Abstract: Recent years the great effect of economic activity on environmental degradation has been witnessed. Many policies have been made to control the carbon emissions, and the best-known is the Kyoto Protocol, The carbon emissions are gradually translated into regulations, which will put some press on the supply chain members. For another, some enterprises with social responsibility have engaged in the voluntary emission reduction programs, for example, company such as BP and Nike have took some actions on reducing emissions to improve their public image. However, reducing the carbon emissions of supply chain's operations provides a huge opportunity. As we know, location, routing and inventory are the key drives of cost and carbon emissions; the collaboration of them is the focus of this paper. The 3-stage supply chain network consists of plants, potential RDCs, and DCs. The carbon cap C^CAP is from the regulatory organization, and the actually emissions are CE_L,CE_Rand CE_Ifrom location, routing and inventory, respectively. So the carbon-capped difference (CCD) is CE_L+CE_R+CE_R-C^CAP. If the CCD is negative, the supply chain members must buy the carbon credit from the carbon market to make up the shortage; else, they can make a profit on the redundant carbon cap. The buying carbon emissions e⁺ and selling carbon emissions e^- must satisfyCE_L+CE_R+CE_I+e^-≤C^CAP+e⁺. When the cap and trade mechanism conducts, the CCD can translate into money. Given that premise, the collaboration of location-routing-inventory model is presented. The aim of this paper is to find an optimal decision minimizing the cost and emissions in supply chain operations. The combinatorial optimization BFA-PSO algorithm is presented to obtain the optimal solution of the proposed model. The case study from part of the northeast petrochemical sales company of CNPC has verified the validity and practicability of the model and algorithm. To compare the effect of CCD on the results, the solutions of joint optimization are also developed without carbon emissions. It can be seen that the joint optimization of location-routing-inventory with CCD considering is superior to the pure one without carbon emissions considering when the carbon emissions can be trade on the carbon market. Even though the carbon emissions are worthless on a commercial footing, the cost of CCD situation is slightly more, but it has a tremendously abatement. That is to say, it is possible to significantly reduce emissions with slightly increasing cost by supply chain operation management. It can be foreseen that the regulatory policy about carbon emissions will be more and more severe in future, this model and algorithm solved in this article shed a light on the trade-off between the cost and emissions, and also provide a way to reduce carbon emissions by operation adjustment.

Key words: carbon-capped difference, optimizing model of location-routing-inventory, carbon emission, BFA-PSO combinatorial optimization algorithm