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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (5): 852-861    DOI: 10.3785/j.issn.1008-973X.2019.05.005
    
Maintenance, repair and overhaul/operations service resource scheduling optimization for complex products in uncertain environment
Xin-yu YANG1,2(),Ye-fa HU1,*()
1. School of Mechanical and Electric Engineering, Wuhan University of Technology, Wuhan 430070, China
2. School of Mechanical and Electric Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
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Abstract  

A stochastic chance-constrained programming mathematical model under uncertain resource scheduling time and uncertain service execution time was built from a realistic perspective, based on the requirement analysis of the collaborative maintenance, repair and overhaul/operations (MRO) service resource scheduling of complex products. A hybrid intelligent algorithm composed of the stochastic simulation, the neural network and the discrete particle swarm optimization algorithm was proposed to solve the proposed optimization problem. The training sample set produced by stochastic simulation was used to train the neural network for the approximation of the optimization objective function. The trained neural network model was used to replace the optimization objective function to perform the optimization iterations of particle swarm algorithm. This hybrid intelligent algorithm can effectively improve the solving rate of bi-objective problem of collaborative MRO service resource scheduling of complex products under uncertain time variables. The results of case study showed that the proposed stochastic chance-constrained programming model and the hybrid intelligent algorithm were more suitable for solving the MRO service resource scheduling problem under uncertainty in the reality, compared with the optimization algorithm under certainty. The proposed scheduling scheme was more robust in practical implementation.

Key words： maintenance, repair and overhaul/operations (MRO) service      parameter uncertainty      stochastic simulation      neural network      discrete particle swarm optimization algorithm     
Received: 15 April 2018      Published: 17 May 2019
CLC:  TP 391  
Corresponding Authors: Ye-fa HU     E-mail: yangxy@zzuli.edu.cn;huyefa@whut.edu.cn
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Cite this article:

Xin-yu YANG,Ye-fa HU. Maintenance, repair and overhaul/operations service resource scheduling optimization for complex products in uncertain environment. Journal of ZheJiang University (Engineering Science), 2019, 53(5): 852-861.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.05.005     OR     http://www.zjujournals.com/eng/Y2019/V53/I5/852


不确定环境下复杂产品维护、维修和大修服务资源调度优化

基于对复杂产品维护、维修和大修（MRO）协同服务资源调度的需求分析，从现实角度出发，建立资源调度时间和服务执行时间参数不确定条件下的随机机会约束规划数学模型. 提出由随机模拟、神经网络和离散粒子群优化算法组合成的混合智能算法，求解所提出的优化问题. 随机模拟方法为所建立的神经网络模型提供训练样本集，得到的训练样本集被用于训练神经网络模型以逼近优化目标函数，训练后的神经网络模型被用于代替优化目标函数来执行粒子群算法优化迭代. 该混合算法能有效提升时间参数不确定条件下的复杂产品MRO协同服务资源调度双目标优化问题的求解速度. 案例分析表明，相比于确定性条件下的优化算法，所提出的随机机会约束规划模型和混合算法更适用于求解现实中不确定条件下的MRO服务资源调度问题，所求得的调度方案在实际执行中具有更好的鲁棒性.


关键词： 维护、维修和大修（MRO）服务,  参数不确定性,  随机模拟,  神经网络,  离散粒子群优化算法 
Fig.1 Multi-objective optimization process analysis
Fig.2 Extraction combination of service resource scheduling scheme
Fig.3 Subtask division and logic execution sequence
Fig.4 Flow chart of proposed hybrid algorithm
Fig.5 Flow chart of standard PSO algorithm
Fig.6 Numerical conversion method of particle position
参数 取值
T3×7/h $\left[ {\begin{array}{*{20}{c}} {N(45, 64)}&{N(20, 64)}&{N(30, 16)}&{N(50, 64)}&{N(12, 16)}&{N(10, 25)}&{N(24, 25)} \\ {N(30, 100)}&{N(10, 25)}&{N(12, 16)}&{N(25, 25)}&{N(24, 64)}&{N(15, 64)}&{N(40, 25)} \\ {N(25, 64)}&{N(40, 49)}&{N(20, 25)}&{N(30, 64)}&{N(40, 25)}&{N(30, 25)}&{N(25, 64)} \end{array}} \right]$
H3×7 $\left[ {\begin{array}{*{20}{c}} {0.8}&{0.8}&{0.7}&{0.7}&{0.6}&{0.6}&{0.8} \\ {0.7}&{0.6}&{0.5}&{0.8}&{0.9}&{0.8}&{0.8} \\ {0.6}&{0.8}&{0.6}&{0.8}&{0.8}&{0.8}&{0.7} \end{array}} \right]$
Cg3×7/元 $\left[ {\begin{array}{*{20}{c}} {425}&{385}&{645}&{880}&{1200}&{130}&{250} \\ {350}&{350}&{570}&{980}&{1600}&{220}&{150} \\ {250}&{410}&{780}&{870}&{1300}&{330}&{240} \end{array}} \right]$
Ch3×7/（元/h） $\left[ {\begin{array}{*{20}{c}} {30}&{20}&{55}&{60}&{60}&{15}&{10} \\ {25}&{25}&{60}&{55}&{50}&{10}&{15} \\ {20}&{35}&{50}&{50}&{55}&{10}&{15} \end{array}} \right]$
w1×7 $[ {\begin{array}{*{20}{c}} 4,&\!\!3,&\!\!5,&\!\!6,&\!\!8,&\!\!3,&\!\!4 \end{array}}]$
Tr1×5/h $[ {\begin{array}{*{20}{c}} {N(12, 16)},&{N(30, 64)},&{N(12, 25)},&{N(20, 64)},&{N(12, 64)} \end{array}}]$
O5×7 $\left[ {\begin{array}{*{20}{c}} 0&1&0&0&0&1&0 \\ 1&0&1&0&0&0&1 \\ 1&0&0&0&1&0&1 \\ 0&0&0&1&0&0&1 \\ 0&1&0&0&0&1&1 \end{array}} \right]$
Q5×5 $\left[ {\begin{array}{*{20}{c}} 0&1&1&0&0 \\ 0&0&0&0&1 \\ 0&0&0&1&0 \\ 0&0&0&0&1 \\ 0&0&0&0&0 \end{array}} \right]$


Tab.1 Key parameters of service task scheduling optimization
Fig.7 BP network training result of MRO service scheduling optimization
Fig.8 Comparison of Pareto optimal solutions under different conditions
序号 资源组合方案编码 确定条件下适应度 不确定条件下适应度
1 [1，1，1，3，2，2，3] [?17，44.238 6] [?26.299 7，22.476 3]
2 [1，1，1，3，2，2，1] [?16，41.161 1] [?24.081 6，31.819 9]
3 [1，1，1，3，2，3，1] [?12，35.159 9] [?19.129 4，29.524 7]
Tab.2 Optimized resource combination scheme and fitness value under certain conditions
序号 资源组合方案编码 不确定条件下适应度
1 [1，3，1，3，2，2，1] [?18.775 9，29.160 2]
2 [1，3，1，2，2，2，1] [?18.525 4，28.799 0]
3 [1，1，2，2，2，2，1] [?23.778 4，30.360 0]
4 [1，3，2，2，2，2，1] [?18.497 5，27.935 0]
5 [1，1，1，3，2，2，1] [?24.081 6，31.819 9]
6 [1，3，1，2，2，3，1] [?18.202 6，27.3827 0]
Tab.3 Optimized resource combination scheme and fitness value under uncertain conditions （partial examples）
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