Application of Multi-Objective Optimization Based on Genetic Algorithm for Sustainable Strategic Supplier Selection under Fuzzy Environment
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hdl:2117/104795
Tipus de documentArticle
Data publicació2017-05
EditorOmniaScience
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Reconeixement-NoComercial 3.0 Espanya
Abstract
Purpose: The incorporation of environmental objective into the conventional supplier selection
practices is crucial for corporations seeking to promote green supply chain management (GSCM).
Challenges and risks associated with green supplier selection have been broadly recognized by
procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS)
problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply
chain environment. In this empirical study, a mathematical model with fuzzy coefficients is
considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model
is developed to tackle this problem.
Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are
typically multi-objectives in nature and it is an important part of green production and supply
chain management for many firms. The proposed uncertain model is transferred into
deterministic model by applying the expected value measure (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multiobjective
optimization model for minimizing lean cost, maximizing sustainable service and
greener product quality level. Finally, a mathematical case of textile sector is presented to
exemplify the effectiveness of the proposed model with a sensitivity analysis.
Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both
the theoretical and practical research related to multi-objective optimization as well as in the study
of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results
suggest that decision makers tend to select strategic supplier first then enhance the sustainability.
Research limitations/implications: Although the fuzzy expected value model (EVM) with
fuzzy coefficients constructed in present research should be helpful for solving real world
problems. A detailed comparative analysis by using other algorithms is necessary for solving
similar problems of agriculture, pharmaceutical, chemicals and services sectors in future.
Practical implications: It can help the decision makers for ordering to different supplier for
managing supply chain performance in efficient and effective manner. From the procurement and
engineering perspectives, minimizing cost, sustaining the quality level and meeting production
time line is the main consideration for selecting the supplier. Empirically, this can facilitate
engineers to reduce production costs and at the same time improve the product quality.
Originality/value: In this paper, we developed a novel multi-objective programming model
based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy
environment. The algorithm was tested and applied to solve a real case of textile sector in
Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness
of our proposed model.
CitacióHashim, M. [et al.]. Application of Multi-Objective Optimization Based on Genetic Algorithm for Sustainable Strategic Supplier Selection under Fuzzy Environment. "Journal of Industrial Engineering and Management", Maig 2017, vol. 10, núm. 2, p. 188-212.
Dipòsit legalB-28744-2008
ISSN2013-0953
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