Evaluating suppliers via a multiple levels multiple criteria decision making method under fuzzy environment

Suppliers are an important component of the supply chain. Their ability and performance are what largely determine the success or failure of the supply chain. Thus the evaluation of suppliers has become a very important part of the supply chain management of a company. This paper suggests a multiple levels multiple criteria decision making (MCDM) model under fuzzy environment to evaluate and select suppliers, where a general hierarchical structure is developed to depict the relationship among parent criteria and their sub-criteria and sub-sub-criteria and so on. These criteria are classified into quantitative and qualitative ones. The ratings of alternatives versus qualitative criteria and the importance weights of all criteria in the hierarchical structure are assessed in linguistic values represented by triangular fuzzy numbers. The ranking approach of center of area is suggested to rank all the fuzzy numbers before their weighted ratings aggregation. The final evaluation values of alternatives can then be obtained through the additive weighted ratings from the last to the first level in the criteria structure. Finally a numerical example demonstrates the feasibility of the proposed model.     

Group decision making process for supplier selection with VIKOR under fuzzy environment

During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy MCDM model based on fuzzy sets theory and VIKOR method is proposed to deal with the supplier selection problems in the supply chain system. A numerical example is proposed to illustrate an application of the proposed model.     

A new linguistic MCDM method based on multiple-criterion data fusion

Multiple-criteria decision-making (MCDM) is concerned with the ranking of decision alternatives based on preference judgements made on decision alternatives over a number of criteria. First, taking advantage of data fusion technology to comprehensively consider each criterion data is a reasonable idea to solve the MCDM problem. Second, in order to efficiently handle uncertain information in the process of decision making, some well developed mathematical tools, such as fuzzy sets theory and Dempster Shafer theory of evidence, are used to deal with MCDM. Based on the two main reasons above, a new fuzzy evidential MCDM method under uncertain environments is proposed. The rating of the criteria and the importance weight of the criteria are given by experts’ judgments, represented by triangular fuzzy numbers. Then, the weights are transformed into discounting coefficients and the ratings are transformed into basic probability assignments. The final results can be obtained through the Dempster rule of combination in a simple and straight way. A numerical example to select plant location is used to illustrate the efficiency of the proposed method.     

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.     

A fuzzy TOPSIS based approach for e-sourcing

This study presents a methodology for solving the sealed bid, multi-attribute reverse auction problem of e-sourcing in which the sales item is defined by several attributes, the buyer is auctioneer, and the suppliers are the bidders. There is only one buyer and a number of suppliers. Both qualitative and quantitative attributes of benefit and cost types are considered for solving the winner determination (WD) problem of reverse auction. Here, the WD problem is considered as multi-criteria decision making problem (MCDM). In order to address the imprecision of suppliers or decision makers in formulating the preference value of various attributes in MCDM, a fuzzy TOPSIS based methodology along with a mechanism for determination of fuzzy linguistic value of each attribute is proposed in this article. Entropy method is utilised to enumerate the weights of various attributes automatically without involvement of decision makers. An illustrative example is presented to demonstrate the applicability of the proposed methodology.     

A consensus based group decision making methodology for strategic selection problems of supply chain coordination

In a supply chain (SC), the partners often make collective decisions to solve a number of problems which are characterized by various quantitative and qualitative criteria. This article presents a fuzzy TOPSIS and soft consensus based group decision making methodology to solve the multi-criteria decision making (MCDM) problems in supply chain coordination, i.e., selection problems. This methodology is proposed to improve the coordination in decentralized supply chains, i.e., supply chains that comprise several independent, legally separated entities with their own decision authorities. In order to address the imprecision of supply chain partners in formulating the preference value of various criteria, a fuzzy TOPSIS based methodology is proposed. Moreover, a soft consensus based group decision making approach is used for consensus forming among the supply chain partners, regarding the preference values of various criteria for different alternatives. Correlation coefficient and standard deviation (CCSD) based objective weight determination method is also used for enumeration of the weights of the criterion for fuzzy TOPSIS. To demonstrate the applicability of proposed methodology, an illustrative example has been presented.     

Data transformation in the evidential reasoning-based decision making process

This paper describes the application of an evidential reasoning (ER)‐based decision making process to multiple‐criteria decision making (MCDM) problems having both quantitative and qualitative criteria. The ER approach is based on the decision theory and the theory of evidence and it uses the concept of ‘degree of belief’ to assess decision alternatives on each attribute. When faced with MCDM problems, evaluation and selection or ranking of alternatives appear to be both challenging and vital to arrive at a rational and robust decision. In the presence of both qualitative and quantitative evaluations in an MCDM problem, it is necessary, when using the ER‐based decision making process, to transform or convert quantitative data into a belief structure using a number of grades so that the converted belief structure and the original quantitative data are equivalent in values or utilities. This paper suggests three scenarios for data transformation and examines how the ranking of decision alternatives is changed when different scenarios of data transformation are used. Ranking of UK universities using the ER approach is illustrated as an example.     

A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

This study presents a strategy-aligned fuzzy simple multiattribute rating technique (SMART) approach for solving the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems obtained their optimal solutions without considering firm operations management (OM)/SC strategy. The proposed system utilizes OM/SC strategy to identify supplier selection criteria. A fuzzy SMART is applied to evaluate the alternative suppliers, and deals with the ratings of both qualitative and quantitative criteria. The final decision-maker incorporates the supply risks of individual suppliers into final decision making. Finally, an empirical study is conducted to demonstrate the procedure of the proposed system and identify the suitable supplier(s).     

A new flexible and reliable interval valued fuzzy VIKOR method based on uncertainty risk reduction in decision making process: An application for determining a suitable location for digging some pits for municipal wet waste landfill

Facility location selection problem is one of the challenging and famous kinds of MCDM problems including both quantitative and qualitative criteria. For each Multiple Criteria Decision Making (MCDM) problem, when the ratings of alternatives with respect to the criteria and/or the values of criteria’s weights are presented by Interval Valued Fuzzy Numbers (IVFNs), the conventional fuzzy MCDM methods (Type-1 fuzzy MCDM methods) tend to be less effective. Therefore, the IVF-MCDM (Interval Valued Fuzzy MCDM) methods should be applied for solving such fuzzy MCDM problems. In this paper, we propose an IVF-VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method based on uncertainty risk reduction in decision making process. By using such method, the reliability of the captured decisions in an IVF decision making problem is significantly increased. The proposed method is applied for solving two numerical examples that the former of which is a real application problem related to selecting a suitable location for digging some pits for municipal wet waste landfill in one of the largest cities in Iran. The second numerical example is presented with an aim of comparing our method with the two other IVF-MCDM methods. As a result, we found out the proposed method is reliable and practical for the facility location selection problems and other MCDM problems. Moreover, the proposed method has a considerable accuracy and is flexible and easy to use.     

TOPSIS with belief structure for group belief multiple criteria decision making

The technique for order performance by similarity to ideal solution(TOPSIS)is one of the major techniques in dealing with multiple criteria decision making(MCDM)problems, and the belief structure(BS)model has been used successfully for uncertain MCDM with incompleteness, impreciseness or ignorance. In this paper, the TOPSIS method with BS model is proposed to solve group belief MCDM problems. Firstly, the group belief MCDM problem is structured as a belief decision matrix in which the judgments of each decision maker are described as BS models, and then the evidential reasoning approach is used for aggregating the multiple decision makers' judgments. Subsequently, the positive and negative ideal belief solutions are defined with the principle of TOPSIS. To measure the separation from ideal solutions, the concept and algorithm of belief distance measure are defined, which can be used for comparing the difference between BS models. Finally, the relative closeness and ranking index are calculated for ranking the alternatives. A numerical example is given to illustrate the proposed method.     

A multiple criteria supplier segmentation using outranking and value function methods

Suppliers play a key role in supply chain management which involves evaluation for supplier selection problem, as well as other complex issues that companies should take into account. The purpose of this research is to develop and test an integrated system, which allows qualifying providers and also supplier segmentation by monitoring their performance based on a multiple criteria tool for systematic decision making. This proposal consists in a general procedure to assess suppliers based mainly on exploiting all reliable databases of the company. Firstly, for each group of products, their evaluation criteria are defined collaboratively in order to determine their critical and strategic performance, which are then integrated with other criteria that are specific of the suppliers and represent relevant aspects for the company, also classified by critical and strategic dimensions. Two multiple criteria methods, compensatory and non-compensatory, are used and compared so as to point out their strengths, weaknesses and flexibility for the supplier evaluation in different contexts, which are usually relevant in the supply chain management. A value function approach is the appropriate method to qualify providers to be included in the panel of approved suppliers of the company as this process depends only on own features of the supplier. On the other hand, outranking methods such as PROMETHEE have shown greater potential and robustness to develop portfolios with suppliers that should be partners of the company, as well as to identify other types of relationships, such as long term contracts, market policies or to highlight those to be removed from their portfolio. These results and conclusions are based on an empirical research in a multinational company for food, pharmaceuticals and chemicals. This system has shown a great impact as it represents the first supplier segmentation proposal applied to industry, in which decision making not only takes into account opinions and judgements, but also integrates historical data and expert knowledge. This approach provides a robust support system to inform operative, tactical and strategic decisions, which is very relevant when applying an advanced management in practice.     

Fuzzy compromise programming for Group decision making

A multicriteria technique named fuzzy compromise programming is combined with a methodology known as group decision making under fuzziness to come up with a new technique that supports decision making with multiple criteria and multiple participants (or experts). All criteria (qualitative and quantitative) are modeled by way of fuzzy sets, utilizing the fact that criteria values in most water resources problems are vague, imprecise and/or ill defined. The involvement of multiple experts in the decision process is achieved by incorporating each participant's perception of criteria weights, best and worst criteria values, relative degrees of risk acceptance, as well as other parameters into the problem. The proposed methodology is illustrated with a case study taken from the literature, combined with the input of four expert individuals with diverse backgrounds. After processing the input from the experts, a group compromise decision is formulated.     

A new flexible and reliable IVF-TOPSIS method based on uncertainty risk reduction in decision making process

One of the challenging and famous types of MCDM (Multiple Criteria Decision Making) problems that includes both quantitative and qualitative criteria is Facility location selection problem. For the common fuzzy MCDM problems (Type-1 fuzzy MCDM problems), the ratings of alternatives with respect to the criteria or/and the values of criteria weights, are expressed by the common fuzzy numbers. However, in the majority of cases, determining the exact membership degree for each element of the fuzzy sets which are considered for the ratings of alternatives with respect to the criteria or/and the values of criteria weights as a number in interval [0,1], is difficult. In this situation, the ratings of alternatives with respect to the criteria or/and the values of criteria weights, are expressed by the IVFNs (Interval Valued Fuzzy Numbers) and thereby the IVF-MCDM (Interval Valued Fuzzy MCDM) methods should be used. In this paper, the authors propose an IVF-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method based on uncertainty risk reduction in decision making process. By using this method, the reliability of the captured decisions in an IVF decision making problem is significantly increased. The proposed method is applied for solving a real application problem related to selecting a suitable location for digging some pits for municipal wet waste landfill in one of the largest cities in Iran. The proposed method is also compared with another IVF-TOPSIS method. As a result, the authors concluded that in addition to benefits such as simplicity and ease of use that exist in the previous IVF-TOPSIS methods, the proposed method has a significant reliability and flexibility and is practical for facility location selection problems and other IVF-MCDM problems.     

TOPSIS with fuzzy belief structure for group belief multiple criteria decision making

TOPSIS is one of the major techniques in dealing with multiple criteria decision making (MCDM) problems, and Belief Structure (BS) model and Fuzzy BS model have been used successfully for uncertain MCDM with incompleteness, impreciseness or ignorance. In this paper, the TOPSIS method with Fuzzy BS model is proposed to solve Group Belief MCDM problems. Firstly, the Group Belief MCDM problem is structured as a fuzzy belief decision matrix in which the judgments of each decision maker are described as Fuzzy BS models, and then the Evidential Reasoning approach is used for aggregating the multiple decision makers’ judgments. Subsequently, the positive and negative ideal belief solutions are defined with the principle of TOPSIS. In order to measure the separation from the ideal belief solutions, the concept and algorithm of Belief Distance Measure are introduced to compare the difference between Fuzzy BS models. Using the Belief Distance Measure, the relative closeness and ranking index can be calculated for ranking the alternatives. A numerical example is finally given to illustrate the proposed method.     

Development of a decision support system for supplier evaluation and order allocation

This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.     

Development of optimal water supply plan using integrated fuzzy Delphi and fuzzy ELECTRE III methods—Case study of the Gamasiab basin

This paper presents a novel method for the development of an optimal water supply plan showcased using data from the Gamasiab basin, located in Kermanshah province, Iran, concerning new dams that are being constructed in this semi-arid region. In this paper, a new group multi-criteria decision-making (MCDM) plan is proposed by combining two MCDM methods based on the fuzzy Delphi and fuzzy ELECTRE III methods that convert the experts' opinions to triangular fuzzy numbers based on the level of uncertainty associated with various quantitative and qualitative criteria. Considering the opinions of four non-stakeholder experts and data analysis using the fuzzy Delphi method, the criteria were evaluated. Then, by analysing the results using the fuzzy ELECTRE III method, the final ranking of scenarios is obtained. A sensitivity analysis was conducted to assess the effect of uncertainty on the performance of the decision-making system in scenarios ranking. The total expense, flood control, reservoir capacity and diversion and water transfer played a significant role in selecting the optimal scenario. Additionally, a hydrologic model was developed to evaluate the performance of the optimal scenario in terms of qualitative criteria. The data indicated that there was a good agreement between the results obtained from the hydrological model and the scenario ranking by the employed method. Altogether, a comparison of the proposed method with other MCDM methods, including fuzzy analytic hierarchy process and fuzzy technique for order preference by simulation of ideal solution, indicated that the results of the employed method matched more closely to the local experts' opinion.     

基于权重集结和相对优势关系的多属性决策方法

分析多属性决策方法中决策矩阵规范化和属性权重计算等步骤可能对决策方法合理性造成的不良影响,为克服这些不良影响,提出一种新的多属性决策方法.该方法采用群决策模式进行赋权,在对专家意见进行一致性分析的基础上,集结各位专家给出的属性权重,通过定义备选方案在属性值为实数、区间数和语言值等不同类型属性上的相对优势关系构造判断矩阵,并以此建立方案效用值计算的线性目标规划模型,从而实现备选方案的评价和排序.实例研究表明了所提出方法的可行性和有效性.     

Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem

Evaluating and selecting a suitable supplier is a complex problem which involves a number of different criteria. In literature, there are various multi-criteria decision making (MCDM) methods available with their own characteristic features. The focus of this study is intuitionistic fuzzy (IF) MCDM methods which have attracted much attention from academics and practitioners in recent years. IF sets are widely used to tackle imprecise and uncertain decision information in decision making due to their capability of accommodating the hesitation in human decision processes. This study proposes a new integrated methodology that is used for the first time in the literature. This approach consists of intuitionistic fuzzy analytic hierarchy process (IFAHP), an MCDM technique, for determining the weights of supplier evaluation criteria, and the concept of intuitionistic fuzzy axiomatic design (IFAD) principles for ranking competing supplier alternatives with respect to their overall performance. Decision makers’ assessments and opinions are extended to the IF environment in this approach and furthermore, the group decision making (GDM) approach is utilized in order to overcome uncertainties and vagueness, minimize the partiality of decision process and to avoid bias. This study contributes to supplier selection and IF sets literature by providing a combined framework based on IFAHP and IFAD methodology for the first time. To assess the validity of the proposed integrated IF MCDM approach, a case study from Turkey is provided. This study can be useful to researchers in better understanding the supplier selection problem theoretically, as well as to organizations in designing better satisfying supplier evaluation systems.     

Performance comparison based on customer relationship management using analytic network process

Customer relationship management (CRM) is a multi-perspective business paradigm which aims maximizing the benefits gained from relationships with customers. The aim of this paper is to compare the CRM performances of e-commerce firms using a multiple criteria decision making (MCDM) approach. Analytical network process (ANP) is a MCDM methodology which can take the inner and outer dependencies among multiple criteria into consideration. As there are dependencies among CRM performance evaluation criteria, ANP is used for comparing the CRM performances of the e-commerce firms under consideration. A sensitivity analysis also provided in order to monitor the robustness of the proposed ANP framework to changes in the weights of evaluation criteria. To the authors’ knowledge, this will be the first study which evaluates CRM performance using ANP.     

Weapon System Capability Assessment under uncertainty based on the evidential reasoning approach

Weapon System Capability Assessment (WSCA) is the initial point of quantification of capabilities in the military capability planning (MCP). WSCA is often a multiple criteria decision making (MCDM) problem with both quantitative and qualitative information under uncertain environment. In this paper, the analysis process and algorithm for WSCA problem is proposed on the basis of belief structure (BS) model and evidential reasoning (ER) approach which were developed to deal with various types of uncertainties such as ignorance and subjectiveness. First of all, the WSCA criteria hierarchy is built by analyzing how the capability is measured. Secondly, a weapon system capability model is formulated using BS. Thirdly, both qualitative and quantitative information involved in capability measure are transformed into BSs by the data transformation algorithm based on rules. Then, the analytical ER approach is used to aggregate the capability measurement information from sub-capability criteria to top-capability criterion, and the assessed weapon systems are ranked and analyzed according to utility intervals. Finally, a case study of real Main Battle Tank capability assessment is explored to show the proposed process for WSCA.