Evolutionary framework design in formulation of decision support models for production emissions and net profit of firm: Implications on environmental concerns of supply chains
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- @Article{GARG:2019:JCP,
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author = "Akhil Garg and Liang Gao and Wei Li and
Surinder Singh and Xiongbin Peng and Xujian Cui and Z. Fan and
Harpreet Singh and C. M. M. Chin",
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title = "Evolutionary framework design in formulation of
decision support models for production emissions and
net profit of firm: Implications on environmental
concerns of supply chains",
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journal = "Journal of Cleaner Production",
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volume = "231",
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pages = "1136--1148",
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year = "2019",
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ISSN = "0959-6526",
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DOI = "doi:10.1016/j.jclepro.2019.05.300",
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URL = "http://www.sciencedirect.com/science/article/pii/S0959652619318360",
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keywords = "genetic algorithms, genetic programming, Carbon
emission, Production emissions, Emission elasticity,
Green technology, Advanced multi-gene genetic
programming",
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abstract = "There have been increased investments in cleaner
technologies and adoption of a voluntarily limit on
transportation emissions by the global firms to handle
the environmental concerns of supply chains and to
increase demand for finished goods. Consequences are
the reduction in net profit for the firm. To address
this trade-off between the net profit and environmental
concerns, the formulation and optimization of a compact
model are needed. Development of these models requires
a thorough understanding of the nature of the impact of
three inputs (investment coefficient, penalty per unit
emission and customer's emission elasticity) on
production emissions and net profit. Past studies
revealed that a compact model comprising the
interactive effect of these inputs on the production
emissions and net profit is not yet formulated.
Therefore, this study illustrates the development of an
evolutionary framework of an advanced multi-gene
genetic programming in the formulation of functional
expressions for the net profit and production emissions
based on the three inputs (investment coefficient,
penalty per unit emission and customer's emission
elasticity) of the monopolist firm. The sensitivity and
parametric based 2-D analysis determine the
relationships and found that the penalty per unit
emission is dominant input for reducing emissions and
maintaining net profit simultaneously. The contribution
of this work lies in designing of an evolutionary
framework in the development of empirical explicit
expressions, which can easily be optimized analytically
to keep production emissions and net profit balanced",
- }
Genetic Programming entries for
Akhil Garg
Liang Gao
Wei Li
Surinder Singh
Xiongbin Peng
Xujian Cui
Z Fan
Harpreet Singh
C M M Chin
Citations