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January  2019, 15(1): 199-219. doi: 10.3934/jimo.2018039

Closed-loop supply chain network equilibrium model with retailer-collection under legislation

Wenbin Wang 1, , Peng Zhang 1, , Junfei Ding 1, , Jian Li 2,, , Hao Sun 3,, and  Lingyun He 1,

1. 

School of Management, China University of Mining and Technology, Xuzhou 221116, China
2. 

College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
3. 

School of Business, Qingdao University, Qingdao 266071, China

* Corresponding author: lijianzh@njau.edu.cn(Jian Li); rivaldoking@126.com(Hao Sun).

Received  May 2017 Revised  August 2017 Published  January 2019 Early access  April 2018

Fund Project: The first author is supported by the Fundamental Research Funds for the Central Universities of China (Project No. 2017XKQY034).

This paper examines the waste of electrical and electronic equipments (WEEE) and draws on variational inequalities to model the closed-loop supply chain network. The network consists of manufacturers, retailers and consumer markets engaging in a Cournot-Nash game. Retailers are responsible for collecting WEEE in the network. It is assumed that the price of the remanufactured goods is different from that of the newly manufactured ones. The network equilibrium occurs when all players agree on volumes and prices. Several properties of the model are examined and the modified projection method is utilized to obtain the optimal solutions. Numerical examples are provided to illustrate the impact of CLSC parameters on the profits of channel members and consumer benefits, and to provide policy support for governments. We find that it is necessary to regulate a medium collection rate and a certain minimum recovery rate. This is also advantageous to manufacturers in producing new manufactured products. The impact of collection rate and recovery rate on manufacturers are greater than that on retailers. Consumers can benefit from the increase of the recovery rate as well as the collection rate.

Keywords: Closed-loop supply chain network, collection, remanufacturing, variational inequality.
Mathematics Subject Classification: Primary: 90B10; Secondary: 91A50, 91A10.
Citation: Wenbin Wang, Peng Zhang, Junfei Ding, Jian Li, Hao Sun, Lingyun He. Closed-loop supply chain network equilibrium model with retailer-collection under legislation. Journal of Industrial & Management Optimization, 2019, 15 (1) : 199-219. doi: 10.3934/jimo.2018039
References:
[1]

P. Calcott and M. Walls, Waste, recycling, and Design for Environment: Roles for markets and policy instruments, Resource and Energy Economics, 27 (2005), 287-305.  doi: 10.1016/j.reseneeco.2005.02.001.  Google Scholar

[2]

S. C. Choi, Price competition in a channel structure with a common retailer, Marketing Science, 10 (1991), 271-296.  doi: 10.1287/mksc.10.4.271.  Google Scholar

[3]

S. C. Choi, Price competition in a duopoly common retailer channel, Journal of Retailing, 72 (1996), 117-134.  doi: 10.1016/S0022-4359(96)90010-X.  Google Scholar

[4]

J. M. Cruz, Dynamics of supply chain networks with corporate social responsibility through integrated environmental decision-making, European Journal of Operational Research, 184 (2008), 1005-1031.  doi: 10.1016/j.ejor.2006.12.012.  Google Scholar

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P. Georgiadis and D. Vlachos, The effect of environmental parameters on product recovery, European Journal of Operational Research, 157 (2004), 449-464.  doi: 10.1016/S0377-2217(03)00203-0.  Google Scholar

[6]

K. Govindan, H. Soleimani and D. Kannan, Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future, European Journal of Operational Research, 240 (2015), 603-626.  doi: 10.1016/j.ejor.2014.07.012.  Google Scholar

[7]

V. D. R. Guide, T. P. Harrison and L. N. Van Wassenhove, The challenge of closed-loop supply chains, Interfaces, 33 (2003), 3-6.   Google Scholar

[8]

Y. Hamdouch, Q. P. Qiang and K. Ghoudi, A closed-loop supply chain equilibrium model with random and price-sensitive demand and return, Networks and Spatial Economics, 17 (2017), 459-503.   Google Scholar

[9]

D. Hammond and P. Beullens, Closed-loop supply chain network equilibrium under legislation, European Journal of Operational Research, 183 (2007), 895-908.  doi: 10.1016/j.ejor.2006.10.033.  Google Scholar

[10]

Y. Hotta, C. Visvanathan and M. Kojima, Recycling rate and target setting: challenges for standardized measurement, Journal of Material Cycles and Waste Management, 18 (2016), 14-21.  doi: 10.1007/s10163-015-0361-3.  Google Scholar

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A. Nagurney, Network Economics: A Variational Inequality Approach, Kluwer Academic Publishers, 1993.  Google Scholar

[12]

A. Nagurney, Supply Chain Network Economics: Dynamics of prices flows and profits, Edward Elgar Publishing Limited, 2006. Google Scholar

[13]

A. Nagurney, J. Dong and D. Zhang, A supply chain network equilibrium model, Transportation Research Part E, 38 (2002), 281-303.  doi: 10.1016/S1366-5545(01)00020-5.  Google Scholar

[14]

A. Nagurney and F. Toyasaki, Reserve supply chain management and electronic waste recycling: A multitiered network equilibrium framework for e-cycling, Transportation Research Part E, 41 (2005), 1-28.   Google Scholar

[15]

A. Nagurney and F. Toyasaki, Supply chain supernetworks and environmental criteria, Transportation Research Part D, 8 (2003), 185-213.  doi: 10.1016/S1361-9209(02)00049-4.  Google Scholar

[16]

V. Padmanabhan and I. P. L. Png, Manufacturer's returns policies and retail competition, Marketing Science, 16 (1997), 81-94.   Google Scholar

[17]

Q. P. Qiang, The closed-loop supply chain network with competition and design for remanufactureability, Journal of Cleaner Production, 105 (2015), 348-356.  doi: 10.1016/j.jclepro.2014.07.005.  Google Scholar

[18]

Q. Qiang, K. Ke, T. Anderson and J. Dong, The closed-loop supply chain network with competition, distribution channel investment, and uncertainties, Omega, 41 (2013), 186-194.  doi: 10.1016/j.omega.2011.08.011.  Google Scholar

[19]

S. Salhofer, B. Steuer, R. Ramusch and P. Beigl, WEEE management in Europe and China--A comparison, Waste Management, 57 (2016), 27-35.   Google Scholar

[20]

R. C. Savaskan and L. N. Van Wassenhove, Reverse Channel Design: The case of competing retailers, Management Science, 52 (2006), 1-14.   Google Scholar

[21]

R. C. Savaskan, S. Bhattacharya and L. N. Van Wassenhove, Closed-Loop supply chain models with product remanufacturing, Management Science, 50 (2004), 239-252.   Google Scholar

[22]

F. Schultmann, M. Zumkeller and O. Rentz, Modeling reverse logistic tasks within closed-loop supply chains: An example from the automotive industry, European Journal of Operational Research, 171 (2006), 1033-1050.   Google Scholar

[23]

G. C. Souza, Closed-loop supply chains: A critical review, and future research, Decision Sciences, 44 (2013), 7-38.  doi: 10.1111/j.1540-5915.2012.00394.x.  Google Scholar

[24]

L. Wang and M. Chen, Policies and perspective on end-of-life vehicles in China, Journal of Cleaner Production, 44 (2013), 168-176.  doi: 10.1016/j.jclepro.2012.11.036.  Google Scholar

[25]

S. Webster and S. Mitra, Competitive strategy in remanufacturing and the impact of take-back laws, Journal of Operations Management, 25 (2007), 1123-1140.  doi: 10.1016/j.jom.2007.01.014.  Google Scholar

[26]

J. Wei, K. Govindan, Y. Li and J. Zhao, Pricing and collecting decisions in a closed-loop supply chain with symmetric and asymmetric information, Computers and Operations Research, 54 (2015), 257-265.  doi: 10.1016/j.cor.2013.11.021.  Google Scholar

[27]

G. Yang, Z. Wang and X. Li, The optimization of the closed-loop supply chain network, Transportation Research Part E: Logistics and Transportation Review, 45 (2009), 16-28.  doi: 10.1016/j.tre.2008.02.007.  Google Scholar

[28]

R. Zuidwijk and H. Krikke, Strategic response to EEE returns: Product eco-design or new recovery processes, European Journal of Operational Research, 191 (2008), 1206-1222.  doi: 10.1016/j.ejor.2007.08.004.  Google Scholar

show all references

References:
[1]

P. Calcott and M. Walls, Waste, recycling, and Design for Environment: Roles for markets and policy instruments, Resource and Energy Economics, 27 (2005), 287-305.  doi: 10.1016/j.reseneeco.2005.02.001.  Google Scholar

[2]

S. C. Choi, Price competition in a channel structure with a common retailer, Marketing Science, 10 (1991), 271-296.  doi: 10.1287/mksc.10.4.271.  Google Scholar

[3]

S. C. Choi, Price competition in a duopoly common retailer channel, Journal of Retailing, 72 (1996), 117-134.  doi: 10.1016/S0022-4359(96)90010-X.  Google Scholar

[4]

J. M. Cruz, Dynamics of supply chain networks with corporate social responsibility through integrated environmental decision-making, European Journal of Operational Research, 184 (2008), 1005-1031.  doi: 10.1016/j.ejor.2006.12.012.  Google Scholar

[5]

P. Georgiadis and D. Vlachos, The effect of environmental parameters on product recovery, European Journal of Operational Research, 157 (2004), 449-464.  doi: 10.1016/S0377-2217(03)00203-0.  Google Scholar

[6]

K. Govindan, H. Soleimani and D. Kannan, Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future, European Journal of Operational Research, 240 (2015), 603-626.  doi: 10.1016/j.ejor.2014.07.012.  Google Scholar

[7]

V. D. R. Guide, T. P. Harrison and L. N. Van Wassenhove, The challenge of closed-loop supply chains, Interfaces, 33 (2003), 3-6.   Google Scholar

[8]

Y. Hamdouch, Q. P. Qiang and K. Ghoudi, A closed-loop supply chain equilibrium model with random and price-sensitive demand and return, Networks and Spatial Economics, 17 (2017), 459-503.   Google Scholar

[9]

D. Hammond and P. Beullens, Closed-loop supply chain network equilibrium under legislation, European Journal of Operational Research, 183 (2007), 895-908.  doi: 10.1016/j.ejor.2006.10.033.  Google Scholar

[10]

Y. Hotta, C. Visvanathan and M. Kojima, Recycling rate and target setting: challenges for standardized measurement, Journal of Material Cycles and Waste Management, 18 (2016), 14-21.  doi: 10.1007/s10163-015-0361-3.  Google Scholar

[11]

A. Nagurney, Network Economics: A Variational Inequality Approach, Kluwer Academic Publishers, 1993.  Google Scholar

[12]

A. Nagurney, Supply Chain Network Economics: Dynamics of prices flows and profits, Edward Elgar Publishing Limited, 2006. Google Scholar

[13]

A. Nagurney, J. Dong and D. Zhang, A supply chain network equilibrium model, Transportation Research Part E, 38 (2002), 281-303.  doi: 10.1016/S1366-5545(01)00020-5.  Google Scholar

[14]

A. Nagurney and F. Toyasaki, Reserve supply chain management and electronic waste recycling: A multitiered network equilibrium framework for e-cycling, Transportation Research Part E, 41 (2005), 1-28.   Google Scholar

[15]

A. Nagurney and F. Toyasaki, Supply chain supernetworks and environmental criteria, Transportation Research Part D, 8 (2003), 185-213.  doi: 10.1016/S1361-9209(02)00049-4.  Google Scholar

[16]

V. Padmanabhan and I. P. L. Png, Manufacturer's returns policies and retail competition, Marketing Science, 16 (1997), 81-94.   Google Scholar

[17]

Q. P. Qiang, The closed-loop supply chain network with competition and design for remanufactureability, Journal of Cleaner Production, 105 (2015), 348-356.  doi: 10.1016/j.jclepro.2014.07.005.  Google Scholar

[18]

Q. Qiang, K. Ke, T. Anderson and J. Dong, The closed-loop supply chain network with competition, distribution channel investment, and uncertainties, Omega, 41 (2013), 186-194.  doi: 10.1016/j.omega.2011.08.011.  Google Scholar

[19]

S. Salhofer, B. Steuer, R. Ramusch and P. Beigl, WEEE management in Europe and China--A comparison, Waste Management, 57 (2016), 27-35.   Google Scholar

[20]

R. C. Savaskan and L. N. Van Wassenhove, Reverse Channel Design: The case of competing retailers, Management Science, 52 (2006), 1-14.   Google Scholar

[21]

R. C. Savaskan, S. Bhattacharya and L. N. Van Wassenhove, Closed-Loop supply chain models with product remanufacturing, Management Science, 50 (2004), 239-252.   Google Scholar

[22]

F. Schultmann, M. Zumkeller and O. Rentz, Modeling reverse logistic tasks within closed-loop supply chains: An example from the automotive industry, European Journal of Operational Research, 171 (2006), 1033-1050.   Google Scholar

[23]

G. C. Souza, Closed-loop supply chains: A critical review, and future research, Decision Sciences, 44 (2013), 7-38.  doi: 10.1111/j.1540-5915.2012.00394.x.  Google Scholar

[24]

L. Wang and M. Chen, Policies and perspective on end-of-life vehicles in China, Journal of Cleaner Production, 44 (2013), 168-176.  doi: 10.1016/j.jclepro.2012.11.036.  Google Scholar

[25]

S. Webster and S. Mitra, Competitive strategy in remanufacturing and the impact of take-back laws, Journal of Operations Management, 25 (2007), 1123-1140.  doi: 10.1016/j.jom.2007.01.014.  Google Scholar

[26]

J. Wei, K. Govindan, Y. Li and J. Zhao, Pricing and collecting decisions in a closed-loop supply chain with symmetric and asymmetric information, Computers and Operations Research, 54 (2015), 257-265.  doi: 10.1016/j.cor.2013.11.021.  Google Scholar

[27]

G. Yang, Z. Wang and X. Li, The optimization of the closed-loop supply chain network, Transportation Research Part E: Logistics and Transportation Review, 45 (2009), 16-28.  doi: 10.1016/j.tre.2008.02.007.  Google Scholar

[28]

R. Zuidwijk and H. Krikke, Strategic response to EEE returns: Product eco-design or new recovery processes, European Journal of Operational Research, 191 (2008), 1206-1222.  doi: 10.1016/j.ejor.2007.08.004.  Google Scholar

Figure 1.  Closed-loop supply chain network with WEEE collected by retailers
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Figure 2.  The volume changes of three kinds of products with the changes of collection rate while $\beta = 0.5$ are fixed
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Figure 3.  The volume changes of three kinds of products with the changes of recovery rate while $\alpha = 1$ are fixed
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Table 1.  The prices under different collection rate ($\alpha$), given $\beta = 0.5$
$\alpha=0$ $\alpha=0.2$ $\alpha=0.4$ $\alpha=0.6$ $\alpha=0.8$ $\alpha=1$
$w_i^{f^*}$ 352.8712 352.8712 358.2704 383.6992 414.7223 435.5493
$w_j^{r^*}$ 59.8369 59.8369 71.6460 98.1422 108.6234 106.8103
$w_i^{r^*}$ 384.0070 384.0070 376.6145 362.1067 359.6196 375.6179
$P_j^{f^*}$ 402.8527 402.8527 406.0255 421.8723 441.8835 456.1049
$P_k^{r^*}$ 39.8914 39.8914 47.7639 65.4282 72.4157 71.2072
$P_j^{r^*}$ 393.9832 393.9832 388.5579 378.4611 377.7207 390.6693
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$\alpha=0$ $\alpha=0.2$ $\alpha=0.4$ $\alpha=0.6$ $\alpha=0.8$ $\alpha=1$
$w_i^{f^*}$ 352.8712 352.8712 358.2704 383.6992 414.7223 435.5493
$w_j^{r^*}$ 59.8369 59.8369 71.6460 98.1422 108.6234 106.8103
$w_i^{r^*}$ 384.0070 384.0070 376.6145 362.1067 359.6196 375.6179
$P_j^{f^*}$ 402.8527 402.8527 406.0255 421.8723 441.8835 456.1049
$P_k^{r^*}$ 39.8914 39.8914 47.7639 65.4282 72.4157 71.2072
$P_j^{r^*}$ 393.9832 393.9832 388.5579 378.4611 377.7207 390.6693
Table 2.  The prices under different recovery rate ($\beta$), given $\alpha = 1$
$\beta=0.1$ $\beta=0.2$ $\beta=0.3$ $\beta=0.4$ $\beta=0.5$
$w_i^{f^*}$ 422.8170 424.1308 427.8478 433.8971 435.5493
$w_j^{r^*}$ 91.0883 97.7935 103.2339 106.4136 106.8103
$w_i^{r^*}$ 424.1402 409.5847 394.2371 378.9959 375.6179
$P_j^{f^*}$ 450.0756 450.2139 451.9405 455.1846 456.1049
$P_k^{r^*}$ 60.7962 65.1957 68.8227 70.9424 71.2072
$P_j^{r^*}$ 427.1726 416.1012 404.5575 393.1814 390.6693
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$\beta=0.1$ $\beta=0.2$ $\beta=0.3$ $\beta=0.4$ $\beta=0.5$
$w_i^{f^*}$ 422.8170 424.1308 427.8478 433.8971 435.5493
$w_j^{r^*}$ 91.0883 97.7935 103.2339 106.4136 106.8103
$w_i^{r^*}$ 424.1402 409.5847 394.2371 378.9959 375.6179
$P_j^{f^*}$ 450.0756 450.2139 451.9405 455.1846 456.1049
$P_k^{r^*}$ 60.7962 65.1957 68.8227 70.9424 71.2072
$P_j^{r^*}$ 427.1726 416.1012 404.5575 393.1814 390.6693
Table 3.  The profits of manufacturers and retailers with different collection rate ($\alpha$), given $\beta = 0.5$
Total profits $\alpha \le 0.2$ $\alpha=0.4$ $\alpha=0.6$ $\alpha=0.8$ $\alpha=1$
Manufactures 13642.876 17247.346 15531.509 12759.803 10108.842
Retailers 2995.498 2993.549 2794.858 2376.378 1916.658
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Total profits $\alpha \le 0.2$ $\alpha=0.4$ $\alpha=0.6$ $\alpha=0.8$ $\alpha=1$
Manufactures 13642.876 17247.346 15531.509 12759.803 10108.842
Retailers 2995.498 2993.549 2794.858 2376.378 1916.658
Table 4.  The profits of manufacturers and retailers with different recovery rate ($\beta$), given $\alpha = 1$
Total profits$\beta=0.1$ $\beta=0.2$ $\beta=0.3$ $\beta=0.4$ $\beta\ge0.5$
Manufactures 9082.426 9616.632 9981.219 10112.544 10108.842
Retailers 1669.838 1785.409 1944.052 2113.821 1916.658
Table Options

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Total profits$\beta=0.1$ $\beta=0.2$ $\beta=0.3$ $\beta=0.4$ $\beta\ge0.5$
Manufactures 9082.426 9616.632 9981.219 10112.544 10108.842
Retailers 1669.838 1785.409 1944.052 2113.821 1916.658
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