Aimed at determining whether China’s active carbon emission reduction policy can respond to the threat of carbon tariff of the USA, this study proposed two kinds of carbon tax schemes for the USA and China, same carbon tax policy and a differential carbon tax policy. Four scenarios are set: the USA only charging carbon tax on domestic products; the USA charging carbon tax on domestic products and carbon tariff on imported products from China; the USA and China taking the same carbon tax policy on domestic products; the USA and China taking the differential carbon tax policy on their domestic products. Global Trade Analysis Project Energy model is applied to discuss whether China’s active measure to reduce carbon emissions can be an effective solution to the threat of carbon tariff of the USA. The research results show that China’s active measure of the same carbon tax policy as the USA is not effective to cope with carbon tariff of the USA. However, it is an effective measure to take a differential carbon tax policy. The specific policy implications of the study are discussed in conclusion.

This paper will present a simulation-optimization model for the scheduling of multi-projects. The objectives of this research include the minimization of value added projects execution cost, project completion time, project tardiness, and underutilization of contracted or outsourced resources. It is the three-phase research. In first phase, a mathematical and simulation models will be developed for multi-objectives. In second phase simulation model will be coupled with genetic algorithm to form a simulation-optimization model. The efficiency of genetic algorithm (GA) will be improved simultaneously with fine-tuning and hybridizing with other algorithms. The third phase will involve the presentation of a numerical example for the real time application of proposed research. Solution of numerical obtained with fine-tuned and hybridized simulation integrated GA will be compared with already available methods of simulation-optimization. This research will be useful for the scheduling of projects to achieve the befits of high profit, effective resource utilization, and customer satisfaction with on time delivery of projects.

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization with multiple objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA.