title: A novel medical information management and decision model for uncertain demand optimization.
abstract: BACKGROUND
  Accurately planning the procurement volume is an effective measure for controlling the medicine inventory cost. Due to uncertain demand it is difficult to make accurate decision on procurement volume. As to the biomedicine sensitive to time and season demand, the uncertain demand fitted by the fuzzy mathematics method is obviously better than general random distribution functions.
  
  
  OBJECTIVE
  To establish a novel medical information management and decision model for uncertain demand optimization.
  
  
  METHODS
  A novel optimal management and decision model under uncertain demand has been presented based on fuzzy mathematics and a new comprehensive improved particle swarm algorithm.
  
  
  RESULTS
  The optimal management and decision model can effectively reduce the medicine inventory cost.
  
  
  CONCLUSIONS
  The proposed improved particle swarm optimization is a simple and effective algorithm to improve the Fuzzy interference and hence effectively reduce the calculation complexity of the optimal management and decision model. Therefore the new model can be used for accurate decision on procurement volume under uncertain demand.
authors.affiliation: College of Public Administration, Huazhong University of Science and Technology, Wuhan, Hubei, China.|School of Logistics and Engineering Management, Hubei University of Economics, Wuhan, Hubei, China E-mail: biya_hut@163.com.
authors.forename: Ya
authors.initials: Y
authors.lastname: Bi
mesh_terms: D000465:Algorithms; D003661:Decision Support Techniques; D004864:Equipment and Supplies; D017143:Fuzzy Logic; D006801:Humans; D019451:Information Management / Q000379:methods*
keywords: Medical information system; fuzzy mathematics; optimal procurement model; uncertain demand