It is difficult to obtain the overall optimal solution by the traditional order batching and the picking path step-by-step optimization of picking operation in the logistics distribution center. In order to improve the efficiency of picking operation, a joint picking strategy based on nested genetic algorithm for order batching and path optimization was proposed. Firstly, the joint optimization model of order batching and picking path was established with the shortest total picking time as the objective function. Then, a nested genetic algorithm was designed to solve the model with the consideration of the complexity of double optimizations. The order batching result was continuously optimized in the outer layer, and the picking path was optimized in the inner layer according to the order batching result in the outer layer. Results of the examples show that, compared with the traditional strategies of order step-by-step optimization and step-by-step optimization in batches, the proposed strategy has reduced the picking time by 45.6% and 6% respectively, and the joint optimization model based on nested genetic algorithm results in shorter picking path and less picking time. To verify that the proposed algorithm has better performance on orders with different sizes, the simulation experiments were performed to the examples with 10, 20, 50 orders respectively. The results show that, with the increase of order quantity, the overall picking distance and time are further reduced, the decrease of picking time is risen from 6% to 7.2%.The joint optimization model of picking operation based on nested genetic algorithm and its solution algorithm can effectively solve the joint optimization problem of order batching and picking path, and provide the basis for the optimization of picking system in the distribution center.

In order to reveal the evolution rules of supply chain network with the core of manufacturers, a kind of five-level local world network model was put forward. This model used the BA model and the multi-local world theory as the foundation, combined with the reality of network node generation and exit mechanism. First of all, the intrinsic characteristics and evolution mechanism of network were studied. Secondly, the topology structure and evolution rules of the network were analyzed, and the simulation model was established. Finally, the changes of network characteristic parameters were simulated and analyzed in different time step and different critical conditions, including nodes number, clustering coefficient and degree distribution, then the evolution law of the network was derived. The simulation results show that the supply chain network with the core of manufacturers has the characteristics of scale-free and high concentration. With the increase of time and the growth rate of the network nodes, the degree distribution of overall network approaches to the power-law distribution with the exponent three. The degree distribution of the network at all levels is different, sub-tier suppliers and retailers obey power-law distribution, suppliers and distributors obey exponential distribution, manufacturers generally obey the Poisson distribution.