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,

• 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",
• 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",
• journal = "Journal of Cleaner Production",
• volume = "231",
• pages = "1136--1148",
• year = "2019",
• ISSN = "0959-6526",
• DOI = "doi:10.1016/j.jclepro.2019.05.300",
• URL = "http://www.sciencedirect.com/science/article/pii/S0959652619318360",
• keywords = "genetic algorithms, genetic programming, Carbon emission, Production emissions, Emission elasticity, Green technology, Advanced multi-gene genetic programming",
• 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