Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment
Abstract: 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 mesurement (EVM) and genetic algorithm with
weighted sum approach for solving the multi-objective problem. This research
focus on a multi-objective 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.
Keywords: Multi-objective
programming, Sustainable supplier selection, Expected value measure, Genetic
algorithm, Textile sector
Author: Muhammad Hashim,
Muhammad Nazam, Liming Yao, Sajjad Ahmad Baig, Muhammad Abrar, Muhammad
Zia-ur-Rehman
Journal Code: jptindustrigg170008