# Research on Pricing and Service Level Strategies of Dual Channel Reverse Supply Chain Considering Consumer Preference in Multi-Regional Situations

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. DRSC

#### 2.2. Service Level Decisions

## 3. Description of Notations

_{i}, and then TPR sells WEEE to the recycling center at the offline transfer price of w

_{i}. In online channels, the recycling center outsources the whole online channel to TPP. Consumers need to sell WEEE to TPP at the price of p

_{e}, and then TPP sells WEEE to the recycling center at the online transfer price w

_{e}. In this process, TPP provides consumers with the recycling service level of s

_{i}. In addition, as mentioned in the first section, combined with the real situation, offline recycling price and online recycling service are affected by regional differences and there are differences. This means that when the regional differences in one region change, the decision and profits of recycling enterprises in another region may be affected. The recycling amount of consumers of online and offline channels depends on their preference for online channels, the recycling price of different channels, and the influence of service level [2,24]. Specifically, we assume that the basic number of WEEE held by consumers in a city is α

_{i}. At the same time, the online (offline) recycling amount is also affected by the positive correlation of the online (offline) recycling price and service level, and by the negative correlation of the recycling price and service level of its competitive channels. In addition, we also assume that the recycling center, TPR, and TPP follow Stackelberg game, and based on a large number of existing studies, we assume that the recycling center dominates, while TPR and TPP follow [1,5,10]. This means that in the game of the recycling center, TPR, and TPP, the recycling center makes a decision in its favor first; TPR and TPP can only make a decision after observing the decision of the recycling center. The major notations used in this paper are listed in Table 1, where i = 1 stands for city A and i = 2 stands for city B.

_{s}= ηs

^{2}/2.This shows that the recycling service level has a significant positive correlation with service cost. In addition, we also assume that WEEE’s recycling is linearly related to online and offline recycling price and service level. Specifically, the recycling amount of different cities can be expressed as: d

_{ri}= (1 − θ

_{i})α

_{i}+ m

_{i}p

_{i}− k

_{i}p

_{e}− n

_{i}s

_{i}, d

_{ei}= θ

_{i}α

_{i}+ m

_{i}p

_{e}− k

_{i}p

_{i}+ h

_{i}s

_{i}.

## 4. Model Analysis

_{1}= d

_{r}

_{1}(w

_{1}− p

_{1}) = (α

_{1}+ m

_{1}p

_{1})(w

_{1}− p

_{1}), П

_{2}= d

_{r}

_{2}(w

_{2}− p

_{2}) = (α

_{2}+ m

_{2}p

_{2})(w

_{2}− p

_{2}), П

_{m}= d

_{r}

_{1}(p

_{0}− w

_{1}) + d

_{r}

_{2}(p

_{0}− w

_{2})= (α

_{1}+ m

_{1}p

_{1}) (p

_{0}− w

_{1}) + (α

_{2}+ m

_{2}p

_{2}) (p

_{0}− w

_{2}).

_{r}

_{1}= α

_{1}+ m

_{1}p

_{1}, and that in region B is d

_{r}

_{2}= α

_{2}+ m

_{2}p

_{2}. In Stackelberg game between the recycling center and TPR, the recycling center always occupies a dominant position, so the recycling center makes decisions first, and TPR makes decisions after observing the decisions of the recycling center. According to the backward induction method, the optimal recovery prices of the two TPRs are first solved.

**Property 1.**

_{1}(p

_{1}) is always concave with p

_{1}. The objective function ∏

_{2}(p

_{2}) is always concave with p

_{2}.

_{1}and ∏

_{2}, there is optimal p

_{1}and p

_{2}respectively, which makes ∏

_{1}and ∏

_{2}reach the maximum. Therefore, by solving the first partial derivatives of ∏

_{1}for p

_{1}and ∏

_{2}for p

_{2}respectively, and making them be 0, the optimal recovery price p

_{1}* of ∏

_{1}can be obtained as follows:

_{2}* of ∏

_{2}is:

_{1}* and p

_{2}* of the two regions into the profit function of the recovery center, we can get:

**Property 2.**

_{m}(w

_{1}, w

_{2}) is always concave with w

_{1}and w

_{2}.

_{m}, there is optimal w

_{1}and w

_{2}respectively, which makes ∏

_{m}reach the maximum. Therefore, by solving the first partial derivatives of ∏

_{m}for w

_{1}and w

_{2}respectively, making them be 0, and combining them, the optimal offline transfer prices of ∏

_{m}can be obtained as follows:

#### 4.1. The Offline Transfer Price Is Unchanged

_{i}= m

_{j}= 2, k

_{i}= k

_{j}= 1, h

_{i}= h

_{j}= 2, n

_{i}= n

_{j}= 1. The profit function of the multi-regional TPR can be obtained as follows:

**Property 3.**

_{1}(p

_{1}) is always concave with p

_{1}. The objective function ∏

_{2}(p

_{2}) is always concave with p

_{2}. When η

_{1}+ η

_{2}− 2η

_{1}η

_{2}< 0, the objective function ∏

_{e}(p

_{e}, s

_{1}, s

_{2}) is always concave with p

_{e}, s

_{1}, s

_{2}.

_{1}and ∏

_{2}, there is optimal p

_{1}and p

_{2}respectively, which makes ∏

_{1}and ∏

_{2}reach the maximum. For the function ∏

_{e}, there is optimal p

_{e}, s

_{1}, s

_{2}, which makes ∏

_{e}reach the maximum. Therefore, by solving ∏

_{1}for p

_{1}and making it be 0, the optimal recovery price p

_{1}* of ∏

_{1}can be obtained as follows:

_{2}* of ∏

_{2}can be obtained as follows:

_{e}for p

_{e}, s

_{1}, s

_{2}respectively, making them be 0, and then ombining them, the optimal decisions p

_{e}*, s

_{1}*, s

_{2}* of ∏

_{e}can be obtained as follows:

_{1}* p

_{2}* p

_{e}*, s

_{1}*, s

_{2}* into the function, and we can get:

**Property 4.**

_{1}+ 7η

_{2}− 15η

_{1}η

_{2}< 0, the objective function ∏

_{m}(w

_{e}) is always concave with w

_{e}.

_{m}, there is an optimal w

_{e}, which makes ∏

_{m}reach the maximum. Therefore, by solving ∏

_{m}for we and making it be 0, the optimal recovery price w

_{e}* of ∏

_{m}can be obtained as follows:

_{1}*, p

_{2}*, p

_{e}*, w

_{e}*, s

_{1}* and s

_{2}* into the profit functions of the recycling centers, TPPs, and TPRs of different regions, we can get the maximum profit of each recycling enterprise under the optimal pricing and service level decision under the 4.1 strategy.

**Corollary 1.**

#### 4.2. Unify All Transfer Prices

_{1}= w

_{2}= w

_{e}. This can make TPP or multi-regional TPR avoid potential conflicts because other recycling enterprises get higher transfer price. The profit function of multi-regional TPR can be expressed as:

_{1}/∂p

_{1}of ∏

_{1}for p

_{1}, making it be 0, and solving it in reverse, the optimal recycling price p

_{1}* of TPR in region A can be obtained:

_{2}* of the TPR in region B is:

_{e}for p

_{e}, s

_{1}and s

_{2}respectively, making them be 0, solving them in reverse, and combining them, the optimal pricing and service level decisions p

_{e}*, s

_{1}* and s

_{2}* of the TPP can be obtained as follows:

_{1}, ∏

_{2}and ∏

_{e}into ∏

_{m}, we can get:

_{m}/∂w of ∏

_{m}for w, making it be 0, and solving it in reverse, the optimal online transfer price w* of the recycling center can be obtained as follows:

_{i}*, p

_{j}*, p

_{e}*, w

_{e}*, s

_{i}*, and s

_{j}* into the profit functions of the recycling center, TPP, and TPR in different regions, the maximum profit of the enterprise under the optimal pricing and service level decision can be obtained.

#### 4.3. Maximize Its Own Profits

_{1}/∂p

_{1}of ∏

_{1}for p

_{1}, making it be 0, and solving it in reverse, the optimal recycling price p

_{1}* of the TPR in region A can be obtained as follows:

_{2}* of the TPR in region B is:

_{e}for p

_{e}, s

_{1}and s

_{2}respectively, making it be 0, solving it in reverse, and combining them, the optimal pricing and service level decision p

_{e}*, s

_{1}* and s

_{2}* of the TPP can be obtained as follows:

_{m}for w

_{e}, w

_{1}and w

_{2}respectively, making it be 0, solving it in reverse, and combining them, the optimal pricing decisions of the recycling center w

_{e}*, w

_{1}* and w

_{2}* can be obtained as follows:

_{1}*, p

_{2}*, p

_{e}*, w

_{e}*, w

_{1}*, w

_{2}*, s

_{1}*, s

_{2}* into the profit functions of the recycling center, TPP, and TPR in different regions, the maximum profit of each recycling enterprise under the optimal pricing and service level decisions can be obtained.

**Corollary 2.**

**Corollary 3.**

_{j}− 37θ

_{i}+ 16 = 0, η = 2, or θ

_{i}= 0.5, θ

_{j}= 0.5, the optimal p

_{1}* under three different models remains equal. When 5θ

_{i}− 37θ

_{j}+ 16 = 0, η = 2, or θ

_{i}= θ

_{j}= 0.5, the optimal p

_{2}* under three different models remains equal. When θ

_{i}+ θ

_{j}= 1, the optimal p

_{e}

^{*}, s

_{1}

^{*}and s

_{2}

^{*}under three different models remain equal. When θ

_{i}= θ

_{j}= 0.5, the optimal w

_{1}* and w

_{2}

^{*}under three different models remain equal. When θ

_{i}= θ

_{j}= 0.5, the profits of the recycling enterprises under three different pricing models remain equal.

## 5. Numerical Example

#### 5.1. θ Analysis

_{1}= a

_{2}= 500, p

_{0}= 1000, θ

_{2}= 0.5, η

_{1}= η

_{2}= 8, and take θ

_{1}to increase gradually from 0.3 to 0.7 at a rate of 0.1. By inputting the above data into Mathematica software, the research results verify that the promotion of consumer preference for online recycling channels in region A will have an impact on the decision and profit of the recycling center, TPP, and multi-regional TPR in a multi-regional situation. According to the simulation results, we draw Table 2, Table 3 and Table 4 to analyze the effect of consumer preference in region A on the decision and profit of recycling enterprises under the three strategies of keeping transfer price unchanged, unifying all transfer prices, and maximizing profit. Secondly, according to Figure 2 and Figure 3, we compare the change rules of decision and profit of recycling enterprises under three pricing strategies. To make the statement clearer, we use S1, S2, and S3 to represent the strategies in Section 4.1, Section 4.2, and Section 4.3, respectively.

_{1}, while its profit decreases monotonically. In addition, for the TPR in region B, the optimal offline recycling price decreases slightly with the increase of θ

_{1}, while its profit increases monotonically. It can be seen from the above that under this strategy, the change of consumer preference for online recycling channels in region A will not only affect the changes of profit and decision of the TPR, TPP, and recycling center in this region, but also affect the changes of profit and decision of the TPR in region B. Finally, it can be seen from the above table that under this strategy, the total profit of the supply chain system will decrease slightly with the increase of consumer preference for online recycling channels in region A.

_{1}, while its profit decreases monotonically. In addition, for the TPR in region B, the optimal offline recycling price decreases slightly with the increase of θ

_{1}, while its profit increases monotonically. It can be seen from the above that under this strategy, the change of consumer preference for online recycling channels in region A will not only affect the changes of profit and decision of the TPR, TPP, and recycling center in this region, but also affect changes of the profit and decision of the TPR in region B. Finally, it can be seen from the above table that under this strategy, the total profit of the supply chain system will increase slightly with the increase of consumer preference for online recycling channels in region A.

_{1}, while its profit decreases monotonically. In addition, for the TPR in region B, the optimal offline recycling price decreases slightly with the increase of θ

_{1}, while its profit increases monotonically. It can be seen from the above that under this strategy, the change of consumer preference for online recycling channels in region A will not only affect the changes of profit and decision of the TPR, TPP, and recycling center in this region, but also affect the changes of the profit and decision of the TPR in region B. Finally, it can be seen from the above table that under this strategy, the total profit of the supply chain system will increase slightly with the increase of consumer preference for online recycling channels in region A.

#### 5.2. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

**Proof of Property 1:**

_{i}for p

_{i}, we can get:

_{I}for p

_{i}, we can obtain:

_{1}> 0, there is a constant ∂

^{2}∏

_{1}/∂p

_{1}

^{2}< 0

_{1}of the third-party recycler is convex function for p

_{1}

_{2}is strictly convex for p

_{2}.

**Proof of Property 2:**

_{m}for w

_{1}, we can obtain:

_{m}for w

_{2}, we can obtain:

_{m}(w

_{1}, w

_{2}) is as follows:

_{m}) is −m

_{1}< 0, and the second-order principal sub-formula is m

_{1}, m

_{2}> 0.

_{m}of the recycling center is strictly joint convex function for w

_{1}and w

_{2}.

**Proof of Property 3:**

_{1}to p

_{1}, we can obtain:

_{1}for p

_{1}, we can obtain

^{2}∏

_{1}/∂p

_{1}

^{2}< 0

_{1}of TPR is convex function for p

_{1}

_{2}is strictly convex for p

_{2}.

_{e}for p

_{e}, s

_{1}, s

_{2}are solved respectively, and the Hessian Matrix of ∏

_{e}(p

_{e}, s

_{1}, s

_{2}) is drawn as follows:

_{m}) is less than 0 and the second order principal sub-formula η

_{i}η

_{j}is greater than 0, when the third order principal sub-formula is 2η

_{1}+ 2η

_{2}− 4η

_{1}η

_{2}< 0, then the function ∏

_{m}is joint convex function for p

_{e}, s

_{1}, s

_{2}.

**Proof of Property 4:**

_{m}for w

_{e}, we can obtain:

_{1}+ 7η

_{2}− 15η

_{1}η

_{2}< 0, the second partial derivative of ∏

_{m}for w

_{e}is always less than 0, the objective function ∏

_{m}(w

_{e}) is always concave with w

_{e}.

## Appendix B

**Proof of Corollary 1:**

_{e}* solve the first-order partial derivatives of θ

_{1}and θ

_{2}respectively, and it is easy to obtain that the coefficients are all less than 0. Therefore, the optimal online transfer price of the recycling center is affected by the negative correlation of consumer preference.

**Proof of Corollary 2:**

_{1}* solve the first-order partial derivatives of θ

_{1}and θ

_{2}respectively, and it is easy to obtain that the coefficient of 5a

_{1}/24 is always greater than 0 when the partial derivative of θ

_{1}is solved and the coefficient of -a

_{2}/24 is always less than 0 when the partial derivative of θ

_{2}is solved. A similar conclusion can be obtained by solving the partial derivatives of θ

_{1}and θ

_{2}for w

_{2}*.

_{e}* solve the first-order partial derivatives of θ

_{1}and θ

_{2}respectively, and it is easy to obtain that the coefficients are always less than 0. Therefore, it can be seen that the optimal online transfer price of the recycling center is affected by the negative correlation of consumer preference in different regions.

**Proof of Corollary 3:**

_{1}= a

_{2}= a,η

_{1}= η

_{2}= η, and substitute them into the decision function of each enterprise.

_{1}* equal under three pricing models. Let (3) = (9) = (15), and we can get the condition that: a = 0; 5θ

_{j}− 37θ

_{i}+ 16 = 0, η = 2; θ

_{i}= 0.5, θ

_{j}= 0.5. According to the hypothesis that a > 0 is required, a = 0 is excluded. Therefore, it can be concluded that conditions for keeping the optimal p

_{1}

^{*}equal under the three pricing models are 5θ

_{j}− 37θ

_{i}+ 16 = 0, η = 2; θ

_{i}= 0.5, θ

_{j}= 0.5.

_{2}* equal under three pricing models. Let (4) = (10) = (16), and we can get the condition that: a = 0; 5θ

_{i}− 37θ

_{j}+ 16 = 0, η = 2; θ

_{i}= 0.5, θ

_{j}= 0.5. According to the hypothesis that a > 0 is required, a = 0 is excluded. Therefore, it can be concluded that conditions for keeping the optimal p

_{2}* equal under the three pricing models are 5θi − 37θj + 16 = 0, η = 2; θ

_{i}= 0.5, θ

_{j}= 0.5.

_{e}* equal under three pricing models. Let (5) = (11) = (17), and we can get the condition that: a = 0; θ

_{i}+ θ

_{j}= 1. According to the hypothesis that a > 0 is required, a = 0 is excluded. Therefore, it can be concluded that conditions for keeping the optimal p

_{e}* equal under the three pricing models are θ

_{i}+ θ

_{j}= 1.

_{1}

^{*}equal under three pricing models. Let (6) = (12) = (18), and we can get the condition that: a = 0; θ

_{i}+ θ

_{j}= 1. According to the hypothesis that a > 0 is required, a = 0 is excluded. Therefore, it can be concluded that conditions for keeping the optimal s

_{1}* equal under the three different strategies are θ

_{i}+ θ

_{j}= 1. In the same way, we can get that condition for keeping the optimal s

_{2}* equal under the three pricing models are θ

_{i}+ θ

_{j}= 1.

_{1}* equal under three pricing models. Let (1) = (14) = (20), and we can get the condition that: a = 0; θ

_{i}= θ

_{j}= 0.5. According to the hypothesis, that a > 0 is required, a = 0 is excluded. Therefore, it can be concluded that conditions for keeping the optimal w

_{1}* equal under the three different strategies are θ

_{i}= θ

_{j}= 0.5. In the same way, we can get that conditions for keeping the optimal w

_{2}* equal under the three pricing models are θ

_{i}= θ

_{j}= 0.5.

_{i}= θ

_{j}= 0.5, all decisions of enterprises under the three pricing models remain equal. All the decisions are equal, which directly leads to the same profits under the three pricing models. Therefore, the condition for each recycling enterprise to keep profits equal under the three pricing models is θ

_{i}= θ

_{j}= 0.5.

## References

- Feng, L.; Govindan, K.; Li, C. Strategic planning: Design and coordination for dual-recycling channel reverse supply chain considering consumer behavior. Eur. J. Oper. Res.
**2017**, 260, 601–612. [Google Scholar] [CrossRef] - Wu, D.; Chen, J.H.; Li, P.; Zhang, R.J. Contract Coordination of Dual Channel Reverse Supply Chain Considering Service Level. J. Clean. Prod.
**2020**, 260, 121071. [Google Scholar] [CrossRef] - Aihuishou Big Data: Three Years of Huge Changes in the Recycling Market. Available online: https://www.sohu.com/a/214819513_398736 (accessed on 5 January 2017).
- Wu, D.; Chen, J.H.; Zhang, R.J. Online Reverse Supply Chain: New Layout to Promote Recycling Industry in China, 2015–2019. Iran. J. Public Health
**2020**, 49, 189–190. [Google Scholar] [CrossRef] [PubMed] - Giri, B.C.; Chakraborty, A.; Maiti, T. Pricing and return product collection decisions in a closed-loop supply chain with dual-channel in both forward and reverse logistics. J. Manuf. Syst.
**2017**, 42, 104–123. [Google Scholar] [CrossRef] - Chen, J.; Wu, D.; Li, P. Research on the Pricing Model of the Dual-Channel Reverse Supply Chain Considering Logistics Costs and Consumers’ Awareness of Sustainability Based on Regional Differences. Sustainability
**2018**, 10, 2229. [Google Scholar] [CrossRef] [Green Version] - Zuo, L.; Wang, C.; Sun, Q. Sustaining WEEE collection business in China: The case of online to offline (O2O) development strategies. Waste Manag.
**2020**, 101, 222–230. [Google Scholar] [CrossRef] [PubMed] - Bai, H.; Wang, J.; Zeng, A.Z. Exploring Chinese consumers’ attitude and behavior toward smartphone recycling. J. Clean. Prod.
**2018**, 188, 227–236. [Google Scholar] [CrossRef] - Wang, Z.; Zhang, B.; Yin, J.; Zhang, X. Willingness and behavior towards e-waste recycling for residents in Beijing city, China. J. Clean. Prod.
**2011**, 19, 977–984. [Google Scholar] [CrossRef] - Li, C.; Feng, L.; Luo, S. Strategic introduction of an online recycling channel in the reverse supply chain with a random demand. J. Clean. Prod.
**2019**, 236, 117683. [Google Scholar] [CrossRef] - Jian, H.Y.; Xu, M.L.; Zhou, L. Collaborative collection effort strategies based on the “Internet+ recycling” business model. J. Clean. Prod.
**2019**, 241, 118120. [Google Scholar] [CrossRef] - Zhang, L.; Zhou, H.; Liu, Y.; Lu, R. Optimal environmental quality and price with consumer environmental awareness and retailer’s fairness concerns in supply chain. J. Clean. Prod.
**2019**, 213, 1063–1079. [Google Scholar] [CrossRef] - Dotoli, M.; Fanti, M.P.; Meloni, C.; Zhou, M.C. A multi-level approach for network design of integrated supply chains. Int. J. Prod. Res.
**2005**, 43, 4267–4287. [Google Scholar] [CrossRef] - Dotoli, M.; Fanti, M.P.; Meloni, C.; Zhou, M. Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum.
**2005**, 36, 62–75. [Google Scholar] [CrossRef] - Wang, W.; Tian, Y.; Zhu, Q.; Zhong, Y. Barriers for household e-waste collection in China: Perspectives from formal collecting enterprises in Liaoning Province. J. Clean. Prod.
**2017**, 153, 299–308. [Google Scholar] [CrossRef] - Wang, B.; Ren, C.Y.; Dong, X.Y.; Zhang, B.; Wang, Z.H. Determinants shaping willingness towards on-line recycling behaviour: An empirical study of household e-waste recycling in China. Resour. Conserv. Recycl.
**2019**, 143, 218–225. [Google Scholar] [CrossRef] - National Development and Reform Commission of People’s Republic of China. 2015 Circular Economy Promotion Plan. Available online: http://www.gov.cn/xinwen/2015-04/20/content_2849620.htm (accessed on 14 April 2015).
- Tong, X.; Tao, D.Y.; Lifset, R. Varieties of business models for post-consumer recycling in China. J. Clean. Prod.
**2018**, 170, 665–673. [Google Scholar] [CrossRef] - Wang, H.D.; Han, H.G.; Liu, T.; Tian, X.; Xu, M.; Wu, W.; Gu, Y.; Liu, Y.; Zuo, T. “Internet +” recyclable resources: A new recycling mode in China. Resour. Conserv. Recycl.
**2018**, 134, 44–47. [Google Scholar] [CrossRef] - Qu, Y.; Wang, W.; Liu, Y.; Zhu, Q. Understanding residents’ preferences for e-waste collection in China—A case study of waste mobile phones. J. Clean. Prod.
**2019**, 228, 52–62. [Google Scholar] [CrossRef] - Wang, Y.; Bell, D.R.; Padmanabhan, V. Manufacturer-owned retail stores. Mark. Lett.
**2009**, 20, 107–124. [Google Scholar] [CrossRef] [Green Version] - Yan, R.; Pei, Z. Retail services and firm profit in a dual-channel market. J. Retail. Consum. Serv.
**2009**, 16, 306–314. [Google Scholar] [CrossRef] - Tsay, A.A.; Agrawal, N. Channel Conflict and Coordination in the E-Commerce Age. Prod. Oper. Manag.
**2004**, 13, 93–110. [Google Scholar] [CrossRef] [Green Version] - Giri, B.C.; Sarker, B.R. Coordinating a two-echelon supply chain under production disruption when retailers compete with price and service level. Oper. Res.
**2016**, 16, 71–88. [Google Scholar] [CrossRef] - Wang, L.S.; Song, H.M.; Wang, Y.Z. Pricing and service decisions of complementary products in a dual-channel supply chain. Comput. Ind. Eng.
**2016**, 105, 223–233. [Google Scholar] [CrossRef] - Zhang, F.; Wang, C. Dynamic pricing strategy and coordination in a dual-channel supply chain considering service value. Appl. Math. Model.
**2018**, 54, 722–742. [Google Scholar] [CrossRef] - Xie, J.P.; Zhang, W.S.; Liang, L.; Xia, Y.; Yin, J.; Yang, G. The revenue and cost sharing contract of pricing and servicing policies in a dual-channel closed-loop supply chain. J. Clean. Prod.
**2018**, 191, 361–383. [Google Scholar] [CrossRef] - Wang, Y.Y.; Li, J. Research on Dominant Models of E-CLSC Based on Network Sale and Recycle Considering Fairness Concern. Chin. J. Manag. Sci.
**2018**, 26, 139–151. [Google Scholar] - Cattani, K.; Gilland, W.; Heese, H.S.; Swaminathan, J. Abstract Boiling Frogs: Pricing Strategies for a Manufacturer Adding a Direct Channel that Competes with the Traditional Channel. Prod. Oper. Manag.
**2005**, 15, 40–56. [Google Scholar] [CrossRef] - Huang, W.; Swaminathan, J.M. Introduction of a second channel: Implications for pricing and profits. Eur. J. Oper. Res.
**2009**, 194, 258–279. [Google Scholar] [CrossRef]

**Figure 2.**The change of decisions under three strategies as θ

_{1}increases: (

**a**) p

_{1}, (

**b**) p

_{2}, (

**c**) p

_{e}, (

**d**) s

_{i}.

**Figure 3.**The change of profits under three strategies as θ

_{1}increases: (

**a**) ∏

_{1}, (

**b**) ∏

_{2}, (

**c**) ∏

_{e}, (

**d**) ∏

_{m}.

**Table 1.**Notation and explanations. WEEE, waste electrical and electronic equipment; TPR, third-party recycler; TPP, third-party platform.

Notation | Explanation |
---|---|

d_{ri} | Offline recycling amount (i = 1,2) |

d_{ei} | Online recycling amount (i = 1,2) |

θ_{i} | Consumer preference for online channels (i= 1,2) |

p_{0} | Unit income of the recycling center by remanufacturing or reselling WEEE |

p_{i} | Offline recycling price of TPR (i = 1,2) |

p_{e} | Online recycling price of the recycling center or TPP |

w_{i} | Offline transfer price of the recycling center (i = 1,2) |

w_{e} | Online transfer price of the recycling center |

s_{i} | Service level of online recycling channels, provided by the recycling center or TPP (i = 1,2) |

c_{si} | Service cost of online recycling channels (i = 1,2) |

η_{i} | Service cost coefficient (η > 0, i = 1,2) |

a_{i} | Basic value of the recycling market (i = 1,2) |

m_{i} | Elasticity coefficient (m_{i} > 0, i = 1,2) of recycling amount affected by its own channel recycling price |

k_{i} | Elasticity coefficient (m_{i} > k_{i} > 0, i = 1,2) of recycling amount affected by the recycling price of competitive channels |

h_{i} | Elasticity coefficient (h_{i} > 0, i = 1,2) of recycling amount affected by the service level of its own channels |

n_{i} | Elasticity coefficient (h_{i} > n_{i} > 0, i = 1,2) of recycling amount affected by the service level of competitive channels |

∏_{m} | profit of the recycling center |

∏_{i} | profit of TPR (i = 1,2) |

∏_{e} | profit of TPP |

θ_{1} | w_{e} | p_{1} | p_{2} | p_{e} | s_{1} | s_{2} | ∏_{1} | ∏_{2} | ∏_{e} | ∏_{m} |
---|---|---|---|---|---|---|---|---|---|---|

0.3 | 385.7 | 153.3 | 178.3 | 155.5 | 57.5 | 57.5 | 98,330 | 77,407 | 185,454 | 1,088,690 |

0.4 | 380.4 | 163.9 | 176.4 | 147.3 | 58.3 | 58.3 | 89,128 | 78,885 | 190,042 | 1,089,680 |

0.5 | 375.0 | 174.5 | 174.5 | 139.1 | 59.0 | 59.0 | 80,378 | 80,378 | 194,687 | 1,090,800 |

0.6 | 369.6 | 185.2 | 172.7 | 131.0 | 59.7 | 59.7 | 72,080 | 81,884 | 199,387 | 1,092,040 |

0.7 | 364.3 | 195.8 | 170.8 | 122.8 | 60.4 | 60.4 | 64,234 | 83,405 | 204,144 | 1,093,400 |

θ_{1} | w | p_{1} | p_{2} | p_{e} | s_{1} | s_{2} | ∏_{1} | ∏_{2} | ∏_{e} | ∏_{m} |
---|---|---|---|---|---|---|---|---|---|---|

0.3 | 375.7 | 152.1 | 177.1 | 150.9 | 56.2 | 56.2 | 99,963 | 78,857 | 176,846 | 1,088,440 |

0.4 | 375.3 | 163.3 | 175.8 | 145.0 | 57.6 | 57.6 | 89,904 | 79,616 | 185,659 | 1,089,620 |

0.5 | 375.0 | 174.5 | 174.5 | 139.2 | 59.0 | 59.0 | 80,378 | 80,378 | 194,687 | 1,090,800 |

0.6 | 374.7 | 185.7 | 173.2 | 133.3 | 60.3 | 60.3 | 71,385 | 81,144 | 203,928 | 1,091,980 |

0.7 | 374.3 | 197.0 | 171.9 | 127.4 | 61.7 | 61.7 | 62,926 | 81,913 | 213,384 | 1,093,160 |

θ_{1} | w_{1} | w_{2} | w_{e} | p_{1} | p_{2} | p_{e} | s_{1} | s_{2} | ∏_{1} | ∏_{2} | ∏_{e} | ∏_{m} |
---|---|---|---|---|---|---|---|---|---|---|---|---|

0.3 | 354 | 379 | 383 | 142.3 | 179.8 | 153.1 | 57.5 | 57.5 | 89,812 | 79,529 | 185,454 | 1,089,120 |

0.4 | 365 | 377 | 379 | 158.4 | 177.1 | 146.1 | 58.3 | 58.3 | 85,030 | 79,953 | 190,042 | 1,089,790 |

0.5 | 375 | 375 | 375 | 174.5 | 174.5 | 139.2 | 59.0 | 59.0 | 80,378 | 80,378 | 194,687 | 1,090,800 |

0.6 | 385 | 373 | 371 | 190.7 | 171.9 | 132.2 | 59.7 | 59.7 | 75,857 | 80,804 | 199,387 | 1,092,150 |

0.7 | 396 | 371 | 367 | 206.8 | 169.3 | 125.2 | 60.4 | 60.4 | 71,467 | 81,231 | 204,144 | 1,093,830 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Kang, Y.; Chen, J.; Wu, D.
Research on Pricing and Service Level Strategies of Dual Channel Reverse Supply Chain Considering Consumer Preference in Multi-Regional Situations. *Int. J. Environ. Res. Public Health* **2020**, *17*, 9143.
https://doi.org/10.3390/ijerph17239143

**AMA Style**

Kang Y, Chen J, Wu D.
Research on Pricing and Service Level Strategies of Dual Channel Reverse Supply Chain Considering Consumer Preference in Multi-Regional Situations. *International Journal of Environmental Research and Public Health*. 2020; 17(23):9143.
https://doi.org/10.3390/ijerph17239143

**Chicago/Turabian Style**

Kang, Yao, Juhong Chen, and Di Wu.
2020. "Research on Pricing and Service Level Strategies of Dual Channel Reverse Supply Chain Considering Consumer Preference in Multi-Regional Situations" *International Journal of Environmental Research and Public Health* 17, no. 23: 9143.
https://doi.org/10.3390/ijerph17239143