending on the complexity of the wrapper search method, the number of wrapper evaluations can still be too large; and (2) they rely on a univariate ranking that does not take into account interaction between the variables already included in the selected subset and the remaining ones.Here we propose a new approach whose main goal is to drastically reduce the number of wrapper evaluations while maintaining good performance (e.g. accuracy and size of the obtained subset). To do this we propose an algorithm that iteratively alternates between filter ranking construction and wrapper feature subset selection (FSS). Thus, the FSS only uses the first block of ranked attributes and the ranking method uses the current selected subset in order to build a new ranking where this knowledge is considered. The algorithm terminates when no new attribute is selected in the last call to the FSS algorithm. The main advantage of this approach is that only a few blocks of variables are analyzed, and so the number of wrapper evaluations decreases drastically.The proposed method is tested over eleven high-dimensional datasets (2400-46,000 variables) using different classifiers. The results show an impressive reduction in the number of wrapper evaluations without degrading the quality of the obtained subset.     

A systematic approach to heterogeneous multiattribute group decision making

Heterogeneous multiattribute group decision making (MAGDM) problems which involve multi-granularity linguistic labels, fuzzy numbers, interval numbers and real numbers are very complex and important in practical applications of decision making theory. Hitherto, there exists no general theoretical inducement for solving such problems. The purpose of this paper is to develop a systematic methodology for solving the heterogeneous MAGDM problems by introducing the multiattribute ranking index based on the particular measure of closeness to the positive ideal solution (PIS) and using the weighted Minkowski distance to measure differences between each alternative and the PIS as well as the negative ideal solution (NIS). The proposed methodology is shown to have some advantages over the fuzzy TOPSIS. Validity and applicability of the methodology proposed in this paper is illustrated with a real example of the missile weapon system selection problem.