Discrete Dynamics in Nature and Society

Volume 2013 (2013), Article ID 971504, 11 pages

http://dx.doi.org/10.1155/2013/971504

Research Article

Pricing Decisions of a Two-Echelon Supply Chain in Fuzzy Environment

Jie Wei,¹ Guoying Pang,¹ Yongjun Liu,² and Qian Ma¹

¹General Courses Department, Military Transportation University, Tianjin 300161, China

²Military Logistics Department, Military Transportation University, Tianjin 300161, China

Received 8 October 2012; Accepted 1 January 2013

Academic Editor: Xiang Li

Copyright © 2013 Jie Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Pricing decisions of a two-echelon supply chain with one manufacturer and duopolistic retailers in fuzzy environment are considered in this paper. The manufacturer produces a product and sells it to the two retailers, who in turn retail it to end customers. The fuzziness is associated with the customers’ demand and the manufacturing cost. The purpose of this paper is to analyze the effect of two retailers’ different pricing strategies on the optimal pricing decisions of the manufacturer and the two retailers themselves in MS Game scenario. As a reference model, the centralized decision scenario is also considered. The closed-form optimal pricing decisions of the manufacturer and the two retailers are derived in the above decision scenarios. Some insights into how pricing decisions vary with decision scenarios and the two retailers’ pricing strategies in fuzzy environment are also investigated, which can serve as the basis for empirical study in the future.

1. Introduction

There is abundant literature on the pricing/ordering policies for two-echelon supply chain management. Most of them focused on the two-echelon supply chain with one manufacturer/supplier and one retailer/buyer and adopted the following assumption on the channel structure: the manufacturer/supplier wholesales a product to the retailer/buyer who in turn retails it to end consumers. The retail market demand varies with the retail price according to a deterministic/stochastic demand function that is assumed to be known to both the manufacturer and the retailer, and the costs incurred in the manufacturing and inventory process are positive constant numbers. Moreover, a most common gaming assumption on the pricing/ordering decision process is that the manufacturer is a Stackelberg leader and the retailer is a Stackelberg follower (hereafter “MS Game”) in the existing two-echelon supply chain literature. For example, A. H. L. Lau and H. S. Lau [1] studied the effects of different demand curves on the optimal solution of a two-echelon system in the manufacturer-Stackelberg process. They found out that under a downward-sloping price-versus-demand relationship the manufacturer’s profit is the double of the retailer’s. Subsequently, Lau et al. [2] considered a two-echelon system with one manufacturer and one retailer. They presented a procedure for the dominant manufacturer to design a profit maximizing volume-discount scheme with stochastic and asymmetric demand information by modeling this supply chain as a manufacturer-Stackelberg game. Zhao et al. [3] studied the pricing problem of substitutable products in a supply chain with one manufacturer and two competitive retailers.

In fact, in order to make effective supply chain management, uncertainties that happen in the real world cannot be ignored. Those uncertainties are usually associated with product supply, manufacturing cost, customer demand, and so on. The quantitative demand forecasts based on manager’s judgements, intuitions, and experience seem to be more appropriate, and the fuzzy theory rather than probability theory should be applied to model this kind of uncertainties [4]. Zadeh [5] initialized the concept of a fuzzy set via membership function. From then on, many researchers such as Nahmias [6], Kaufmann and Gupta [7], Liu [8], and B. Liu and Y. K. Liu [9] made great contributions to this field. Fuzzy theory provides a reasonable way to deal with possibility and linguistic expressions (i.e., decision maker’s judgements; e.g., manufacturing cost may be expressed as “low cost” or “high cost” to make rough estimates, and market base can be expressed as “large market base” or “small market base” to make rough estimates, etc.).

Many researchers have already adopted fuzzy theory to depict uncertainties in the supply chain model. Zhao et al. [3] considered the pricing problem of substitutable products in a fuzzy supply chain by using game theory in this paper. Xie et al. [10] developed a new two-level coordination strategy that aims to improve the overall supply chain performance through hierarchical inventories control and by introducing a coordination function. They supposed that the supply chain operates under uncertainty in customer demand, which is described by imprecise terms and modelled by fuzzy sets.

This paper extends the current model related to two-echelon supply chain pricing issue from two aspects: one is considering fuzziness associated with customer’s demands and the manufacturing cost; the other is analyzing the effect of the two retailers’ different pricing strategies (e.g., Bertrand, Cooperation and Stackelberg) on the optimal pricing decisions of the manufacturer and the duopolistic retailers in MS Game scenario. First, as a benchmark model, one centralized pricing model (namely, assume that the manufacturer and the duopolistic retailers behave as part of a unified system) is established. Second, based on the two retailers’ different pricing strategies, three decentralized pricing models are constructed in fuzzy environment (e.g., the MSB model where the two retailers implement the Bertrand competition, the MSC model where the two retailers implement the cooperation strategy, and the MSS model where the two retailers implement the Stackelberg competition) and the effect of the two retailers’ different pricing strategies on the pricing decisions of the manufacturer and the two retailers is considered. Third, the closed-form solutions for these models are provided. Finally, we provide numerical examples to show the difference among each firm’s optimal pricing decisions, the difference among each firm’s maximum expected profits, and the variation of each firm’s optimal pricing strategy and maximum expected profit with the two retailers’ pricing strategies and these decision scenarios in fuzzy environment.

The rest of the paper is organized as follows. Section 2 presents preliminaries of fuzzy theory for this paper. Section 3 gives the problem description and notations, and Section 4 details our key analytical results. Numerical studies are given in Section 5. Finally, some concluding comments are presented in Section 6.

2. Preliminaries

A possibility space is defined as a triplet , where is a nonempty set, is the power set of , and Pos is a possibility measure. Each element in is called an event. For each event , Pos indicates the possibility that will occur. Nahmias [6] and Liu [11] gave the following four axioms.

Axiom 1. Pos = 1.

Axiom 2. Pos = 0, where denotes the empty set.

Axiom 3. Pos Pos for any collection in (Θ).

Axiom 4. Let be a nonempty set, on which is the possibility measure satisfying the above three axioms, , then

for each . In that case we write .

Lemma 1 (see [12]). Suppose that is a possibility space. By Axiom 4, is also a possibility space, which is called the product possibility space.

Definition 2 (see [6]). A fuzzy variable is defined as a function from the possibility space to the set of real numbers.

Definition 3 (see [12]). A fuzzy variable is said to be nonnegative (or positive) if (or .

Definition 4 (see [12]). Let be a function and let be a fuzzy variable defined on the possibility space , respectively. Then is a fuzzy variable defined on the product possibility space as for any .

The independence of fuzzy variables was discussed by several researchers, such as Zadeh [13] and Nahmias [6].

Definition 5. The fuzzy variables are independent if for any sets , of ,

Lemma 6 (see [11]). Let be independent fuzzy variable, and function, , then are independent fuzzy variables.

Definition 7 (see [12]). Let be a fuzzy variable on the possibility space , and , then

are called the -pessimistic value and the -optimistic value of , respectively.

Example 8. The triangular fuzzy variable has its -pessimistic value and -optimistic value as

Lemma 9 (see [14]). Let be an independent fuzzy variable defined on the possibility space with continuous membership function, , and a measurable function. If is monotonic with respect to , respectively, then

(a), where , if   is nondecreasing with respect to ; , otherwise; (b), where , if   is nondecreasing with respect to ; , otherwise,

where and denote the -optimistic value and the -pessimistic value of fuzzy variable , respectively.

Definition 10 (see [9]). Let be a possibility space and a set in . The credibility measure of is defined as

where denotes the complement of .

Definition 11 (see [9]). Let be a fuzzy variable; the expected value of is defined as

provided that at least one of the two integrals is finite.

Example 12. The triangular fuzzy variable has an expected value

Definition 13 (see [9]). Let be a function on and let be a fuzzy variable. Then the expected value is defined as

provided that at least one of the two integrals is finite.

Lemma 14 (see [15]). Let be a fuzzy variable with finite expected value. Then

Lemma 15 (see [15]). Let and be independent fuzzy variables with finite expected values. Then for any numbers and , we have

3. Problem Description

Consider a two-echelon supply chain with one monopolistic manufacturer and two duopolistic retailers (retailer 1 and retailer 2) in fuzzy environment. The monopolistic manufacturer manufactures products and sells them to the duopolistic retailers, who in turn retail them to end customers. The manufacturer produces products with unit manufacturing cost , which is a fuzzy variable, and wholesales them to the retailers with unit wholesale price , respectively. The retailer then sells products to end consumers with unit retail price . The manufacturer and retailers must make their pricing decisions in order to achieve the maximum expected profits and behave as if they have perfect information of the demand and the cost structures of other channel members.

Similar to McGuire and Staelin [16], we assume that the demand for each retailer’s product is sensitive to the retail prices of the duopolistic retailers, which uses a set of basic characteristics of the type of demand of each product, for example, downward sloping in its own price, and increases with respect to the competitor’s price. And we assume that all activities occur within a single period. Specifically, the demand faced by the retailer can be expressed as

where are nonnegative fuzzy variables. denotes the primary demand faced by the retailer , denotes the measure of the responsiveness of each retailer’s market demand to the price charged by herself, and denotes the measure of the responsiveness of each retailer’s market demand to her competitor’s price. Here we assume that the fuzzy variables and satisfy , which means that the expected demand for a retailer’s product is more sensitive to the change in its own price than to the change in the price of the other competitor’s product. This assumption is reasonable in reality.

In our model, the manufacturer can influence the market demand by setting his wholesale price, and the retailers can also influence the market demands by making their retail prices, respectively. We assume that the chain members are independent, risk neutral, and profit maximizing. The chain members choose their decisions sequentially in a manufacturer-Stackelberg game (namely, the manufacturer acts as the Stackelberg leader and the duopolistic retailers act as the followers), and they have complete information about the other members. Moreover, the logistic cost components of the chain members (i.e., carrying cost and inventory cost, etc.) are not considered in our paper for analytical convenience. As explained in A. H. L. Lau and H. S. Lau [1], by removing the confounding effect of the logistic cost components, their profit functions are more effective to reveal the effects of different game procedures.

From the above problem description, the manufacturer’s objective is to maximize his expected profit (For convenience, it is for short sometimes in this paper), which can be described as

The objectives of the retailers are to maximize their respective expected profits and (For convenience, abbreviated to and for short sometimes in this paper), which can be described as

4. Analytical Results

4.1. Centralized Pricing Model (CD Model)

As a benchmark to evaluate channel decisions under different decision cases, we first give the centralized pricing model; namely, there is one entity that aims to optimize the whole supply chain system performance, so both the duopolistic retailers’ and the manufacturer’s decisions are fully coordinated in the centralized decision case. The wholesale price charged by the manufacturer is seen as inner transfer price and thus will be neglected. The total profit is determined by the production cost and retail prices.

Let be the total profit of the centralized supply chain; we have

To maximize the system expected profit , the objective is

Proposition 16. In the CD model, the optimal retail prices and are given as

where

Proof. From (15), the expected profit can be expressed as

Then, the first-order derivatives of to and are

and the second-order derivatives are

By (21), together with the assumption , we can get a negative definite Hessian Matrix, so the expected profit is jointly concave in and . Let (20) be equal to zeros; we derive the first order conditions as

So, solving (22) simultaneously, we get the solutions (17), and then the proposition is proved.

4.2. Pricing Models in MS Game

In this section, we assume that the manufacturer acts as the Stackelberg leader and the duopolistic retailers act as the followers. The game-theoretical approach is used to analyze the models established in the following. For this case, the manufacturer chooses the wholesale prices of the product using the response functions of both the retailers. Then, given the wholesale prices made by the manufacturer, the duopolistic retailers determine their retail prices.

4.2.1. The MSB Model

When the two retailers pursue the Bertrand solution, the manufacturer first announces the wholesale price and the two retailers observe the wholesale price and then decide the retail prices simultaneously. Then the MSB model is formulated as

We first derive the retailers’ Bertrand decisions as follows.

Proposition 17. When the duopolistic retailers pursue the Bertrand solution, the optimal retail prices (denoted by and , resp.), given earlier decision made by the manufacturer , are

Proof. Using (13) and (14), we get the expected value of and as

By (25), the first order derivative of to is

and the second order derivative is given below to check for the optimality:

From (28), the expected profit is concave in . Let (27) be equal to zero; we get

Similarly, from (26), we get

Therefore, solving (29) and (30) simultaneously, we get the solutions (24), and thus Proposition 17 is proved.

Having the information about the decisions of the two retailers, the manufacturer would then use them to maximize his expected profit . So, we get the following result.

Proposition 18. When the duopolistic retailers pursue the Bertrand solution, the manufacturer’s optimal wholesale price (denoted by ) is

Proof. By (13), together with some manipulations, we get

Then, from (24) and (32), the first order derivative of to is

Furthermore, its second order derivative satisfies

So, is concave in . Therefore, let (33) be equal to zero; the proposition is proved.

Proposition 19. When the duopolistic retailers pursue the Bertrand solution, their optimal retail prices (denoted by and , resp.) are given as

where is given in Proposition 18.

Proof. By Propositions 17 and 18, we can easily see that Proposition 19 holds.

4.2.2. The MSC Model

In this decision case where the two retailers adopt the cooperation strategy, we assume that the retailers recognize their interdependence and agree to act in union in order to maximize the total expected profit of the downstream retail market. So, the manufacturer first announces the wholesale price and the retailers observe the wholesale price and then decide their retail prices with the objective to maximize the total expected profit of the downstream retail market. Thus, the MSC model is formulated as

We first derive the retailers’ decisions.

Proposition 20. When the two retailers adopt the cooperation strategy, their optimal retail prices and , given earlier decision made by the manufacturer , are

Proof. By (13) and (14), we have

Then

From (40) and the assumption , its Hessian Matrix is negative definite, so the expected profit is jointly concave in and . Let (39) be equal to 0, respectively; we get

Thus, solving (41) simultaneously, we get (37), so the proposition is proved.

Having the information about the decisions of the retailers, the manufacturer would then use them to maximize his expected profit . So, we get the following result.

Proposition 21. When the two retailers adopt the cooperation strategy, the manufacturer’s optimal wholesale price (denoted as ) is

Proof. By (32) and (37), we get

By (44), is concave in . Then, let (43) be equal to zero; we can easily get the proposition.

Proposition 22. When the two retailers adopt the cooperation strategy, their optimal retail prices and are given as follows:

where is given in Proposition 20.

Proof. By Propositions 20 and 21, we can easily see that Proposition 22 holds.

4.2.3. The MSS Model

In this decision case when the duopolistic retailers play Stackelberg Game, we assume that one of the duopolistic retailers (e.g., retailer 1) acts as a Stackelberg leader and the other (i.e., retailer 2) acts as a Stackelberg follower. The manufacturer first announces the wholesale price of the product, and retailer 1 then decides the retail price to maximize her expected profit and retailer 2 finally decides the retail price when knowing both the manufacturer and retailer 1 decisions. So, we first need to derive retailer 2’s decision (as the Stackelberg game’s follower). The MSS model is formulated as follows:

We first derive retailer 2’s decision as follows.

Proposition 23. When the duopolistic retailers play Stackelberg Game, retailer 2’s optimal decision (denoted as ), given earlier decisions made by the manufacturer and retailer 1 which are and , respectively, is

Proof. Using (26), given earlier decisions made by the manufacturer and retailer 1 which are and , respectively, we can have the first-and second order derivatives of to as follows:

By (49), we know that is concave in for given earlier decisions made by the manufacturer and retailer 1 which are and , respectively. Therefore, equating (48) to zero and solving it, we can easily have Proposition 23.

Proposition 24. When the duopolistic retailers play Stackelberg Game, retailer 1’s optimal decision (denoted as ), given earlier decision made by the manufacturer which is , is

where

Proof. Using (25) and (47), given earlier decision made by the manufacturer which is , we can have the first-and second order derivatives of to as follows:

By (53), we know that is concave in for given earlier decision made by the manufacturer which is . Therefore, equating (52) to zero and solving it, we can easily have Proposition 24.

Proposition 25. When the duopolistic retailers play Stackelberg Game, retailer 2’s optimal decision (denoted as ), given earlier decisions made by the manufacturer which is , is

Proof. Using Propositions 23 and 24, one can easily have Proposition 25.

Proposition 26. When the duopolistic retailers play Stackelberg Game, the manufacturer’s optimal decision is (denoted by ) given as follows:

Proof. Using (32), (50), and (54), one can have the first-and second order derivatives of to as follows:

where

By (57), we know that is concave in . Therefore, equating (56) to zero and solving it, we can easily have Proposition 26.

Proposition 27. When the duopolistic retailers play Stackelberg Game, their optimal retail prices (denoted by and , resp.) are given as follows:

where is given as in Proposition 26.

Proof. By Propositions 24, 25, and 26, we can easily see that Proposition 27 holds.

5. Numerical Studies

In this section, we compare analytical results obtained from the above different decision scenarios using numerical approach and study the behavior of firms facing changing fuzzy environment. Here, we assume that the fuzzy variables used in this paper are all triangular fuzzy variables which take values as follows: the manufacturing cost is high ( is about 9), the market bases and are large ( is about 1400 and is about 1200), and price elasticities and are sensitive ( is about 180 and is about 130). More specifically, ,  ,  ,  ,  and  . Similar to Example 8 in Preliminaries (see Section 2), the expected values of the above triangular fuzzy variables can be obtained as follows: , and  ,  . Similar to Example 12 in Preliminaries (see Section 2), the -pessimistic values and -optimistic values of triangular fuzzy variables , , and are ,  ,  ,  ,,,,  ,  , respectively. Using the analytical results obtained in this paper, we can easily have the following numerical results expressed in Tables 1 and 2 when the parameters take the values described above.

Table 1: Maximum expect profit of total system and every firm under different pricing models.

Pricing model

CD model
7696.5

MSB model
7388.7
6301.3
701.1
386.3

MSC model
6163.1
4604.8
1058.1
500.2

MSS model
6778.0
5492.1
546.5
739.4

Table 2: Optimal retail prices and wholesale price under different pricing models.

Pricing model

CD model
17.3190

16.9190

MSB model
19.0790
1.9600
18.5740
1.4549
17.1190

MSC model
21.1405
4.0214
20.7405
3.6214
17.1190

MSS model
20.5076
3.4869
19.0337
2.0129
17.0208

Remark 28. From Tables 1 and 2, we derive the following results when the two retailers have different market bases.

(1.1) The expected profit of the total supply chain in the centralized decision case is higher than that in all decentralized decision cases.(1.2) One can observe directly from Table 1 that different pricing strategies of the two retailers affect the maximum expected profits of the manufacturer and the two retailers. The manufacturer achieves his largest expected profit in the MSB model, retailer 1 achieves her largest expected profit in MSC model, and retailer 2 achieves her largest expected profit in MSS model.(1.3) From Table 1, we can see that the two retailers’ Bertrand action benefits the manufacturer as well as the total supply chain. On the other hand, the two retailers’ cooperation action will always make the manufacturer and the total supply chain obtain the lowest expected profits, which implies that the manufacturer who acts as the leader does not always have the superiority of gaining expected profit in a two-echelon supply chain with two retailers. This is counterintuitive. Therefore, the manufacturer, as a Stackelberg leader, should find a way to induce the two retailers to implement Bertrand policy.(1.4) From Table 1, we can also see that the cooperation action does not always benefit every retailer; for example, retailer 2’s expected profit in the MSC model is lower than that in the MSS model. This insight is helpful to a retailer who is the follower, which tells the retailer that a suitable profit-split should be negotiated with his rival before agreeing to act in union.(1.5) From Table 2 we find that the two retailers’ cooperation behavior will result in the highest unit margins for themselves.(1.6) From Table 2, we can see that the wholesale price in the MSB model is equal to that in the MSC model, which is consistent with the results expressed in both Propositions 18 and 21, and the wholesale price in the MSS model is the lowest one among the three decentralized decision models.(1.7) We observe from Table 1 that the manufacturer’s expected profit is bigger than the sum of the two retailers’ expected profits in the above all Game cases. Moreover, retailer 2’s expected profit is bigger than that of retailer 1 in the MSS model. However, retailer 1’s expected profit is bigger than that of retailer 2 in other Game cases.

In order to see how the two retailers’ different competitive behaviors affect the optimal pricing policy and the total expected profits of the manufacturer and the two retailers, we further assume that the retailers have the same market bases (here we set , which can be intuitively explained as the duopolistic retailers facing the similar market demand. Tables 3 and 4 present the optimal solutions when the two retailers have the same market bases.

Table 3: Maximum profit of total system and every firm under different pricing models.

Pricing model

CD model
5790.5

MSB model
5572.3
4813.9
379.2
379.2

MSC model
4697.6
3604.8
546.4
546.4

MSS model
5212.3
4311.1
289.1
612.1

Table 4: Optimal retail prices and wholesale price under different pricing models.

Pricing model

CD model
15.9286

15.9286

MSB model
17.3701
1.4415
17.3701
1.4415
15.9286

MSC model
19.1548
3.2262
19.1548
3.2262
15.9286

MSS model
18.4646
2.5361
17.7599
1.8313
15.9286

Remark 29. From Tables 3 and 4, we can have the following results.

(2.1) The expected profit of the whole supply chain system in the centralized decision is higher than that in all decentralized decisions. This is consistent with the general case when the two retailers have different market bases.(2.2) One can observe directly from Table 3 that the two retailers’ different pricing strategies affect the total maximum expected profit of the manufacturer and the two retailers. First, retailer 1 achieves her highest expected profit in the RSC model while both the manufacturer and the whole supply chain achieve the lowest expected profits in this case. Secondly, the manufacturer achieves his highest expected profit in the MSB model while retailer 2 achieves her lowest expected profit in this case. This is consistent with the general case when the two retailers have different market bases. Thirdly, from Table 3, one can easily see that both retailer 1 and retailer 2 will achieve the same expected profit in the MSB model. Similar results occur in the MSC model. This is against to the general case when the two retailers have different market bases.(2.3) From Table 3, we can see that the two retailers’ Bertrand action benefits the manufacturer as well as the total supply chain, and the duopolistic retailers’ cooperation action will always make the manufacturer and the total supply chain obtain the lowest expected profits. This is consistent with the general case when the two retailers have different market bases.(2.4) From Table 3, we can also see that action in cooperation does not always benefit every duopolistic retailer; for example, retailer 2’s expected profit in the MSC model is lower than that in the MSS model. This is also consistent with the general case when the two retailers have different market bases.(2.5) One can observe directly from Table 4 that the duopolistic retailers’ cooperation action will result in the highest unit margins for themselves. Moreover, we can have the following insights: firstly, the two retailers achieve the lowest unit margin in the MSB model, followed by the MSS model, then the MSC model; secondly, the two retailers will achieve equal unit margins in the MSB/MSC models. Thirdly, the retail prices charged by the two retailers achieve the highest value the retail prices charged by the two retailers achieve the highest value in the MSC model and achieve the lowest value in the CD model. Finally, the two retailers will charge the same retail price in the MSB and MSC models.(2.6) From Table 4, we can see that the wholesale price charged by the manufacturer in three models does not vary with the two retailers’ pricing strategies. This is against the general case when the two retailers have different market bases.

6. Conclusions

We have analyzed the duopolistic retailers’ and the manufacturer’s pricing decisions by considering the duopolistic retailers’ three kinds of pricing strategies: Bertrand, Cooperation, and Stackelberg in fuzzy environment. As a benchmark to evaluate channel decision in different decision case, we first developed the pricing model in centralized decision case and derived the optimal retail prices. We then established the pricing models in decentralized decision cases by considering the duopolistic retailers’ three kinds of pricing strategies and obtained the analytic equilibrium decisions. Finally, we provided comparison of the expected profits and optimal pricing decisions of the whole supply chain and every supply chain members in both the general case (namely, the two retailers have different market bases) and the special case (viz., the two retailers have the same market bases). The analytical and numerical results revealed some insights into the economic behavior of firms.

Our results, however, are based upon some assumptions about the two-echelon supply chain models. Thus, several extensions to the analysis in this paper are possible by considering the duopolistic retailers’ three kinds of pricing strategies. First, as opposed to the risk neutral two-echelon supply chain members considered in this paper, one could study the case where the supply chain members with different attitudes toward risk and could also examine the influence of their attitudes toward risk on individual expected profits and the expected profit of the whole supply chain. This would add complications to the analysis of the two-echelon supply chain members’ decisions. Second, we assumed that both the duopolistic retailers and the manufacturer have symmetric information about costs and demands. So, an extension would be to consider the two-echelon supply chain models in information asymmetry, such as asymmetry in cost information and demand information. Finally, we can also consider the coordination of the two-echelon supply chain under linear or isoelastic demand with symmetric and asymmetric information.

Acknowledgment

The authors wish to express their sincerest thanks to the editors and anonymous referees for their constructive comments and suggestions on the paper. This research was supported in part by the National Natural Science Foundation of China, nos.: 71001106, 70971069.

References

A. H. L. Lau and H. S. Lau, “Effects of a demand-curve's shape on the optimal solutions of a multi-echelon inventory/pricing model,” European Journal of Operational Research, vol. 147, no. 3, pp. 530–548, 2003.

A. H. L. Lau, H. S. Lau, and Y. W. Zhou, “A stochastic and asymmetric-information framework for a dominant-manufacturer supply chain,” European Journal of Operational Research, vol. 176, no. 1, pp. 295–316, 2007.

J. Zhao, W. Tang, R. Zhao, and J. Wei, “Pricing decisions for substitutable products with a common retailer in fuzzy environments,” European Journal of Operational Research, vol. 216, no. 2, pp. 409–419, 2012.

H. J. Zimmermann, “Application-oriented view of modeling uncertainty,” European Journal of Operational Research, vol. 122, no. 2, pp. 190–198, 2000.

L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8, pp. 338–353, 1965.

S. Nahmias, “Fuzzy variables,” Fuzzy Sets and Systems, vol. 1, no. 2, pp. 97–110, 1978.

A. Kaufmann and M. M. Gupta, Introduction to Fuzzy Arithmetic: Theory and Applications, D. Van Nostrand Reinhold Company, New York, NY, USA, 1985.

B. Liu, “A survey of credibility theory,” Fuzzy Optimization and Decision Making, vol. 5, no. 4, pp. 387–408, 2006.

B. Liu and Y. K. Liu, “Expected value of fuzzy variable and fuzzy expected value models,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 4, pp. 445–450, 2002.

Y. Xie, D. Petrovic, and K. Burnham, “A heuristic procedure for the two-level control of serial supply chains under fuzzy customer demand,” International Journal of Production Economics, vol. 102, no. 1, pp. 37–50, 2006.

B. Liu, Uncertainty Theory: An Introduction to its Axiomatic Foundations, Springer, Berlin, Germany, 2004.

B. Liu, Theory and Practice of Uncertain Programming, Physica, Heidelberg, Germany, 2002.

L. A. Zadeh, “Fuzzy sets as a basis for a theory of possibility,” Fuzzy Sets and Systems, vol. 1, no. 1, pp. 3–28, 1978.

C. Wang, W. Tang, and R. Zhao, “On the continuity and convexity analysis of the expected value function of a fuzzy mapping,” Journal of Uncertain Systems, vol. 1, no. 2, pp. 148–160, 2007.

Y. K. Liu and B. Liu, “Expected value operator of random fuzzy variable and random fuzzy expected value models,” International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 11, no. 2, pp. 195–215, 2003.

T. W. McGuire and R. Staelin, “An industry equilibrium analyses of down stream vertical integration,” Marketing Science, vol. 2, no. 2, pp. 161–191, 1983.