The Study On Lot-Sizing Planning Of Scrap Steel Hot-Rolling With Carbon Emission Constraint

Iron and steel industry is an important pillar of the national economy, but also is energy-hungry. Steel companies released a lot of carbon dioxide during the manufacturing process, which has a negative effect on the environment. In addition, iron and steel industry is faced with structural surplus capacity, iron ore resource constraints and so on. In this case, scrap steel remanufacturing with carbon emissions can not only save iron ore resource, turn surplus capacity into effective production capacity, but also can help steel companies save energy and reduce emission. Therefore, the study of scrap steel remanufacturing with carbon emission is of great importance.Hot-rolling is one of the key processes of steel manufacturing, and hot-rolling lot planning is designed to determine the combination and sequence of slab while satisfying the production constraints. Scientific and reasonable production plan can effectively improve product quality, reduce completion time, reduce energy consumption and achieve the overall process optimization. Currently, there are many researches on hot-rolling lot planning both at home and abroad, but literatures on hot-rolling lot planning which considers energy-saving and carbon emission is still relatively rare.Based on the related researches at home and abroad, firstly, the research status of hot-rolling lot planning was summarized. At the same time, scrap steel remanufacturing, hot-rolling production process, hot-rolling batch planning problem, and other questions were described systematically. Hot rolling batch planning problem is often viewed as multiple travelling salesman problem (MTSP) or the vehicle routing problem (VRP). In order to make products of high quality, penalty value is minimized as the optimization goal. Secondly, how to build the model of hot-rolling lot planning problem was introduced, and the method to solve the model was selected and explained. There are many intelligent optimization algorithms to solve hot-rolling lot planning model, such as genetic algorithms, ant colony algorithms, particle swarm algorithms and so on, among which genetic algorithms was selected to solve hot-rolling lot planning in this paper. And steps to solve the hot planning lot problem with genetic algorithms were introduced. Thirdly, energy-saving based scrap steel hot-rolling lot planning problem was studied, which batch policy was combined into. Hot-rolling lot planning was made by orders in this paper, and an optimization model was established to minimize the energy consumption. This problem was solved by improved genetic algorithm. Meanwhile, the validity of the model was verified by the analysis of an example. Finally, slab hot-rolling lot planning with carbon constraints was proposed. Carbon emission trading mechanism was introduced in this part, and a multi-objective model was established aiming at minimizing the carbon cost and optimizing product quality at the same time. In the same way, this model was solved by genetic algorithm, after which an example was analyzed in order to verify the validity of the model. And the results offered the managers some advice on production decisions.