## 7 Conclusions and Further Research  
In this paper, we describe an event-based supply chain partnership integration model which lists the events related with all phases of cooperation with partners and puts events into a dynamic supply chain network to obtain factors influencing supply chain partnership integration. Based on the indices of trustworthiness, supplier's service, qualified products rate and cost of supply chain, a multi-objective optimization model is established. A hybrid algorithm PSACO that employs particle swarm optimization (PSO) and ant colony optimization (ACO) is introduced to solve the integration  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Supply Chain** & \multicolumn{2}{c}{**Cooperated Suppliers**} & \multicolumn{2}{c}{**Objective Function Values**} \\ \hline \multirow{8}{*}{PSO} & 1 & \(s_{1,13}\)\(s_{2,6}\)\(s_{3,14}\)\(s_{4,4}\)\(s_{5,6}\)\(s_{6,1}\)\(s_{7,14}\)\(s_{8,5}\)\(s_{9,10}\)\(s_{10,2}\) & 1.4844 \\  & 2 & \(s_{1,13}\)\(s_{2,6}\)\(s_{3,14}\)\(s_{4,4}\)\(s_{5,6}\)\(s_{6,12}\)\(s_{7,4}\)\(s_{8,5}\)\(s_{9,10}\)\(s_{10,2}\) & 1.4872 \\  & 3 & \(s_{1,13 model. To verify the rationality and validity of our model and PSACO algorithm, a simulation experiment using actual data of computer assembling was designed to assess the performance of PSACO in the comparisons with that of PSO and ACO. Analysis of the experimental results shows that PSACO provides better solution and has a higher efficiency than PSO and ACO. By implementing the event-driven model with a PSACO algorithm, a B2B platform can provide supply chain coordination scheme in a dynamic manner, as in most cases it will have to be adjusted under a disruption scenario. The parties in the supply chain fully coordinate their decisions for the partner selection based on the real-time values on the key events which are identified by the retrospective steeling wheel analysis. The event-based model provides more accurate information to reduce the uncertainties and increase visibility in the supply chain. Enterprises use this information to make better decisions and achieve better integration with downstream and upstream suppliers. It is an effective tool for enterprises to build trust relationships. Our approach adapts an objective method to determine the index weights and creates several alternatives for partnership integration based on an objective method which considers both the difference and correlation between indicators. This is different from the approach in the work of [PERSON] and [PERSON] [5] which depends on the experts' experience which results in high subjectivity. They choose alternatives for integration by using sensitivity analysis for each factor individually and try to determine the outcome. There are some limitations and problems that are worth further discussing and improving upon. Firstly, we only considered simple events in the model. Future research should push limitations by adopting more complex events such as competitions between suppliers, bullwhip effect, and considering a different order distribution decision, such as a trust-based decision, a price-based decision rule, etc. In addition, a pull-model supply chain is considered in this paper. In reality, however, every supply chain cannot rely entirely on either push or pull strategy. Further study should employ a mixture of the two strategies to make the model reflect actual operation conditions of a supply chain more factually. Secondly, a linear decreasing inertia weight is considered in the proposed algorithm. However, according to [PERSON] and [PERSON] [49], chaotic inertia weight is the best strategy for better accuracy and random inertia weight strategy is better for better efficiency. It is possible to use these strategies to enhance the performance of the algorithm. Meanwhile the optimal setting of parameters in the PSO has not yet been considered.