An integrated supplier selection methodology incorporating QFD and DEA with imprecise data

Supplier evaluation and selection is an important group decision making problem that involves not only quantitative criteria but also qualitative factors incorporating vagueness and imprecision. This paper proposes a novel fuzzy multi-criteria group decision making framework for supplier selection integrating quality function deployment (QFD) and data envelopment analysis (DEA). The proposed methodology allows for considering the impacts of inner dependence among supplier assessment criteria through constructing a house of quality (HOQ). The lower and upper bounds of the weights of supplier assessment criteria are identified by adopting fuzzy weighted average (FWA) method that enables the fusion of imprecise and subjective information expressed as linguistic variables. An imprecise DEA methodology is implemented for supplier selection, which employs the weights of supplier assessment criteria computed by FWA utilizing the data from the HOQ and the supplier ratings with respect to supplier assessment criteria. The application of the proposed framework is demonstrated through a case study in a private hospital in Istanbul.     

Integrated evidential reasoning approach in the presence of cardinal and ordinal preferences and its applications in software selection

A combination of cardinal and ordinal preferences in multiple-attribute decision making (MADM) demonstrates more reliability and flexibility compared with sole cardinal or ordinal preferences derived from a decision maker. This situation occurs particularly when the knowledge and experience of the decision maker, as well as the data regarding specific alternatives on certain attributes, are insufficient or incomplete. This paper proposes an integrated evidential reasoning (IER) approach to analyze uncertain MADM problems in the presence of cardinal and ordinal preferences. The decision maker provides complete or incomplete cardinal and ordinal preferences of each alternative on each attribute. Ordinal preferences are expressed as unknown distributed assessment vectors and integrated with cardinal preferences to form aggregated preferences of alternatives. Three optimization models considering cardinal and ordinal preferences are constructed to determine the minimum and maximum minimal satisfaction of alternatives, simultaneous maximum minimal satisfaction of alternatives, and simultaneous minimum minimal satisfaction of alternatives. The minimax regret rule, the maximax rule, and the maximin rule are employed respectively in the three models to generate three kinds of value functions of alternatives, which are aggregated to find solutions. The attribute weights in the three models can be precise or imprecise (i.e., characterized by six types of constraints). The IER approach is used to select the optimum software for product lifecycle management of a famous Chinese automobile manufacturing enterprise.     

A new fuzzy dempster MCDM method and its application in supplier selection

Supplier selection is a multi-criterion decision making problem under uncertain environments. Hence, it is reasonable to hand the problem in fuzzy sets theory (FST) and Dempster Shafer theory of evidence (DST). In this paper, a new MCDM methodology, using FST and DST, based on the main idea of the technique for order preference by similarity to an ideal solution (TOPSIS), is developed to deal with supplier selection problem. The basic probability assignments (BPA) can be determined by the distance to the ideal solution and the distance to the negative ideal solution. Dempster combination rule is used to combine all the criterion data to get the final scores of the alternatives in the systems. The final decision results can be drawn through the pignistic probability transformation. In traditional fuzzy TOPSIS method, the quantitative performance of criterion, such as crisp numbers, should be transformed into fuzzy numbers. The proposed method is more flexible due to the reason that the BPA can be determined without the transformation step in traditional fuzzy TOPSIS method. The performance of criterion can be represented as crisp number or fuzzy number according to the real situation in our proposed method. The numerical example about supplier selection is used to illustrate the efficiency of the proposed method.     

Evaluation and ranking DMUs in the presence of both undesirable and ordinal factors in data envelopment analysis

In the last decade,ranking units in data envelopment analysis(DEA) has become the interests of many DEA researchers and a variety of models were developed to rank units with multiple inputs and multiple outputs.These performance factors(inputs and outputs) are classified into two groups:desirable and undesirable.Obviously,undesirable factors in production process should be reduced to improve the performance.Also,some of these data may be known only in terms of ordinal relations.While the models developed in the past are interesting and meaningful,they didn t consider both undesirable and ordinal factors at the same time.In this research,we develop an evaluating model and a ranking model to overcome some deficiencies in the earlier models.This paper incorporates undesirable and ordinal data in DEA and discusses the efficiency evaluation and ranking of decision making units(DMUs) with undesirable and ordinal data.For this purpose,we transform the ordinal data into definite data,and then we consider each undesirable input and output as desirable output and input,respectively.Finally,an application that shows the capability of the proposed method is illustrated.     

Interval-valued fuzzy permutation method and experimental analysis on cardinal and ordinal evaluations

This paper presents interval-valued fuzzy permutation (IVFP) methods for solving multiattribute decision making problems based on interval-valued fuzzy sets. First, we evaluate alternatives according to the achievement levels of attributes, which admits cardinal or ordinal representation. The relative importance of each attribute can also be measured by interval or scalar data. Next, we identify the concordance, midrange concordance, weak concordance, discordance, midrange discordance and weak discordance sets for each ordering. The proposed method consists of testing each possible ranking of the alternatives against all others. The evaluation value of each permutation can be computed either by cardinal weights or by solving programming problems. Then, we choose the permutation with the maximum evaluation value, and the optimal ranking order of alternatives can be obtained. An experimental analysis of IVFP rankings given cardinal and ordinal evaluations is conducted with discussions on consistency rates, contradiction rates, inversion rates, and average Spearman correlation coefficients.     

Fuzzy DEMATEL method for developing supplier selection criteria

Supply chain management (SCM) practices have flourished since the 1990s. Enterprises realize that a large amount of direct and indirect profits can be obtained from effective and efficient SCM practices. Supplier selection has great impact on integration of the supply chain relationship. Effective and accurate supplier selection decisions are significant components for productions and logistics management in many firms to enhance their organizational performance. This study pioneers in using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method to find influential factors in selecting SCM suppliers. The DEMATEL method evaluates supplier performance to find key factor criteria to improve performance and provides a novel approach of decision-making information in SCM supplier selection. This research designs a fuzzy DEMATEL questionnaire sent to seventeen professional purchasing personnel in the electronic industry. Our research results find that stable delivery of goods is the most influence and the strongest connection to other criteria.     

A fuzzy-Bayesian model for supplier selection

The selection supplier problem has received a lot of attention from academics in recent years. Several models were developed in the literature, combining consolidated operations research and artificial intelligence methods and techniques. However, the tools presented in the literature neglected learning and adaptation, since this decision making process is approached as a static one rather than a highly dynamic process. Delays, lack of capacity, quality related issues are common examples of dynamic aspects that have a direct impact on long-term relationships with suppliers. This paper presents a novel method based on the integration of influence diagram and fuzzy logic to rank and evaluate suppliers. The model was developed to support managers in exploring the strengths and weaknesses of each alternative, to assist the setting of priorities between conflicting criteria, to study the sensitivity of the behavior of alternatives to changes in underlying decision situations, and finally to identify a preferred course of action. To be effective, the computational implementation of the method was embedded into an information system that includes several functionalities such as supply chain simulation and supplier’s databases. A case study in the biodiesel supply chain illustrates the effectiveness of the developed method.     

Quality-based supplier selection and evaluation using fuzzy data

Since fuzzy quality data are ubiquitous in the real world, under this fuzzy environment, the supplier selection and evaluation on the basis of the quality criterion is proposed in this paper. The C_pk index has been the most popular one used to evaluate the quality of supplier’s products. Using fuzzy data collected from q2 possible suppliers’ products, fuzzy estimates of q suppliers’ capability indices are obtained according to the form of resolution identity that is a well-known theorem in fuzzy sets theory. Certain optimization problems are formulated and solved to obtain α-level sets for the purpose of constructing the membership functions of fuzzy estimates of C_pki. These membership functions are sorted by using a fuzzy ranking method to choose the preferable suppliers. Finally, a numerical example is illustrated to present the possible application by incorporating fuzzy data into the quality-based supplier selection and evaluation.     

A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting

Recently, resolving the problem of evaluation and ranking the potential suppliers has become as a key strategic factor for business firms. With the development of intelligent and automated information systems in the information era, the need for more efficient decision making methods is growing. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable criteria assuming that compromising is acceptable to resolve conflicts. On the other side objective weights based on Shannon entropy concept could be used to regulate subjective weights assigned by decision makers or even taking into account the end-users’ opinions. In this paper, we treat supplier selection as a group multiple criteria decision making (GMCDM) problem and obtain decision makers’ opinions in the form of linguistic terms. Then, these linguistic terms are converted to trapezoidal fuzzy numbers. We extended the VIKOR method with a mechanism to extract and deploy objective weights based on Shannon entropy concept. The final result is obtained through next steps based on factors R, S and Q. A numerical example is proposed to illustrate an application of the proposed method.     

A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method

Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. On the other hand, it is a hard problem since supplier selection is typically a multi criteria group decision-making problem involving several conflicting criteria on which decision maker’s knowledge is usually vague and imprecise. In this study, TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate supplier in group decision making environment. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers for rating the importance of criteria and alternatives. Finally, a numerical example for supplier selection is given to illustrate application of intuitionistic fuzzy TOPSIS method.     

An effective supplier selection method for constructing a competitive supply-relationship

We propose an effective supplier selection method to maintain a continuous supply-relationship with suppliers. Costs have been sharply increasing and profit decreasing as the global competition among companies has increased and customer demands have diversified in the current business environment. Many other functions are now outsourced globally to strengthen competition. As a result, one of the issues is how to select good suppliers which can maintain a continuous supply-relationship.

We suggest a mathematical programming model that considers the change in suppliers' supply capabilities and customer needs over a period in time. We design a model which not only maximizes revenue but also satisfies customer needs. The suggested model is applied to supplier selection and management of the agriculture industry in Korea.     

A green supplier selection model for high-tech industry

With growing worldwide awareness of environmental protection, green production has become an important issue for almost every manufacturer and will determine the sustainability of a manufacturer in the long term. A performance evaluation system for green suppliers thus is necessary to determine the suitability of suppliers to cooperate with the firm. While the works on the evaluation and/or selection of suppliers are abundant, those that concern environmental issues are rather limited. Therefore, in this study, a model for evaluating green suppliers is proposed. The Delphi method is applied first to differentiate the criteria for evaluating traditional suppliers and green suppliers. A hierarchy is constructed next to help evaluate the importance of the selected criteria and the performance of green suppliers. Since experts may not identify the importance of factors clearly, the results of questionnaires may be biased. To consider the vagueness of experts’ opinions, the fuzzy extended analytic hierarchy process is exploited. With the proposed model, manufacturers can have a better understanding of the capabilities that a green supplier must possess and can evaluate and select the most suitable green supplier for cooperation.     

A maximum discrimination DEA method for ranking association rules in data mining

A data mining algorithm, such as Apriori, discovers a huge number of association rules (ARs) and therefore efficiently ranking all these rules is an important issue. This paper suggests a data envelopment analysis (DEA) method for ranking the discovered ARs using a maximum discrimination between the interestingness criteria defined for all ARs. It is shown that the proposed DEA model has a unique optimal solution which can be computed efficiently when the maximum discrimination between the criteria, the difference between DEA weights, is considered. The contribution of this study can be explained as follows: First, we show that using the conventional DEA model for ranking ARs may produce an invalid result because the weights corresponding to interestingness criteria would not discriminate between the criteria. This is investigated for a dataset consisting of 46 ARs with four criteria, namely support, confidence, itemset value and cross-selling. The paper also introduces the maximum discrimination between the weights of the criteria and obtains the optimal solution of the corresponding DEA model efficiently without the need of solving the related mathematical models. On the other hand, this model concludes less number of useful rule(s). A comparative analysis is then used to show the advantage of the proposed DEA method.     

A new hybrid method for gene selection

Gene selection is a significant preprocessing of the discriminant analysis of microarray data. The classical gene selection methods can be classified into three categories: the filters, the wrappers and the embedded methods. In this paper, a novel hybrid gene selection method (HGSM) is proposed by exploring both the mutual information criterion (filters) and leave-one-out-error criterion (wrappers) under the framework of an improved ant algorithm. Extensive experiments are conducted on three benchmark datasets and the results confirm the effectiveness and efficiency of HGSM.     

How to combine inconsistent ordinal and cardinal preferences: A satisficing modelling approach

This paper analyzes a situation where a decision-maker provides cardinal and ordinal information about her/his preferences towards m objects (criteria, alternatives, etc.). However, the two rankings derived from her/his preferences are different. This situation of “inconsistent preferences” is formalized. Moreover, in order to deal with this type of conflictive situation, a satisficing model based on the minimization of p-metric distance functions is proposed. In this way, a final set of weights associated with the m objects, by using the cardinal and the ordinal information, is obtained. The final model corresponds to a linear extended goal programming, so the proposed approach is operationally and computationally efficient, since the computations are reduced to solving a small number of linear programming models of a moderate size.     

A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty

This paper addresses one of the key objectives of the supply chain strategic design phase, that is, the optimal selection of suppliers. A methodology for supplier selection under uncertainty is proposed, integrating the cross‐efficiency data envelopment analysis (DEA) and Monte Carlo approach. The combination of these two techniques allows overcoming the deterministic feature of the classical cross‐efficiency DEA approach. Moreover, we define an indicator of the robustness of the determined supplier ranking. The technique is able to manage the supplier selection problem considering nondeterministic input and output data. It allows the evaluation of suppliers under uncertainty, a particularly significant circumstance for the assessment of potential suppliers. The novel approach helps buyers in choosing the right partners under uncertainty and ranking suppliers upon a multiple sourcing strategy, even when considering complex evaluations with a high number of suppliers and many input and output criteria.     

基于混合VIKOR方法的供应商选择决策模型

通过文献统计方法构建供应商选择评价准则体系. 针对主客观评价准则下多种数据类型混合的评价信息, 提出根据采集到的原始数据客观确定决策者和决策准则权重, 避免主观赋权带来的二次不确定性. 采用基于混合信息的VIKOR方法集结评价值, 以克服数据类型的不可公度性和数据转换造成的信息损失. 从理论、数值实验和敏感性分析3 个维度与TOPSIS 方法进行对比, 表明了所提出方法在供应商选择决策过程中的特点和优势.

     

A fuzzy model for supplier selection in quantity discount environments

Traditionally, supplier selection should simultaneously take into account numerous heterogeneous criteria, and then is a tedious task for the purchasing decision makers. It becomes especially complicated when quantity discounts are considered at the same time. Under such manner, most studies often formulate such a problem as a Multi-Objective Linear Programming (MOLP) problem, and then scale it down to a Mixed Integer Programming (MIP) problem to handle the inherited multi-objectives simultaneously. However, this approach often neglects to consider scaling and subjective weighting issues. In order to ease the problem mentioned above and to obtain a more reasonable compromise solution for allocating order quantities among suppliers with their quantity discount rate offered, the Analytical Hierarchy Process (AHP) and fuzzy compromise programming are introduced in this study. An illustrated example is presented to demonstrate the proposed model and to illuminate two kinds of attitudes for decision makers. The information from the experiments can be utilized further to explain the suppliers’ possible improvement and to help create win–win policies.     

Combining Bayesian Networks and Total Cost of Ownership method for supplier selection analysis

In this study, we analyze the supplier selection process by combining Bayesian Networks (BN) and Total Cost of Ownership (TCO) methods. The proposed approach aims to efficiently incorporate and exploit the buyer’s domain-specific information when the buyer has both limited and uncertain information regarding the supplier. This study examines uncertainty from a total cost perspective, with regards to causes of supplier performance and capability on buyer’s organization. The proposed approach is assessed and tested in automotive industry for tier-1 supplier for selecting its own suppliers. To efficiently facilitate expert opinions, we form factors to represent and explain various supplier selection criteria and to reduce complexity. The case study in automotive industry shows several advantages of the proposed method. A BN approach facilitates a more insightful evaluation and selection of alternatives given its semantics for decision making. The buyer can also make an accurate cost estimation that are specifically linked with suppliers’ performance. Both buyer and supplier have clear vision to reduce costs and to improve the relations.     

A genetic algorithm for a bicriteria supplier selection problem

In this paper, we discuss the problem of selecting suppliers for an organisation, where a number of suppliers have made price offers for supply of items, but have limited capacity. Selecting the cheapest combination of suppliers is a straightforward matter, but purchasers often have a dual goal of lowering the number of suppliers they deal with. This second goal makes this issue a bicriteria problem – minimisation of cost and minimisation of the number of suppliers. We present a mixed integer programming (MIP) model for this scenario. Quality and delivery performance are modelled as constraints. Smaller instances of this model may be solved using an MIP solver, but large instances will require a heuristic. We present a multi-population genetic algorithm for generating Pareto-optimal solutions of the problem. The performance of this algorithm is compared against MIP solutions and Monte Carlo solutions.