# American Institute of Mathematical Sciences

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 and Management Optimization, 2015, 11 (2) : 515-528. doi: 10.3934/jimo.2015.11.515
##### References:
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Tang, Product Variety Management: Research Advances, Boston/Dordrecht/London: Kluwer Academic Publishers, 1998. [12] C. A. Holloway, Decision Making Under Uncertainty: Models and Choices, Prentice-Hall, Englewood Cliffs, NJ, 1979. [13] R. A. Howard, Decision analysis: Practice and promise, Manag. Sci., 34 (1988), 679-695. doi: 10.1287/mnsc.34.6.679. [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, Environmental Protection Agency, Working Report, 34 (2006). [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), 414-423. doi: 10.1016/j.jclepro.2010.05.021. [16] Y. H. Kwak and K. S. LaPlace, Examining risk tolerance in project-driven organization, Technovation, 25 (2005), 691-695. doi: 10.1016/j.technovation.2003.09.003. [17] M. D. Levi and B. R. Nault, Converting technology to mitigate environmental damage, Manag. Sci., 50 (2004), 1015-1030. doi: 10.1287/mnsc.1040.0238. [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), 1497-1506. doi: 10.1016/j.compchemeng.2010.02.004. [19] H. M. Markowitz, Portfolio selection, J. Financ., 7 (1952), 77-91. [20] H. M. Markowitz, The optimization of a quadratic function subject to linear constraints, Nav. Res. Logist. Q., 3 (1956), 111-133. doi: 10.1002/nav.3800030110. [21] D. Maxwell and R. van der Vorst, Developing sustainable products and services, J. Clean. Prod., 11 (2003), 883-895. doi: 10.1016/S0959-6526(02)00164-6. [22] S. K. Moorthy, Product and price competition in a duopoly, Mark. Sci., 7 (1988), 141-168. doi: 10.1287/mksc.7.2.141. [23] U. Nissen, A methodology for the development of cleaner products: The ideal-eco-product approach, J. Clean. Prod., 3 (1995), 83-87. [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), 567-579. doi: 10.1016/j.resconrec.2010.06.003. [25] H. Raiffa, Decision analysis: Introductory lectures on choices under uncertainty, Journal of the Royal Statistical Society, Series D (The Statistician), 19 (1969), 180-181. doi: 10.2307/2987280. [26] J. Sarkis, A strategic decision framework for green supply chain management, J. Clean. Prod., 11 (2003), 397-409. doi: 10.1016/S0959-6526(02)00062-8. [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), 1218-1226. doi: 10.1016/j.jenvman.2010.01.016. [28] A. Tversky and D. Kahneman, Judgment under uncertainty: Heuristics and biases, Sci., 185 (1974), 1124-1131. [29] F. Wirl, Energy conservation, expectations and uncertainty, Energy Econ., 30 (2008), 1957-1972. doi: 10.1016/j.eneco.2007.12.001. [30] G. Xie, Modeling Decision Processes of a Green Supply Chain with Regulation on Energy Saving Level, Comput. Oper. Res., in press. doi: 10.1016/j.cor.2013.11.020. [31] G. Xie, S. Wang and K. K. Lai, Quality improvement in competing supply chains, Int. J. Prod. Econ., 134 (2011), 262-270. doi: 10.1016/j.ijpe.2011.07.007. [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), 13757-13763. [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), 2232-2241. doi: 10.1016/j.asoc.2013.02.002. [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), 473-484. [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), 403-410. doi: 10.1016/j.ejor.2011.04.036. [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), 891-901. doi: 10.1016/j.techfore.2010.01.013. [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), 331-343. doi: 10.1016/j.ijar.2007.04.005. [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), Article ID 493931, 9pp. doi: 10.1155/2013/493931. [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), 577-591. doi: 10.1016/j.omega.2006.11.009.

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##### References:
 [1] , Caution to risk in new energy automobile industry due to the uncertainty of support policy (in Chinese), 2010. Available from: http://info.auto.hc360.com/2010/08/060841447946.shtml. [2] , Qingdao Haier: Investment in technological improvement project for 3 million energy conservative refrigerators by a subsidiary company, 2010 (in Chinese). Available from: http://www.haier.net/en/. [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), 615-629. doi: 10.1016/S0305-0483(96)00026-6. [4] C. Chen, Design for the environment: A quality-based model for green product development, Manag. Sci., 47 (2001), 250-263. doi: 10.1287/mnsc.47.2.250.9841. [5] N. Chen, Credit risk is upraising in energy conservation and environment protection industry, Chinese Securities Journal, 2011 (in Chinese). Available from: http://stock.eastmoney.com/news/1421,20110126117369740.html. [6] G. Dowell, S. Hart and B. Yeung, Do corporate global environmental standards create or destroy market value? Manag. Sci., 46 (2000), 1059-1074. doi: 10.1287/mnsc.46.8.1059.12030. [7] B. Fischhoff, Acceptable Risk, Cambridge University Press, 1981. [8] F. Friedler, Process integration, modelling and optimisation for energy saving and pollution reduction, Appl. Therm. Eng., 30 (2010), 2270-2280. doi: 10.1016/j.applthermaleng.2010.04.030. [9] O. J. Hanssen, Sustainable product systems-experiences based on case projects in sustainable product development, J. Clean. Prod., 7 (1999), 27-41. doi: 10.1016/S0959-6526(98)00032-8. [10] N. Harmancioglu, Portfolio of controls in outsourcing relationships for global new product development, Ind. Mark. Manag., 38 (2009), 394-403. doi: 10.1016/j.indmarman.2009.04.001. [11] T. H. Ho and C. S. Tang, Product Variety Management: Research Advances, Boston/Dordrecht/London: Kluwer Academic Publishers, 1998. [12] C. A. Holloway, Decision Making Under Uncertainty: Models and Choices, Prentice-Hall, Englewood Cliffs, NJ, 1979. [13] R. A. Howard, Decision analysis: Practice and promise, Manag. Sci., 34 (1988), 679-695. doi: 10.1287/mnsc.34.6.679. [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, Environmental Protection Agency, Working Report, 34 (2006). [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), 414-423. doi: 10.1016/j.jclepro.2010.05.021. [16] Y. H. Kwak and K. S. LaPlace, Examining risk tolerance in project-driven organization, Technovation, 25 (2005), 691-695. doi: 10.1016/j.technovation.2003.09.003. [17] M. D. Levi and B. R. Nault, Converting technology to mitigate environmental damage, Manag. Sci., 50 (2004), 1015-1030. doi: 10.1287/mnsc.1040.0238. [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), 1497-1506. doi: 10.1016/j.compchemeng.2010.02.004. [19] H. M. Markowitz, Portfolio selection, J. Financ., 7 (1952), 77-91. [20] H. M. Markowitz, The optimization of a quadratic function subject to linear constraints, Nav. Res. Logist. Q., 3 (1956), 111-133. doi: 10.1002/nav.3800030110. [21] D. Maxwell and R. van der Vorst, Developing sustainable products and services, J. Clean. Prod., 11 (2003), 883-895. doi: 10.1016/S0959-6526(02)00164-6. [22] S. K. Moorthy, Product and price competition in a duopoly, Mark. Sci., 7 (1988), 141-168. doi: 10.1287/mksc.7.2.141. [23] U. Nissen, A methodology for the development of cleaner products: The ideal-eco-product approach, J. Clean. Prod., 3 (1995), 83-87. [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), 567-579. doi: 10.1016/j.resconrec.2010.06.003. [25] H. Raiffa, Decision analysis: Introductory lectures on choices under uncertainty, Journal of the Royal Statistical Society, Series D (The Statistician), 19 (1969), 180-181. doi: 10.2307/2987280. [26] J. Sarkis, A strategic decision framework for green supply chain management, J. Clean. Prod., 11 (2003), 397-409. doi: 10.1016/S0959-6526(02)00062-8. [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), 1218-1226. doi: 10.1016/j.jenvman.2010.01.016. [28] A. Tversky and D. Kahneman, Judgment under uncertainty: Heuristics and biases, Sci., 185 (1974), 1124-1131. [29] F. Wirl, Energy conservation, expectations and uncertainty, Energy Econ., 30 (2008), 1957-1972. doi: 10.1016/j.eneco.2007.12.001. [30] G. Xie, Modeling Decision Processes of a Green Supply Chain with Regulation on Energy Saving Level, Comput. Oper. Res., in press. doi: 10.1016/j.cor.2013.11.020. [31] G. Xie, S. Wang and K. K. Lai, Quality improvement in competing supply chains, Int. J. Prod. Econ., 134 (2011), 262-270. doi: 10.1016/j.ijpe.2011.07.007. [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), 13757-13763. [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), 2232-2241. doi: 10.1016/j.asoc.2013.02.002. [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), 473-484. [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), 403-410. doi: 10.1016/j.ejor.2011.04.036. [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), 891-901. doi: 10.1016/j.techfore.2010.01.013. [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), 331-343. doi: 10.1016/j.ijar.2007.04.005. [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), Article ID 493931, 9pp. doi: 10.1155/2013/493931. [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), 577-591. doi: 10.1016/j.omega.2006.11.009.
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