Fuzzy Bi-level Decision-Making Techniques: A Survey <s> Supply chain and logistics management <s> With the deepen of market competition, product pricing and production decision problem in many firms have become more and more important. A bilevel model is proposed to describe the pricing and production decisions with fuzzy demand and fuzzy cost parameters. The upper level is to determine the optimal price and production quantity with capacity constraints. Using this information, the lower level problem tries to structure a response (the distribution pattern of customers (or markets)) that will satisfy his demand at minimum cost. And after transforming the fuzzy numbers into the crisp value by Graded Mean Integration Representation method, the solution algorithm based on difference method is given. Finally, the application of the model and its algorithm are illustrated with a simple example. <s> BIB001 </s> Fuzzy Bi-level Decision-Making Techniques: A Survey <s> Supply chain and logistics management <s> This paper considers the model for the newsboy problem with fuzzy demands and quantity discounts in hierarchical decision system, in which the manufacturer is considered as the decision-maker (DM) of the top level and decides the wholesale prices of newspaper in order to maximize his profit, while the retailers are the DMs of the lower level and decide the ordering quantities of newspaper in order to maximize their own expected profits. A bilevel programming model is formulated for this kind of problem. To solve the model, a hybrid intelligent algorithm based on genetic algorithm and fuzzy simulation is developed, and some numerical examples are given to illustrate its effectiveness. <s> BIB002 </s> Fuzzy Bi-level Decision-Making Techniques: A Survey <s> Supply chain and logistics management <s> Due to rapid technological innovation and severe competition, the upstream component price and the downstream product cost in hi-tech industries usually decline significantly with time. In building a pricing supply chain model, some coefficients are generally obtained from experiments and cannot be defined as crisp numbers. Thus, an effective fuzzy pricing supply chain model becomes crucial. This paper establishes a fuzzy bi-level pricing model for buyers and vendors in supply chains. Then, a particle swarm optimization (PSO) based algorithm is developed to solve problems defined by this model. Experiments show that this PSO-based algorithm can solve fuzzy bi-level pricing problems effectively. <s> BIB003
In terms of applications in supply chain and logistics management, Gao et al. BIB003 established a fuzzy bi-level decision model for buyers and vendors in a pricing supply chain that some coefficients are generally obtained from experiments and cannot be defined as crisp numbers. A PSO algorithm was developed to solve the resulting model. Sun and Gao BIB001 proposed a bilevel decision model to handle pricing and production decisions with fuzzy demand and fuzzy cost parameters. The upper level (leader) aims to determine the optimal price and production quantity with capacity constraints, whereas the lower level (follower) problem tries to structure the distribution pattern of customers with the aim of satisfying the demand at minimum cost. Ji and Shao BIB002 addressed a bi-level decision model for a newsboy problem with fuzzy demands and quantity discounts in a hierarchical decision system, in which manufacturer at the upper level decides the wholesale prices of newspaper in order to maximize its profit, while retailers at the lower level decide ordering quantities of newspaper in order to maximize their own expected profits. Zhang et al.  applied fuzzy bi-level optimization techniques to model and support bidding decision-making between the generating company and the market operator in electricity markets. Lan et al.  studied an optimal taxation problem with asymmetric information in which the government and the monopolist are principals while the consumer is their agent. The authors formulated this problem as a fuzzy nonlinear bi-level decision model with the purpose of maximizing the expected social welfare and the monopolist's expected welfare under an incentive feasible mechanism. Author's Names / Paper Title (4 Words maximum)