Using of Fuzzy SWARA and Fuzzy ARAS Methods to Solve Supplier Selection Problem

2020 ◽  
pp. 136-148
Author(s):  
Alptekin Ulutas
Keyword(s):  
Supplier Selection ◽  
Integrated Model ◽  
Selection Problem ◽  
Ratio Analysis ◽  
Imprecise Data ◽  
Extant Literature ◽  
Selection Problems ◽  
The Past ◽  
Proposed Model ◽  
Supplier Selection Problem

As the performance of suppliers directly or indirectly affects the performance of the companies with which they engage, working with the most suitable supplier has become the key to success for companies. When solving the supplier selection problem, many different criteria involving qualitative and quantitative criteria are considered. Therefore, the supplier selection problem is considered an MCDM problem. These criteria can include uncertain and imprecise data. Additionally, the judgments of many managers are considered in supplier selection problems. Thus, in this chapter, a fuzzy group integrated model including Fuzzy SWARA (step-wise weight assessment ratio analysis) and Fuzzy ARAS (additive ratio assessment) is proposed to select the best supplier. This study contributes to the extant literature since these two methods were not used in the past to solve any problems together. The proposed model is applied to a Turkish textile company.

A multi-objective supplier selection model for green supply chain network under uncertainty

2018 ◽  
Vol 13 (3) ◽  
pp. 605-625 ◽  
Author(s):  
Mohammad Khalilzadeh ◽  
Hadis Derikvand
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Objective Function Value ◽  
Selection Problem ◽  
Green Supply Chain ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Objective Model ◽  
Supplier Selection Problem

Purpose Globalization of markets and pace of technological change have caused the growing importance of paying attention to supplier selection problem. Therefore, this study aims to choose the best suppliers by providing a mathematical model for the supplier selection problem considering the green factors and stochastic parameters. This paper aims to propose a multi-objective model to identify optimal suppliers for a green supply chain network under uncertainty. Design/methodology/approach The objective of this model is to select suppliers considering total cost, total quality parts and total greenhouse gas emissions. Also, uncertainty is tackled by stochastic programming, and the multi-objective model is solved as a single-objective model by the LP-metric method. Findings Twelve numerical examples are provided, and a sensitivity analysis is conducted to demonstrate the effectiveness of the developed mathematical model. Results indicate that with increasing market numbers and final product numbers, the total objective function value and run time increase. In case that decision-makers are willing to deal with uncertainty with higher reliability, they should consider whole environmental conditions as input parameters. Therefore, when the number of scenarios increases, the total objective function value increases. Besides, the trade-off between cost function and other objective functions is studied. Also, the benefit of the stochastic programming approach is proved. To show the applicability of the proposed model, different modes are defined and compared with the proposed model, and the results demonstrate that the increasing use of recyclable parts and application of the recycling strategy yield more economic savings and less costs. Originality/value This paper aims to present a more comprehensive model based on real-world conditions for the supplier selection problem in green supply chain under uncertainty. In addition to economic issue, environmental issue is considered from different aspects such as selecting the environment-friendly suppliers, purchasing from them and taking the probability of defective finished products and goods from suppliers into account.

The multi-objective supplier selection problem with fuzzy parameters and solving the order allocation problem with coverage

2020 ◽  
Vol 15 (3) ◽  
pp. 705-725
Author(s):  
Mohammad Khalilzadeh ◽  
Arya Karami ◽  
Alborz Hajikhani
Keyword(s):  
Supply Chain ◽  
Combinatorial Optimization ◽  
Supplier Selection ◽  
Multiple Objectives ◽  
Selection Problem ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model ◽  
Supplier Selection Problem ◽  
Fuzzy Parameters

Purpose This study aims to deal with supplier selection problem. The supplier selection problem has significantly become attractive to researchers and practitioners in recent years. Many real-world supply chain problems are assumed as multiple objectives combinatorial optimization problems. Design/methodology/approach In this paper, the authors propose a multi-objective model with fuzzy parameters to select suppliers and allocate orders considering multiple periods, multiple resources, multiple products and two-echelon supply chain. The objective functions consist of total purchase costs, transportation, order and on-time delivery, coverage and the weights of suppliers. Distance-based partial and general coverage of suppliers makes the number of orders of products more realistic. In this model, the weights of suppliers are determined by fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, as a multi-criteria decision analysis method, in the objective function. Also, the authors consider the parameters related to delays as triangular fuzzy numbers. Findings A small-sized numerical example is provided to clearly show the proposed model. The exact epsilon constraint method is used to solve this given multi-objective combinatorial optimization problem. Subsequently, the sensitivity analysis is conducted to testify the proposed model. The obtained results demonstrate the validity of the proposed multiple objectives mixed integer mathematical programming model and the efficiency of the solution approach. Originality/value In real-life situations, supplier selection parameters are uncertain and incomplete. Hence, the fuzzy set theory is used to tackle uncertainty. In this paper, a multi-objective supplier selection problem is formulated taking into consideration the coverage of suppliers and suppliers’ weights. Integrating coverage of suppliers to select and allocate the order to them can be mentioned as the main contribution of this study. The proposed model considers the delay from suppliers as fuzzy parameters.

scholarly journals Credibility-Based Biobjective Fuzzy Optimization for Supplier Selection Problem with Disruption

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Xuejie Bai
Keyword(s):  
Optimization Model ◽  
Supplier Selection ◽  
Fuzzy Optimization ◽  
Selection Problem ◽  
Equivalent Model ◽  
Main Concern ◽  
Total Quality ◽  
Fuzzy Variables ◽  
Proposed Model ◽  
Supplier Selection Problem

This paper addresses a supplier selection problem in which a buyer procures multiple products from multiple suppliers under disruption risk. The problem is formulated as a new credibility-based biobjective fuzzy optimization model. In the proposed model, cost, capacity, and demand are characterized by fuzzy variables with known possibility distributions. The objectives of our model are to maximize the total quality of purchased products and minimize the expected total cost. Two credibility constraints are used to guarantee that the chance about the supplier capacity and buyer demand can satisfy the predetermined levels. The main concern in solving the optimization model is to calculate the expected value of the objective function and the credibility in the constraints. When the key parameters are mutually independent triangular fuzzy variables, the expected cost objective and credibility constraints can be transformed into their equivalent forms. Taking advantage of the structural characteristics of the equivalent model, the goal programming method is employed to solve the supplier selection model, which can be solved by conventional optimization method. At last, some numerical experiments have been performed to illustrate the effectiveness of the proposed model and solution strategy.

scholarly journals Multicriteria supplier selection using acceptability analysis

2019 ◽  
Vol 11 (10) ◽  
pp. 168781401988371
Author(s):  
Qian Zhang ◽  
Kin Keung Lai ◽  
Jorome Yen
Keyword(s):  
Decision Making ◽  
Supplier Selection ◽  
Input Data ◽  
Full Rank ◽  
Interval Data ◽  
Selection Problem ◽  
Group Consensus ◽  
Extant Literature ◽  
Effective Instrument ◽  
Supplier Selection Problem

The supplier selection problem has been largely explored in the extant literature and attracted considerable attention of academics and purchasing managers. Practical supplier selection problem is usually revolved around multicriteria and a committee of experts. However, even using the exact values of the input data, certain experts may generate uncertain evaluation results on a supplier, because the exact weights with respect to each criterion are extremely difficult to reach a group consensus. In this article, first the interval data to describe all experts’ evaluation on all suppliers are formulated and then a stochastic multicriteria acceptability analysis (SMAA-2) is applied to provide a full rank of all candidate suppliers. SMAA-2 method is considered as an effective instrument to deal with stochastic decision-making problems. The rank acceptability indices and holistic rank indices are obtained to support the supplier selection. A numerical example drawn from the previous paper is recalculated to show the effectiveness of our approach.

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