| In the 21st century, the competition between supply chain and supply chain is the most significant one. The establishment of the supply chain is based on its added value. Even tiny disorderly operations of any node in the supply chain, can affect the entire supply chain performance. Supply Hub, which undertakes the upstream enterprise assemble task and the downstream distribution task, directly affects the operational efficiency of co-operation with the entire supply chain. In order to optimize network resource allocation and coordination of the logistics division of tasks, Supply Hub is proposed to solve job scheduling problem. An excellent job scheduling strategy within supply hub plays an important role in improving responsiveness and increasing competitiveness.According to basic theory and operation scheduling algorithm, this basis set a job scheduling optimization model for supply hub. In this model, every factor is considered, including limited resources and the different requirements of customers on product cost, time, urgency of the logistics operations. To solve the model, a global spatial search and the advantages of implicit parallelism of genetic algorithms are used. The optimization goal of this model is to set rational allocation of resources, in accordance with product requirements and resources constraints. Logistics activities identified for each specific line of work, time, action and so on are also part of the goal. The model, considering time and cost factors, obtained by genetic manipulation of the logistics activities for the order and execution mode. Customers are grouped, in considering the overall needs of client characteristics on the impact of customer satisfaction from the perspective of qualitative and quantitative way. For each customer group, an optimal way of job scheduling is set. Matlab genetic algorithm toolbox is used in the process to solve the model. The case study to validate the proposed model and algorithm are effective and reasonable.
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