April  2015, 11(2): 515-528. doi: 10.3934/jimo.2015.11.515

Optimal selection of cleaner products in a green supply chain with risk aversion

1. 

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

2. 

Department of Intelligence and Informatics, Konan University, 8-9-1 Okamoto, Kobe 658-8501

3. 

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190

Received  February 2013 Revised  April 2014 Published  September 2014

In this paper, we investigate the selection of cleaner products with the consideration of the tradeoff between risk and the return of players in two different of supply chain structures: a vertically integrated structure and a decentralized setting. In an integrated supply chain, the price of cleaner products is decided according to the maximum utility for the whole supply chain, while the retailer offers their price with respect to their own maximum utility in a decentralized setting. A numerical example of a green supply chain of household electrical appliances in China is presented to illustrate related issues. Finally, conclusions are drawn and some topics for future work are suggested.
Citation: Gang Xie, Wuyi Yue, Shouyang Wang. Optimal selection of cleaner products in a green supply chain with risk aversion. Journal of Industrial & Management Optimization, 2015, 11 (2) : 515-528. doi: 10.3934/jimo.2015.11.515
References:
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M. S. Jørgensen, et al., Green technology foresight about environmentally friendly products and materials: The challenges from nanotechnology, biotechnology and ICT,, Danish Ministry of the Environment, 34 (2006).   Google Scholar

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E. Kikuchi, Y. Kikuchi and M. Hirao, Analysis of risk trade-off relationships between organic solvents and aqueous agents: case study of metal cleaning processes,, J. Clean. Prod., 19 (2011), 414.  doi: 10.1016/j.jclepro.2010.05.021.  Google Scholar

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B. Mansoornejad, V. Chambost and P. Stuart, Integrating product portfolio design and supply chain design for the forest biorefinery,, Comput. Chem. Eng., 34 (2010), 1497.  doi: 10.1016/j.compchemeng.2010.02.004.  Google Scholar

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[26]

J. Sarkis, A strategic decision framework for green supply chain management,, J. Clean. Prod., 11 (2003), 397.  doi: 10.1016/S0959-6526(02)00062-8.  Google Scholar

[27]

K. Shang, C. Lu and S. Li, A taxonomy of green supply chain management capability among electronics-related manufacturing firms in Taiwan,, J. Environ. Manag., 91 (2010), 1218.  doi: 10.1016/j.jenvman.2010.01.016.  Google Scholar

[28]

A. Tversky and D. Kahneman, Judgment under uncertainty: Heuristics and biases,, Sci., 185 (1974), 1124.   Google Scholar

[29]

F. Wirl, Energy conservation, expectations and uncertainty,, Energy Econ., 30 (2008), 1957.  doi: 10.1016/j.eneco.2007.12.001.  Google Scholar

[30]

G. Xie, Modeling Decision Processes of a Green Supply Chain with Regulation on Energy Saving Level,, Comput. Oper. Res., ().  doi: 10.1016/j.cor.2013.11.020.  Google Scholar

[31]

G. Xie, S. Wang and K. K. Lai, Quality improvement in competing supply chains,, Int. J. Prod. Econ., 134 (2011), 262.  doi: 10.1016/j.ijpe.2011.07.007.  Google Scholar

[32]

G. Xie, S. Wang and K. K. Lai, Optimal $\beta_k$-stable interval in VPRS-based group decision-making: A further application,, Expert Syst. Appl., 38 (2011), 13757.   Google Scholar

[33]

G. Xie, S. Wang, Y. Zhao and K. K. Lai, Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study,, Appl. Soft Comput., 13 (2013), 2232.  doi: 10.1016/j.asoc.2013.02.002.  Google Scholar

[34]

G. Xie, W. Yue, W. Liu and S. Wang, Risk based selection of cleaner products in a green supply chain,, Pac. J. Optim., 8 (2012), 473.   Google Scholar

[35]

G. Xie, W. Yue, S. Wang and K. K. Lai, Quality investment and price decision in a risk-averse supply chain,, Eur. J. Oper. Res., 214 (2011), 403.  doi: 10.1016/j.ejor.2011.04.036.  Google Scholar

[36]

G. Xie, W. Yue, S. Wang and K. K. Lai, Dynamic risk management in petroleum project investment based on a variable precision rough set model,, Technol. Forecast. Soc. Chang., 77 (2010), 891.  doi: 10.1016/j.techfore.2010.01.013.  Google Scholar

[37]

G. Xie, J. Zhang, K. K. Lai and L. Yu, Variable precision rough set for group decision-making: An application,, Int. J. Approx. Reason., 49 (2008), 331.  doi: 10.1016/j.ijar.2007.04.005.  Google Scholar

[38]

G. Xie, Y. Zhao, M. Jiang and N. Zhang, A novel ensemble learning approach for corporate financial distress forecasting in fashion and textiles supply chains,, Math. Probl. Eng., (2013).  doi: 10.1155/2013/493931.  Google Scholar

[39]

Q. Zhu, J. Sarkis, J. J. Cordeiro and K. Lai, Firm-level correlates of emergent green supply chain management practices in the Chinese context,, Omega, 36 (2008), 577.  doi: 10.1016/j.omega.2006.11.009.  Google Scholar

show all references

References:
[1]

, Caution to risk in new energy automobile industry due to the uncertainty of support policy (in Chinese),, 2010. Available from: , ().   Google Scholar

[2]

, Qingdao Haier: Investment in technological improvement project for 3 million energy conservative refrigerators by a subsidiary company,, 2010 (in Chinese). Available from: , ().   Google Scholar

[3]

J. M. Bloemhof-Ruwaard, L. N. Van Wassenhove, H. L. Gabel and P. M. Weaver, An environmental life cycle optimization model for the European pulp and paper industry,, Omega, 24 (1996), 615.  doi: 10.1016/S0305-0483(96)00026-6.  Google Scholar

[4]

C. Chen, Design for the environment: A quality-based model for green product development,, Manag. Sci., 47 (2001), 250.  doi: 10.1287/mnsc.47.2.250.9841.  Google Scholar

[5]

N. Chen, Credit risk is upraising in energy conservation and environment protection industry, Chinese Securities Journal,, 2011 (in Chinese). Available from: , ().   Google Scholar

[6]

G. Dowell, S. Hart and B. Yeung, Do corporate global environmental standards create or destroy market value?, Manag. Sci., 46 (2000), 1059.  doi: 10.1287/mnsc.46.8.1059.12030.  Google Scholar

[7]

B. Fischhoff, Acceptable Risk,, Cambridge University Press, (1981).   Google Scholar

[8]

F. Friedler, Process integration, modelling and optimisation for energy saving and pollution reduction,, Appl. Therm. Eng., 30 (2010), 2270.  doi: 10.1016/j.applthermaleng.2010.04.030.  Google Scholar

[9]

O. J. Hanssen, Sustainable product systems-experiences based on case projects in sustainable product development,, J. Clean. Prod., 7 (1999), 27.  doi: 10.1016/S0959-6526(98)00032-8.  Google Scholar

[10]

N. Harmancioglu, Portfolio of controls in outsourcing relationships for global new product development,, Ind. Mark. Manag., 38 (2009), 394.  doi: 10.1016/j.indmarman.2009.04.001.  Google Scholar

[11]

T. H. Ho and C. S. Tang, Product Variety Management: Research Advances,, Boston/Dordrecht/London: Kluwer Academic Publishers, (1998).   Google Scholar

[12]

C. A. Holloway, Decision Making Under Uncertainty: Models and Choices,, Prentice-Hall, (1979).   Google Scholar

[13]

R. A. Howard, Decision analysis: Practice and promise,, Manag. Sci., 34 (1988), 679.  doi: 10.1287/mnsc.34.6.679.  Google Scholar

[14]

M. S. Jørgensen, et al., Green technology foresight about environmentally friendly products and materials: The challenges from nanotechnology, biotechnology and ICT,, Danish Ministry of the Environment, 34 (2006).   Google Scholar

[15]

E. Kikuchi, Y. Kikuchi and M. Hirao, Analysis of risk trade-off relationships between organic solvents and aqueous agents: case study of metal cleaning processes,, J. Clean. Prod., 19 (2011), 414.  doi: 10.1016/j.jclepro.2010.05.021.  Google Scholar

[16]

Y. H. Kwak and K. S. LaPlace, Examining risk tolerance in project-driven organization,, Technovation, 25 (2005), 691.  doi: 10.1016/j.technovation.2003.09.003.  Google Scholar

[17]

M. D. Levi and B. R. Nault, Converting technology to mitigate environmental damage,, Manag. Sci., 50 (2004), 1015.  doi: 10.1287/mnsc.1040.0238.  Google Scholar

[18]

B. Mansoornejad, V. Chambost and P. Stuart, Integrating product portfolio design and supply chain design for the forest biorefinery,, Comput. Chem. Eng., 34 (2010), 1497.  doi: 10.1016/j.compchemeng.2010.02.004.  Google Scholar

[19]

H. M. Markowitz, Portfolio selection,, J. Financ., 7 (1952), 77.   Google Scholar

[20]

H. M. Markowitz, The optimization of a quadratic function subject to linear constraints,, Nav. Res. Logist. Q., 3 (1956), 111.  doi: 10.1002/nav.3800030110.  Google Scholar

[21]

D. Maxwell and R. van der Vorst, Developing sustainable products and services,, J. Clean. Prod., 11 (2003), 883.  doi: 10.1016/S0959-6526(02)00164-6.  Google Scholar

[22]

S. K. Moorthy, Product and price competition in a duopoly,, Mark. Sci., 7 (1988), 141.  doi: 10.1287/mksc.7.2.141.  Google Scholar

[23]

U. Nissen, A methodology for the development of cleaner products: The ideal-eco-product approach,, J. Clean. Prod., 3 (1995), 83.   Google Scholar

[24]

E. U. Olugu, K. Y. Wong and A. M. Shaharoun, Development of key performance measures for the automobile green supply chain,, Resour. Conserv. Recycl., 55 (2011), 567.  doi: 10.1016/j.resconrec.2010.06.003.  Google Scholar

[25]

H. Raiffa, Decision analysis: Introductory lectures on choices under uncertainty,, Journal of the Royal Statistical Society, 19 (1969), 180.  doi: 10.2307/2987280.  Google Scholar

[26]

J. Sarkis, A strategic decision framework for green supply chain management,, J. Clean. Prod., 11 (2003), 397.  doi: 10.1016/S0959-6526(02)00062-8.  Google Scholar

[27]

K. Shang, C. Lu and S. Li, A taxonomy of green supply chain management capability among electronics-related manufacturing firms in Taiwan,, J. Environ. Manag., 91 (2010), 1218.  doi: 10.1016/j.jenvman.2010.01.016.  Google Scholar

[28]

A. Tversky and D. Kahneman, Judgment under uncertainty: Heuristics and biases,, Sci., 185 (1974), 1124.   Google Scholar

[29]

F. Wirl, Energy conservation, expectations and uncertainty,, Energy Econ., 30 (2008), 1957.  doi: 10.1016/j.eneco.2007.12.001.  Google Scholar

[30]

G. Xie, Modeling Decision Processes of a Green Supply Chain with Regulation on Energy Saving Level,, Comput. Oper. Res., ().  doi: 10.1016/j.cor.2013.11.020.  Google Scholar

[31]

G. Xie, S. Wang and K. K. Lai, Quality improvement in competing supply chains,, Int. J. Prod. Econ., 134 (2011), 262.  doi: 10.1016/j.ijpe.2011.07.007.  Google Scholar

[32]

G. Xie, S. Wang and K. K. Lai, Optimal $\beta_k$-stable interval in VPRS-based group decision-making: A further application,, Expert Syst. Appl., 38 (2011), 13757.   Google Scholar

[33]

G. Xie, S. Wang, Y. Zhao and K. K. Lai, Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study,, Appl. Soft Comput., 13 (2013), 2232.  doi: 10.1016/j.asoc.2013.02.002.  Google Scholar

[34]

G. Xie, W. Yue, W. Liu and S. Wang, Risk based selection of cleaner products in a green supply chain,, Pac. J. Optim., 8 (2012), 473.   Google Scholar

[35]

G. Xie, W. Yue, S. Wang and K. K. Lai, Quality investment and price decision in a risk-averse supply chain,, Eur. J. Oper. Res., 214 (2011), 403.  doi: 10.1016/j.ejor.2011.04.036.  Google Scholar

[36]

G. Xie, W. Yue, S. Wang and K. K. Lai, Dynamic risk management in petroleum project investment based on a variable precision rough set model,, Technol. Forecast. Soc. Chang., 77 (2010), 891.  doi: 10.1016/j.techfore.2010.01.013.  Google Scholar

[37]

G. Xie, J. Zhang, K. K. Lai and L. Yu, Variable precision rough set for group decision-making: An application,, Int. J. Approx. Reason., 49 (2008), 331.  doi: 10.1016/j.ijar.2007.04.005.  Google Scholar

[38]

G. Xie, Y. Zhao, M. Jiang and N. Zhang, A novel ensemble learning approach for corporate financial distress forecasting in fashion and textiles supply chains,, Math. Probl. Eng., (2013).  doi: 10.1155/2013/493931.  Google Scholar

[39]

Q. Zhu, J. Sarkis, J. J. Cordeiro and K. Lai, Firm-level correlates of emergent green supply chain management practices in the Chinese context,, Omega, 36 (2008), 577.  doi: 10.1016/j.omega.2006.11.009.  Google Scholar

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