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.

An Evolutionary Game Model in ClosedLoop Supply Chain An evolutionary game model is investigated to study the stability conditions for four different reverse channel structures in the closedloop supply chain. The proper channel structures are analyzed for the given conditions. We consider one centrally coordinated model and three decentralized models that consist of manufacturer collection, retailer collection, and thirdparty collection model. The profit function is maximized for the centralized model and Stackelberg equilibriums are obtained for the other three decentralized models. Using the optimal profit functions, an evolutionary game model is proposed. On the basis of the stable conditions, we propose a profit sharing allocation method that can make the centralized supply chain model stable from a longterm view. Also, several numerical experiments are conducted. The results show that the coordinated channel structure is preferable over other structures with a proper profit sharing allocation method.