Optimizing joint technology selection, production planning and pricing decisions under emission tax: A Stackelberg game model and nested genetic algorithm

In practice, manufacturers and their independent retailers in dyadic supply chains jointly make decisions by capitalizing on decision interactions while respecting the carbon emission tax and subsidy determined by local governments. Though studies have been published to address the joint decision-making, they involve only a very few of the important supply chain decisions due to the problem complexities. In this study, we, therefore, investigate a comprehensive joint decision-making of a manufacturer and his independent retailer considering both carbon emission tax and subsidy offered by the local government. The decisions in our study include i) the manufacturer's technology selection, production quantities, and wholesale price and ii) the retailer's retail price. Per the decision interactions, we analyze the decision-making problem as a Stackelberg game. The game model developed, by nature, is a bilevel 0-1 mixed nonlinear programming, and cannot be solved analytically. Considering its complexities, we further develop a nested genetic algorithm (NGA) to solve the model. Numerical examples demonstrate the applicability of the Stackelberg game model in facilitating supply chain members to jointly make decisions and the robustness of the NGA. With sensitivity analysis, we shed light on several important managerial implications, such as, manufacturers need to identify "optimal" ranges of emissions released from producing a unit of green (or dirty) product to obtain higher profits; manufacturers need to control well their production processes so that emissions released from producing a unit of product from a green (or dirty) technology fall in "optimal" ranges contributing to higher profits.