Simulation Training Center

Xuanhua, China

Simulation Training Center

Xuanhua, China
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Su X.-J.,Ordnance Engineering College | Lyu X.-Z.,Simulation Training Center
Binggong Xuebao/Acta Armamentarii | Year: 2017

An algorithm of phased-mission system with multiple states is presented. The concepts of phased-mission system and its features are discussed, and an active standby (AS) tree structure method is proposed to depict the system structure of each phase. The behaviors of phased-mission system with multiple states are discussed based on state chart. According to discrete event simulation concept, a simulation algorithm is presented to estimate the reliability parameters of phased-mission system with multiple states. A case-study is introduced to verify the algorithm. The proposed simulation algorithm is very practical and versatile. The algorithm can be used to describe the complex behaviors of phased-mission system flexibly and obtain the more reliability parameters to understand the system operation. © 2017, Editorial Board of Acta Armamentarii. All right reserved.


Lyu X.-Z.,Simulation Training Center | Chen L.,Academy of Equipment Command and Technology | Yin J.,Simulation Training Center | Fan B.-X.,Simulation Training Center
Binggong Xuebao/Acta Armamentarii | Year: 2014

As the maintenance resources are periodically used, namely, they must have rest after running for a period of time, a maintenance task usually is urgent, which must be going on without interruption, how to give out maintenance task scheduling plan and rest times of maintenance resources at same time is an issue being worth to be discussed. A hybrid integer-programming model is established based on the model assumption. A PSO-based solving algorithm is proposed, which includes algorithm framework, particle representation, resources-skills allocation algorithm, particle decoding algorithm and update methods. The validity and feasibility of the model and resolving algorithm are verified by an example.


Lu X.-Z.,Ordnance Engineering College | Lu X.-Z.,Simulation Training Center | Yu Y.-L.,Ordnance Engineering College | Zhang L.,Ordnance Engineering College | And 3 more authors.
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2013

In order to get optimum solution of mission assignment and cannibalization, it puts forward a nonlinear programming model to optimize mission assignment and cannibalization solution, the factors such as mission requirements to equipment subsystem, real condition of equipment system, maintenance resource are considered in the model, the model harmonize the mission, equipment fleet and maintenance resource to the utmost, so it is more practical. It designs the solution algorithm based on particle swarm optimization (PSO), includes algorithm framework, particle representation, initialization, fitness function and update methods. Finally, applies the algorithm to a specific example, analysis shows that the model and algorithm can optimize the mission assignment and cannibalization solution effectively and efficiently, increase the mission success probability, and provide instruction for decision maker in decision-making.


Lu X.-Z.,Ordnance Engineering College | Lu X.-Z.,Simulation Training Center | Yu Y.-L.,Ordnance Engineering College | Zhang L.,Ordnance Engineering College | And 2 more authors.
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | Year: 2013

Accompanying repair is an important maintenance support form; optimally scheduling maintenance task will improve the efficiency for accompanying repair, and increase the combat efficiency for combat unit. In the paper, maintenance task scheduling heuristics in accompanying repair were researched with discrete event simulation methods. First, accompanying repair and maintenance task scheduling heuristics were presented. Next, simulation model of accompanying repair for equipment minimal combat unit was established. At last, maintenance task scheduling heuristics were evaluated, and influential factors, such as MTBF (mean time between fault), mission duration, preemption, priority updating methods, were analyzed through the simulation model. The simulation results show that it is better to prioritize the important maintenance task and allow preemption; when maintenance task emerging rate is high "MFCFS (modified first come first serve)" is better; when maintenance task emerging rate is low the "MSMPT (modified shortest mean process time)" is better; and it is reliable of "MEETOC (modified estimated earliest time to complete)".

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