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Role-based approaches for operational task-resource flexible matching model and algorithm

Zhigang Zou1,2,3,*, Wangfang Che1, Fuling Mu1, Yiqian Chao1, and Bo Zhang2   

  1. 1. Science and Technology on Complex Aviation Systems Simulation Laboratory, Beijing 100076, China;
    2. Air and Missile Defense College, Air Force Engineering University, Xi’an 710051, China;
    3. Department of Information Operation & Command Training, National Defense University of PLA, Beijing 100091, China
  • Online:2017-02-24 Published:2010-01-03


In order to solve the problem that previous relation researches in operational task-resource matching are hard to flexible computing for generating a new matching scheme from the existing scheme under the condition of operational tasks or resources adjusted in uncertainty cases, role-based approaches to operational task-resource flexible matching model and algorithm are proposed in the paper for flexible matching operational tasks with resources. Firstly, through introducing the concept of roles, the role-based calculation framework of operational task-resource flexible matching is constructed. Secondly, two aspect indexes about resource utilization ratio (RUR) and time utilization ratio (TUR) are given to reflect operational task-resource matching quality (TRMQ), which can be regarded as the objective function for modeling role-based operational task-resource flexible matching mathematical formulation. On the basis of this, the role-based artificial bee colony (RABC) algorithm, including five specific calculating operators, is put forward for solving the flexible matching problem with double encoding on operational tasks and resources by roles. Finally, via comparing with the previous methods in application cases, it can be validated that role-based model and proposed algorithm are more effective, which can be used to obtain a new incremental scheme from the existing matching scheme for adopting the operational tasks or resources adjustment. Moreover, there are flexible computation advantages of the approaches proposed in this paper to solve the problem about operational task resource matching in large-scale.