Optimization Of Production Planning And Emission-Reduction Policy-Making

Carbon emission reduction has become a consensus by the international community, since the environmental deterioration caused by the increasing carbon emissions seriously affects the social and economic development around the world. Emission-reduction issues are also receiving increasing attention from the academic community, especially in the Operations Research/Management Science (OR/MS) research area, due to the opportunities and challenges they offer.This research focuses on carbon emission-reduction issues in a local region, in which a local government imposes emission-reduction policies on manufacturers located in this region. Policymaking decision problems for the government and the production planning decision problems for the manufacturers are investigated by OR/MS research approaches. Two types of emission-reduction policies, including emission-cap regulation policy and emission cap-and-trade scheme, are considered in this research.Firstly, we discuss manufacturers’ long-term strategic decision problems considering the government-imposed emission-cap regulation policy. With an objective of maximizing the manufacturers’ profits, Stackelberg game models are formulated to optimize their decisions on carbon footprint, wholesale price and retailer selection under the governmental policy. The problem is proven to be NP-hard. Therefore, a hybrid algorithm combining genetic algorithm, dynamic programming approach and analytical methods is developed to solve the proposed model.Secondly, we investigate manufacturers’ medium-term operational decision problems considering emission-reduction policies to minimize their overall costs. Production planning and technology selection are optimized by mixed integer linear programming models. In addition, an allowance trading strategy is studied under the emission cap-and-trade scheme. The equivalent production cost functions in the models are non-continuous, and make the problems difficult to solve. However, a polynomial dynamic programming algorithm is developed, and can solve the problems under both policies in O(T6) time, where T is the number of periods involved in the planning horizon.Thirdly, we study the government’s policymaking decision problem to maximize the social welfare of the local region. Stackelberg game models are formulated to optimize the emission-reduction policies by considering manufacturers’ operational decisions in response to the governmental policies. Hybrid algorithms integrating genetic algorithm and polynomial dynamic algorithms are developed for the models. In particular, Cournot competition models are formulated to optimize the carbon price under the emission cap-and-trade scheme.Lastly, numerical analyses are conducted to illustrate the application of all models and algorithms proposed in this research. The computation results show that the algorithms developed in this research can solve the proposed problems efficiently and effectively. Some interesting and valuable managerial insights are explored from computational results and sensitivity analyses.