A multi-level programming model for green supplier selection

PurposeIndustrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a compe...

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Published in:Management decision Vol. 59; no. 10; pp. 2496 - 2527
Main Authors: Gupta, Srikant, Chatterjee, Prasenjit, Yazdani, Morteza, Santibanez Gonzalez, Ernesto D.R
Format: Journal Article
Language:English
Published: London Emerald Publishing Limited 06.09.2021
Emerald Group Publishing Limited
Subjects:
Climate change
Efficiency
Flexibility
Manufacturing
Order quantity
Product design
Product development
Social responsibility
Suppliers
Supply chain management
Sustainable development
Green supplier selection
Multi-level programming problem
Forecasting
Green supply chain
Interval type-2 fuzzy numbers
ISSN:0025-1747, 1758-6070
Online Access:Get full text
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Abstract PurposeIndustrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.Design/methodology/approachIn this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.FindingsThis research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.Research limitations/implicationsThe proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.Practical implicationsThe proposed model is generic and can be applied for large-scale GSC environments with little modifications.Originality/valueNo prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.
AbstractList PurposeIndustrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.Design/methodology/approachIn this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.FindingsThis research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.Research limitations/implicationsThe proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.Practical implicationsThe proposed model is generic and can be applied for large-scale GSC environments with little modifications.Originality/valueNo prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.
Author Gupta, Srikant
Chatterjee, Prasenjit
Santibanez Gonzalez, Ernesto D.R
Yazdani, Morteza
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  surname: Santibanez Gonzalez
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Issue 10
Keywords Green supplier selection
Multi-level programming problem
Forecasting
Green supply chain
Interval type-2 fuzzy numbers
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StartPage 2496
SubjectTerms Climate change
Efficiency
Flexibility
Manufacturing
Order quantity
Product design
Product development
Social responsibility
Suppliers
Supply chain management
Sustainable development
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