This research focuses on carbon emission-reduction issues in an area where the government imposes emission-reduction policies on local manufacturers. Policymaking problems for the government and production planning problems for the manufacturers are investigated with Operations Research/Management Science (OR/MS) approaches. Two types of emission-reduction policies, including emission-cap regulation policy and emission cap-and-trade scheme, are addressed. We first discuss manufacturers' long-term strategic decision problem under the government-imposed emission-cap regulation policy. With the objective of maximizing the manufacturers' profits, Stackelberg game model is formulated to optimize their decisions on carbon footprint, wholesale price and retailer selection. The problem is proven to be NP hard and a hybrid algorithm is developed to solve the model. We then investigate manufacturers' medium-term production planning to minimize the total production and inventory holding cost, by considering emission-reduction constraints through technology selection, some of the technologies being green. The problems are shown to be polynomially solvable. Based on these results, we study the government's policymaking problems to maximize the social welfare of the area. Stackelberg game models are formulated to optimize the emission-reduction policies by anticipating manufacturers' operational decisions in response to the governmental policies. Hybrid algorithms are developed to solve the problems. For each studied problem, numerical analyses are conducted to evaluate the algorithms. The computation results show that the algorithms developed in this research are effective. Some interesting and valuable managerial insights are drawn from computational results and sensitivity analyses.
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00978457 |
| Date | 12 November 2013 |
| Creators | Hong, Zhaofu |
| Publisher | Ecole Centrale Paris |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
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