Multi-criteria Supplier Selection Problem Research Articles

A Procedure for Choosing among Different Solutions to the Multi-Criteria Supplier Selection Problem along with Two Solution Methods

A Procedure for Choosing among Different Solutions to the Multi-Criteria Supplier Selection Problem along with Two Solution Methods

A Decision Framework Based on Integration of DEMATEL, QFD, TOPSIS and VIKOR Approaches for Fuzzy Multi-Criteria Supplier Selection Problem

Continuous and effective communication with suppliers can play a very important role in the strength and balance of the supply chain and create competitive advantages for manufacturers. Choosing the right supplier is crucial while choosing the wrong suppliers can affect the financial and operational activities of an organization negatively. In this study, an attempt is made to tackle a fuzzy multi-criteria supplier selection problem of a detergent factory as a case study. Moreover, the objective of the investigation is to put forth a model within a fully ambiguous setting, as this allows for the model’s practicality in addressing real-world issues and achieving more precise outcomes. This problem is modeled as ranking 5 suppliers according to 15 criteria selected from the literature. For this aim, a stepwise solution methodology consisting of fuzzy DEMATEL, fuzzy QFD, fuzzy TOPSIS, and fuzzy VIKOR approaches is developed for the first time in order to cover all aspects of the problem e.g., the fuzzy DEMATEL is used to select the effective criteria and eliminate the unnecessary ones, the fuzzy QFD is used to determine importance weight of each selected criterion, and the fuzzy TOPSIS and VIKOR approaches are used to rank the suppliers separately. According to the computations made on the data of the case study, the ranking of the suppliers is obtained by both of the methods while some similarities of the obtained rankings are discovered. Consequently, this study introduces a hybrid model that combines highly favorable decision-making methodologies specifically designed for a fully ambiguous environment.

Multi-criteria supplier selection problem with fuzzy demand: a newsvendor model

Multi-criteria supplier selection problem with fuzzy demand: a newsvendor model

A supplier selection model using group decision-making systems under multiple criteria considering regret factor

In the current literature, supplier selection problem is defined as "the process by which companies identify, evaluate and choose the supplier to become part of their supply chain." Indeed, supplier selection is a multi-criteria decision-making (MCDM) problem affected by several conflicting factors (i.e., price, delivery time, etc.). However, some authors claim that this problem is typically a multi-criteria group decision-making (MCGDM) problem. On the other side, incorporating behavioural aspects, such as feelings of regret and rejoicing, into MCDM model intriguing area has been introduced. Therefore, the question typically posed is whether emotional feelings (particularly, regret factor) can be modelled systematically for an established MCGDM model? In this paper, we propose a new TOPSIS-based algorithm for determining regret measure in the group decision-making process. So, in the first step, the proposed method is used, the results of this step are inputs for the existing group TOPSIS methodology in the next step. Finally, to clarify our proposed algorithm, a numerical example involves a multi-criteria supplier selection problem in a supply chain context is discussed. The results indicate that the accuracy of the decision has been increased by considering the regret factors.

A supplier selection model using group decision-making systems under multiple criteria considering regret factor

In current literature, supplier selection problem is defined as the process by which companies identify, evaluate and choose supplier to become part of their supply chain. Indeed, supplier selection is a multi-criteria decision-making (MCDM) problem affected by several conflicting factors (i.e., price, delivery time, etc.). However, some authors claim that this problem is typically a multi-criteria group decision-making (MCGDM) problem. On other side, incorporating behavioural aspects, such as feelings of regret and rejoicing, into MCDM model intriguing area has been introduced. Therefore, question typically posed is whether emotional feelings (particularly, regret factor) can be modelled systematically for an established MCGDM model? In this paper, we propose a new TOPSIS-based algorithm for determining regret measure in group decision-making process. So, in first step, proposed method is used, results of this step are inputs for existing group TOPSIS methodology in next step. Finally, to clarify our proposed algorithm, a numerical example involves a multi-criteria supplier selection problem in a supply chain context is discussed. The results indicate that accuracy of decision has been increased by considering regret factors.

A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization

Nowadays, with the development of information technology and economic globalization, supplier selection problem gets more and more attraction. The recent literature shows huge interest in hybrid artificial intelligence (AI)-based models for solving supplier selection problem. In this paper, to solve a multi-criteria supplier selection problem, based on genetic algorithm (GA) and ant colony optimization (ACO), hybrid algorithm of GA and ACO is developed. It combines merits of GA with great global converging rate and ACO with parallelism and effective feedback. A numerical experiment was conducted to optimize parameters and to analyze and compare the performance of the original and hybrid algorithms. Results demonstrate the quality and efficiency improvement of new integrated algorithm, verifying its feasibility and effectiveness. It is an innovative pilot research to leverage hybrid AI-based algorithm of GA and ACO to settle the supplier selection problem, which not only makes a clear methodological contribution for optimization algorithm research, but also can be served as a decision tool and provide management reference for companies.

An a posteriori decision support methodology for solving the multi-criteria supplier selection problem

This research presents a novel, state-of-the-art methodology for solving a multi-criteria supplier selection problem considering risk and sustainability. It combines multi-objective optimization with the analytic network process to take into account sustainability requirements of a supplier portfolio configuration. To integrate ‘risk’ into the supplier selection problem, we develop a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: (1) minimizing the purchasing costs, (2) selecting the supplier portfolio with the highest logistics service, (3) minimizing the supply risk, and (4) ordering as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm that analytically computes a set of efficient solutions and provides graphical decision support through a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto-optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different objectives of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company's strategy best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.

An improved-based TOPSIS method in interval-valued intuitionistic fuzzy environment

In supply chain management, one of the important criteria is the selection of suitable suppliers. It is the quality and the timely supply of the different items by the supplier, so that the customers are satisfied to maintain the success of a business. In this paper, we considered a fuzzy multi-objective linear programming solution model for a supplier selection problem. In the model, first, the objectives and constraints are weighted using TOPSIS method; and then, using these weights, membership function for objective functions are constructed. Now, applying the membership function and weights, the multi-objective programming problem is transformed to an equivalent linear programming problem for getting the solution of the multi-criteria supplier selection problem. The proposed algorithm provides better performance results in comparison to other methods available in the literature.

An a Posteriori Multi-Objective Supplier Portfolio Selection Approach Under Risk and Sustainability Considerations

This research presents a novel, state-of-the-art methodology for solving the multi-criteria supplier selection problem under risk and sustainability considerations. The approach combines multi-objective optimization with the analytic network process to meet the requirements for supplier portfolio configuration with sustainability considerations. To integrate the aspect ‘risk’ into the supplier selection problem, this research develops a multi-objective optimization model based on the investment portfolio theory introduced by Markowitz. The proposed model is a non-standard portfolio selection problem with four objectives: to minimize the purchasing costs, to select the supplier portfolio with the highest logistics service, to minimize the supply risk, and to order as much as possible from those suppliers with outstanding sustainability performance. The optimization model, which has three linear and one quadratic objective function, is solved by an algorithm which analytically computes a set of efficient solutions and provides graphical decision support by a visualization of the complete and exactly-computed Pareto front (a posteriori approach). The possibility of computing all Pareto optimal supplier portfolios is beneficial for decision makers as they can compare all optimal solutions at once, identify the trade-offs between the criteria, and study how the different aspects of supplier portfolio configuration may be balanced to finally choose the composition that satisfies the purchasing company’s strategy at best. The approach has been applied to a real-world supplier portfolio configuration case to demonstrate its applicability and to analyze how the consideration of sustainability requirements may affect the traditional supplier selection and purchasing goals in a real-life setting.

Improving preference assessment in TOPSIS method for multi-criteria supplier selection problem

The conventional TOPSIS method attempts to choose alternatives that simultaneously have the shortest distance from the positive ideal solution and the farthest distance from the negative-ideal alternative. However, this assumption may not be always valid. It is argued here that both distances must be considered simultaneously. Since, in order to effectively solve this limitation, this paper proposed an extended TOPSIS method to deal with the supplier selection problem. In the proposed method, a final ranking of the feasible alternatives is the intersection of the two descending and ascending ranking, which in TOPSIS method show them by distance to ideal and negative-ideal solution respectively. A comparative analysis is performed, and the proposed method seems to be more satisfactory than the conventional method for solving decision problems. So, in 49% of times (out of 100 cases), two best-ranked alternatives are incomparable, and there is no clear evidence in favour of either of alternatives. However, conventional TOPSIS introduced only one of them as the best (optimal) alternative.

Improving preference assessment in TOPSIS method for multi-criteria supplier selection problem

The conventional TOPSIS method attempts to choose alternatives that simultaneously have the shortest distance from the positive ideal solution and the farthest distance from the negative-ideal alternative. However, this assumption may not be always valid. It is argued here that both distances must be considered simultaneously. Since, in order to effectively solve this limitation, this paper proposed an extended TOPSIS method to deal with the supplier selection problem. In the proposed method, a final ranking of the feasible alternatives is the intersection of the two descending and ascending ranking, which in TOPSIS method show them by distance to ideal and negative-ideal solution respectively. A comparative analysis is performed, and the proposed method seems to be more satisfactory than the conventional method for solving decision problems. So, in 49% of times (out of 100 cases), two best-ranked alternatives are incomparable, and there is no clear evidence in favour of either of alternatives. However, conventional TOPSIS introduced only one of them as the best (optimal) alternative.

Simulation based fuzzy TOPSIS approach for group multi-criteria supplier selection problem

Supplier selection is nowadays one of the critical topics in supply chain management. This paper presents a new decision making approach for group multi-criteria supplier selection problem, which clubs supplier selection process with order allocation for dynamic supply chains to cope market variations. More specifically, the developed approach imitates the knowledge acquisition and manipulation in a manner similar to the decision makers who have gathered considerable knowledge and expertise in procurement domain. Nevertheless, under many conditions, exact data are inadequate to model real-life situation and fuzzy logic can be incorporated to handle the vagueness of the decision makers. As per this concept, fuzzy-AHP method is used first for supplier selection through four classes (CLASS I: Performance strategy, CLASS II: Quality of service, CLASS III: Innovation and CLASS IV: Risk), which are qualitatively meaningful. Thereafter, using simulation based fuzzy TOPSIS technique, the criteria application is quantitatively evaluated for order allocation among the selected suppliers. As a result, the approach generates decision-making knowledge, and thereafter, the developed combination of rules order allocation can easily be interpreted, adopted and at the same time if necessary, modified by decision makers. To demonstrate the applicability of the proposed approach, an illustrative example is presented and the results analyzed.

Multiple criteria group decision-making with generalized interval-valued fuzzy numbers based on signed distances and incomplete weights

Decision-making information provided by decision makers is often imprecise or uncertain, due to lack of data, time pressure, or the decision makers’ limited attention and information-processing capabilities. Interval-valued fuzzy sets are associated with greater imprecision and more ambiguity than are ordinary fuzzy sets. For these reasons, this paper presents a signed distance-based method for handling fuzzy multiple-criteria group decision-making problems in which individual assessments are provided as generalized interval-valued trapezoidal fuzzy numbers, and the information about criterion weights are not precisely but partially known. First, concerning the relative importance of decision makers and the group consensus of fuzzy opinions, all individual decision opinions were aggregated into group opinions using a hybrid average with weighted averaging and signed distance-based ordered weighted averaging operations. Next, considering a decision situation with incomplete weight information of criteria, an integrated programming model was developed to estimate criterion weights and to order the priorities of various alternatives based on signed distances. In addition, several deviation variables were introduced to mitigate the effect of inconsistent evaluations on the importance of criteria. Finally, the feasibility of the proposed method is illustrated by a numerical example of a multi-criteria supplier selection problem. Furthermore, a comparative analysis with other methods was conducted to validate the effectiveness and applicability of the proposed methodology.

An improved voting analytic hierarchy process–data envelopment analysis methodology for suppliers selection

Selecting an appropriate supplier is now one of the most important decisions of the purchasing department. Liu and Hai (Liu, F.H.F. and Hai, H.L., 2005. The voting analytic hierarchy process method for selecting supplier. International Journal of Production Economics, 97, 308–317) proposed a voting analytic hierarchy process method for selecting suppliers. Despite its many advantages, Liu and Hai's model (LH-model) has some shortcomings. In this article, the authors present an extended version of the LH-model for multi-criteria supplier selection problem. An illustrative example is presented to compare the authors' model and the LH-model.

Bivariate models of optimism and pessimism in multi-criteria decision-making based on intuitionistic fuzzy sets

This paper presents a useful method of relating optimism and pessimism to multiple criteria decision analysis in the context of intuitionistic fuzzy sets based on the unipolar bivariate model. We use eight point operators to estimate the adaptational outcome expectations of optimism and/or pessimism and then determine the net predisposition, the aggregated effect of positive and negative evaluations. A series of new net predispositions for bivariate evaluations are proposed for neutrality; for complete, moderate, and rational optimism; for complete, moderate, and rational pessimism; and for complete and moderate optimism–pessimism. The suitability function, which measures the overall evaluation of each alternative, is then presented. Because positive or negative leniency may exist, such that most of the criteria may be assigned unduly high or low ratings, respectively, we introduce deviation variables to mitigate the effects of such ratings on the apparent importance of various criteria. Based on the two objectives of maximal weighted suitability and minimal deviation values, an integrated programming model is used to compute the optimal weights for the criteria and the corresponding degrees of suitability of the alternative rankings. We establish flexible algorithms that incorporate both objective and subjective information to compute the optimal optimistic and pessimistic decisions. The proposed methods are illustrated and discussed using a numerical example, a multi-criteria supplier selection problem. Finally, an empirical study of job choices is conducted to establish the feasibility and applicability of the current method.

Configurable supply chain: framework, methodology and application

Dynamic nature of modern supply chains leads to frequent changes in the supply chain structure. Appropriate decision-modelling mechanisms are required to support decision-making in such an environment. This paper discusses general requirements for a methodology governing modelling of reconfigurable supply chain and develops supply chain configuration methodology according to these requirements. Two main features of the proposed methodology are information modelling and hybrid modelling. Information modelling enables systematic formalisation of modelling problems and processes. Hybrid modelling combining optimisation and simulation allows for comprehensive evaluation of alternative configurations subject to dynamic and stochastic factors. The methodology is applied to deal with a multicriteria supplier selection problem. Specific optimisation and simulation models are developed for this particular problem. These models are integrated using meta-modelling.

An efficient and simple model for multiple criteria supplier selection problem

Simply looking for vendors offering the lowest prices is not “efficient sourcing” any more. Selection of suppliers is a multiple criteria decision. We propose a weighted linear program for the multi-criteria supplier selection problem. In addition to mathematical formulation, this paper studies a transformation technique which enables our proposed model to be solved without an optimizer. The model for multi-criteria supplier selection problem can be easily implemented with a spreadsheet package. The model can be widely applied to practical situations and does not require the user with any optimization background.