Liability Cost Sharing, Product Quality Choice, and Coordination in TwoEchelon Supply Chains Abstract   We consider a twoechelon supply chain in which an upstream manufacturer (M) and a downstream retailer (R) share the product liability cost caused by quality defects. We assume that M and R share the expected liability cost per unit of the lowquality product (the ULQ liability cost). We use two respective Stackelberg games to model two different channel leadership structures: the manufacturerled Stackelberg (MS) and the retailerled Stackelberg (RS). We first obtain the analytical subgame perfect equilibria for the games. Subsequently, we investigate the impacts of the ULQ liability cost sharing on the product quality and the pricing decisions and on the profitability for supply chain members and the entire supply chain in equilibrium. We find that (1) in response to a shift of the share of the ULQ liability cost from R to M, the leader M under the MS structure is able to use a higher wholesale price to transfer its increased ULQ liability cost completely to R without the need to improve its product quality, while the leader R under the RS structure would decrease its retail margin to incentivize M to improve its product quality; (2) such a shift of the ULQ liability cost sharing does not have any impact on the supply chain efficiency under the MS structure, but it enhances the supply chain efficiency under the RS structure; (3) despite the different responses of the leaders under the MS and the RS structures, we are able to establish a same set of contracts combining quantity discounts with quality improvement cost sharing to coordinate the supply chain, regardless of the channel leadership structures.

Optimizing Joint Production Planning, Pricing and Retailer Selection with Emission Control based on Stackelberg Game and Nested Genetic Algorithm We address the joint decision making of production planning, pricing, and retailer selection while considering emission control. In view of the interactive decisionmaking processes, we adopt the Stackelberg game theory and develop a 0xe2x80x931 mixed nonlinear bilevel programming model to maximize the profits of a manufacturer and his retailers. Involving two submodels representing the decisionmaking processes of the manufacturer and retailers, respectively, the Stackelberg game model is very complex and impossible to be solved analytically. Therefore, we further develop a nested genetic algorithm (NGA) to solve the game model. Numerical examples demonstrate (i) the applicability of the Stackelberg game model in making joint decisions and (ii) the robustness of the NGA. Sensitivity analysis sheds light on several important managerial implications.