Green supplier selection and order allocation: a nonlinear stochastic model

Hashemzahi, Pooria; Azadnia, Amir Hossein; Galankashi, Masoud Rahiminezhad; Helmi, Syed Ahmad; Rafiei, Farimah Mokhatab

doi:10.1504/ijvcm.2020.106821

Cited by 15 publications

(10 citation statements)

References 49 publications

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“…In modern business operations, green supply chain management has become a concern for an environmentally sustainable supply chain [17,18,19,20,21]. Unlike the sustainable supply chain that encompasses the social, economic, and environmental dimensions, the green supply chain categorically emphasizes the environmental process flow, focusing mainly on reducing wastes and greenhouse gas emissions during production [22,23].…”

Section: The Green Supply Chainmentioning

confidence: 99%

Application of multi-attribute decision-making method based on fuzzy influence diagram in green supplier selection

Peng,

Zhao,

Cai

et al. 2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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The external business environment exhibits many uncertainties with the continuing changes in technological progress, political environment, and consumption behavior. This has made companies recognize the significance of addressing environmental issues to remain competitive in the modern global market. Thus, much attention is given to the supply chain to ensure environmental factors are considered during supplier selection. However, the process of selecting the right suppliers to enable green supply chain management is proving difficult. This paper presents a novel approach using fuzzy influence diagrams to identify key green supplier selection criteria, combined with multi-criteria decision making to evaluate alternatives. The fuzzy influence diagram derives priority supplier capabilities by modeling interrelationships and uncertainties. The multi-criteria technique then ranks suppliers based on prospect values. A case study of furniture manufacturer supplier selection validates the proposed method. Compared to existing models, this integrated approach reduces subjective weight bias and improves handling of complex criteria dependencies and uncertainties. The results demonstrate state-of-the-art performance in identifying ideal green suppliers. This technique can enhance sustainability efforts across supply chains.

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Section: The Green Supply Chainmentioning

confidence: 99%

Application of multi-attribute decision-making method based on fuzzy influence diagram in green supplier selection

Peng,

Zhao,

Cai

et al. 2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…In this study, the half-lengths of the intervals are determined by fuzzy membership functions. Hashemzahi et al [10] studied supplier selection and order allocation with stochastic demand. In the first step, a fuzzy hierarchy analysis process (FAHP) is performed to determine the weights and select suppliers based on economic and environmental criteria, and in the second step, a multi-objective nonlinear programming (MONLP) is used to solve the problem using a genetic algorithm (GA).…”

Section: Stochastic Modelsmentioning

confidence: 99%

Modeling a stochastic multi-objective, multi-site, multi-mode, and multi-item supplier selection and order allocation problem

Seifbarghy,

Yargholi,

Hamidi

2024

JIMO

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Supplier selection and order allocation are important issues in supply chain management. Several features differentiate this research from other studies. The two-echelon supply chain network studied in this research comprises of suppliers and production centers. The supply chain model is a multisite, multi-mode, and multi-item model which incorporates multiple sites for production centers, multiple transportation modes, and multiple purchased items from suppliers. In this model, demand, delivery delay, and percentage of defective items are deemed stochastic parameters. It is assumed that these uncertain parameters have normal distributions with known means and standard deviations. The most significant contribution of this research is incorporating uncertainty with two indicators of mean and standard deviation into a multi-objective model for the supplier selection and order allocation problem. To deal with stochastic demand, the chance constraint approach is applied. The multi-objective optimization model aims to minimize purchasing cost, delivery delay, and defective items. To solve this model, first, a mixed-integer formulation is established to address the order allocation problem. Then, after using the epsilon constraint method, the Torabi-Hassini approach is used to transform the multi-objective model into an equivalent single-objective model. Real data from poultry and dairy industries are used to evaluate the model performance. Sensitivity analysis shows that the most improvements in the objective functions occur when the means and standard deviations of delivery delay and percentage of defective items for suppliers and transportation modes are decreased simultaneously.

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“…Furthermore, as there are several measures, metrics and criteria to assess and compare the performance of hospitals, it is critical to use fit, measurable and understandable criteria. Finally, it is very common for managers, practitioners, decision makers and people to make their expressions, judgments and comparisons in fuzzy environment instead of exact scales (Hashemzahi et al, 2020). Therefore, to address this gap, this study develops a methodology to investigate hospital selection criteria, prioritize them using an AHP and finally compares and ranks three hospitals using Fuzzy-TOPSIS methodology.…”

Section: Identification Of Research Gapmentioning

confidence: 99%

Hospital Selection Problem: An Integrated Analytic hierarchy process (AHP) and Fuzzy- TOPSIS Approach

Proceedings of the International Conference on Industrial Engineering and Operations Management

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This research provides a step by step procedure for hospital selection problem. Although the concept of hospital location selection, site selection and other related tools and techniques have been investigated in previous literature, hospital selection problem is less investigated. In addition, the problem is not adequately linked with Multiple-Criteria Decision Making (MCDM) approaches. To address the gap of previous literature, this research has been completed in three linked steps to achieve as follows. The first phase of this research aims to investigate potential criteria to be applied in hospital selection problem. To do so, it applies a literature review to find potential criteria of hospital selection. Following, as developed criteria should be fit with the problem, an Analytic Hierarchy Process (AHP) is applied to rank and finalize the developed criteria of previous phase. Finally, the obtained criteria of the second phase are applied to compare and select three potential hospitals by a Fuzzy-TOPSIS approach. According to the obtained results of the first phase, cost, knowledge and expertise, quality, communication, environment, reliability and fast service are potential criteria of hospital selection. In addition, the second phase shows that cost, quality, knowledge and expertise, environment and communication are the main decision making criteria of this research. Finally, the third phase provided final ranking of hospitals as A1>A2>A3.

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Green supplier selection and order allocation: a nonlinear stochastic model

Cited by 15 publications

References 49 publications

Application of multi-attribute decision-making method based on fuzzy influence diagram in green supplier selection

Application of multi-attribute decision-making method based on fuzzy influence diagram in green supplier selection

Modeling a stochastic multi-objective, multi-site, multi-mode, and multi-item supplier selection and order allocation problem

Hospital Selection Problem: An Integrated Analytic hierarchy process (AHP) and Fuzzy- TOPSIS Approach

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