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Precise control of hospital pharmacy inventory is an important guarantee for hospital. The optimal inventory policy for a multi-level medicine inventory control in hospital pharmacy was considered in this paper, and modeling simulation technology and intelligent optimization has been used. Analyzed with large-scale hospitals data, this paper has discussed the following problems: random fluctuations of demand in hospital departments, total volume limits and uncertainty response time of suppliers’ delivery. Establish multilevel pharmaceutical inventory simulation model, then use PSO method for intelligent optimization. Examples of simulation models and PSO algorithms verified by numerical results show that: when each department was tested randomly varying demand of the patients, multi-level inventory management must be overall coordination in order to achieve optimum system.

A simple incentive mechanism based on penalties and bonuses to enhance supply chain networks (SCNs) cooperation is introduced. SCNs is regarded as one "learning" gradually evolutionary system, and game theory is applied to research the co-competition evolutionary of SCNs. Firstly, SCNs topology model is presented based on the analysis of small world and scale free characters. And secondly, the co-competition evolutionary game model is constructed mixed the penalties and bonuses incentive mechanism. Finally, the simulation results illuminate the incentive mechanism could accelerate cooperation emergency and advance the cooperation levels of SCNs.

It is known that a consensus problem on any connected complete Riemannian manifold can be transformed into the one on its strongly convex balls via the compression–decompression along geodesics. From the viewpoint of interior metrics, this paper mainly provides a consensus protocol for strongly convex geodesic balls, in which the communication can be switching and directed. With the aid of nonsmooth analysis tools on Riemannian manifolds, our analysis shows that all dynamical points involved can achieve consensus in finite time. Meanwhile, the corresponding global algorithm is given, with its application to the consensus problem of rotation attitudes, as well as a case simulation, to demonstrate and verify our proposed techniques. Refereed/Peer-reviewed