面向动用计划的车辆装备备件预测研究

针对动用计划下的车辆装备备件的消耗特点,研究了车辆装备维修备件消耗量和库存控制两个预测优化问题。考虑动用计划期内车辆装备的预防性维修和修复性维修,实现定时定程维修和自然随机故障维修下装备维修备件的消耗量的预测。在此基础上,根据备件库存检查方式的特点,建立基于定期检查策略的联合补货库存控制模型,根据模型的结构特点确定决策变量界限,并利用多类种群位置更新方式改进了果蝇优化算法。仿真结果表明,改进的果蝇优化算法具有良好的求解效率,本文所提出的优化方法可为车辆维修保障资源优化提供决策依据。     

二维产品二维成组更换间隔期优化研究

对于故障受日历时间与使用时间共同影响的二维产品,传统的只按照单一时间变量开展的预防性维修已不能满足二维产品的实际维修保障需求,鉴于此,针对二维产品的特点,提出了二维预防性维修的策略,以日历时间与使用时间对二维产品进行二维预防性维修决策;首先建立了二维产品故障率函数表达式,然后对二维成组更换的过程进行具体的分析,在有限使用条件下分别从经济性的角度与任务性的角度建立二维成组更换的费用模型与可用度模型;根据所建模型,结合实例,采用了费效分析中的费效比准则来优化二维成组更换间隔期,通过对比分析验证了该模型的适用性与有效性,证明了二维成组更换策略要优于传统的成组更换策略,最后对二维预防性维修提出了展望。     

基于串件拼修的装备系统可用度分析及方案优化

首先研究了串件拼修下两级保障体制的备件维修供应流程,分析了非串件拼修、串件拼修以及不完全串件拼修三种策略下的系统可用度;在此基础上,建立以系统可用度为约束条件,以保障费用最小化为目标函数的库存优化模型,并用边际效应分析法对模型求解。通过对比不同策略方案下的结果表明,在满足最低可用度下,采取串件拼修策略,可以降低库存量,减少总费用;在库存量一定下,采取串件拼修策略可以有效提高装备可用度。     

可用度约束条件下的设备不完美预防维修策略

针对2M1B生产系统,基于设备实际维修情况,提出了故障设备不完美预防维修策略。首先,考虑设备随时间不断劣化的情况,基于准更新过程,建立了生产周期内设备随机故障次数的表达式,计算了设备维修总费用。其次,通过分析缓冲区内库存的变化轨迹,以生产周期内设备随机故障次数为基础,计算了缓冲区库存费用,综合设备维修费用和缓冲区库存费用,构建了周期内生产总成本模型。以满足系统最低要求的可用度水平为约束条件,以预防维修周期和缓冲区库存量为决策变量,以生产周期内单位时间总成本为目标函数,通过离散迭代算法获得最优预防维修周期和此周期下的最佳缓冲区库存量。最后,通过数值分析验证了模型的有效性,为制定最佳维修策略提供了有效依据。     

基于混合果蝇优化算法的选址-库存联合优化策略

针对装备维修保障仓库系统运营费用高、仓库点位布局不合理、备件库存结构不合理等问题,建立以多品种联合补货问题为基础的装备维修备件仓库选址-库存控制决策联合优化模型,模型可用于求解仓库的开设位置、维修活动需求点的指派情况、仓库补货时间以及库存水平等.根据模型的结构特点,利用多种群协同进化的方法改进传统果蝇优化算法的位置更新方式,设计一种内外两层搜索策略的混合果蝇优化算法,外层搜索策略作为算法的主程序用于搜索仓库选址决策变量,内层搜索策略采用改进的RAND算法用于搜索库存控制决策变量.仿真结果表明,混合果蝇优化算法具有良好的求解效率,能够确保库存系统在一定服务水平的基础上有效降低库存运营总成本.     

不完善检测情况视情维修费用优化及仿真研究

针对定期更换周期内辅以视情维修的维修策略,结合＂基于状态的维修＂理论,应用概率论和随机过程方法,在分析产品维修费用组成和结构的基础上,建立了其不完善检测情况下维修费用的数学模型,通过优化更换周期内的功能检测次数和检测间隔期,使产品单位时间内的期望维修费用达到最低。最后通过1个仿真实例,验证了该维修策略和数学模型的有效性。     

基于仿真优化的飞机维修备件优化问题研究

飞机维修备件优化问题研究目标是寻求备件费用与飞机的战备可靠性之间的平衡。针对通常的解析方法假设过多的问题，提出了仿真优化的方法加以解决，在两种备件优化模型的基础上，建立更符合备件优化实际情况的仿真模型，在仿真模型中引入优化策略，对备件优化问题进行求解。最后，以具体应用遗传算法为例，简要介绍了仿真优化的基本原理、仿真优化算法及仿真优化方法的应用过程。结果表明，仿真优化的方法解决备件优化问题是合理可行的。     

带有横向调度的维修备件选址库存路径问题研究

存储点的合理选址、库存的合理控制及运输配送的有效决策是支撑维修备件物流系统的重要内容。通过分析基于横向调度的维修备件物流系统,建立了基于横向调度的维修备件库存选址问题的数学模型,设计了隐枚举法和遗传算法相结合的启发式算法,通过仿真算例验证了方法的可行性和模型的先进性。通过分析得出,在考虑横向调度的情况下,物流系统内总体安全库存和最大虚拟库存总体水平会降低,同时实现对客户的服务承诺也会降低。     

基于Flexsim仿真技术的航空关键备件存储问题研究

通过在Flexsim中建立航空关键备件存储仿真模型,结合仿真实验的结果,根据设计的维修地点平均等待率指标和储备点可提供航空关键备件的概率指标,研究了某类航空关键备件的库存控制问题。仿真结果验证了模型的有效性。     

需求随机依赖库存环境下的订货仿真优化模型

需求的随机性和依赖库存性是库存问题的特点之一,在需求以泊松分布的形式随机依赖库存的条件下讨论了（Q,T）型库存控制问题。为了评估库存控制策略的平均盈利水平,建立了该库存问题的离散事件系统仿真模型,设计了一种基于仿真的种群重叠、遗传操作非重叠的进化算法,用以优化库存控制策略,类似设计了基于仿真的模拟退火和粒子群优化算法进行比较。通过实例分析了不同参数的变化对模型最优解的影响,灵敏度分析表明需求依赖库存效应越明显时,利润水平越高,最优订货策略越倾向于高库存、短周期和现货销售。仿真实例说明了基于仿真的优化算法的可行性、有效性。     

Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory

The interconnection of maintenance and spare part inventory management often puzzles managers and researchers. The deterioration of the inventory affects decision-making and increases losses. Block replacement and periodic review inventory policies were here used to evaluate a joint optimization problem for multi-unit systems in the presence of inventory deterioration. The deterministic deteriorating inventory (DDI) model was used to describe deteriorating inventory when deteriorating inventory data were available and the stochastic deteriorating inventory (SDI) model was used when they were not. Analytical joint optimization models were established, and the preventive replacement interval and the maximum inventory level served as decision variables to minimize the expected system total cost rate. This work proved the existence of the optimal maximum inventory level and gave the uniqueness condition under the DDI model. Numerical experiments based on the electric locomotives in Slovenian Railways were performed to confirm the effectiveness of the proposed models. Results showed the total cost rate to be sensitive to the maximum inventory level, which indicates that the research of this work is necessary. Further, the optimal preventive replacement interval was reduced and the optimal maximum inventory level was increased to balance the influence of deteriorating inventory. Monte Carlo experiments were used to show that the proposed policy is better than policies that do not take deteriorating inventory into account.     

Optimum block replacement policy over a random time horizon

In this paper we consider the block replacement policy (BRP) for a system operating over a random time horizon. Under such a policy, a system is replaced by a new one either at failure or at a given time interval. The optimality criterion is the expected total replacements cost. Conditions under which an optimal replacement period exits are given. It is shown that BRP over an infinite time horizon is obtained as a particular case of the present work. A numerical example is given to illustrate the proposed replacement model.     

A fixed interval ordering policy for joint optimization of age replacement and spare part provisioning

A new policy is presented for the joint optimization of age replacement and spare provisioning. The policy, referred to as a fixed interval ordering policy, is formulated by combining an age replacement policy with a periodic review ( t₀,q) type inventory policy, where t0 is the order interval and q is the order quantity. It is generally applicable to any operating system with either a single item or a number of identical items. A SLAM based simulation model has been developed to determine the optimal values of the decision variables by minimizing the total cost of replacement and inventory. The behaviour of the policy has been studied for a number of case problems specifically constructed by five-factor second-order rotatory design and the effects of different cost elements and item failure characterisics have been highlighted. The performance of the proposed policy has also been compared with that of the stocking policy which incorporates a continuous review ( s, S) type of inventory policy, where s is the stock reorder level and S is the maximum stock level. Simulation results clearly indicate that the optimal fixed interval ordering policy is less expensive than the optimal stocking policy when the system consists of a large number of operating units.     

Discrete-time spare ordering policy with randomized lead times and discounting

The paper considers a generalized discrete‐time order‐replacement model for a single unit system, which is subject to random failure when in operation. Two types of discrete randomized lead times are considered for a spare unit; one is for regular (preventive) order and another is for expedited (emergency) order. The model is formulated based on the discounted cost criterion. The underlying two‐dimensional optimization problem is reduced to a simple one‐dimensional one and then the optimal ordering policy for the spare unit is characterized under two extreme conditions: (i) unlimited inventory time and (ii) zero inventory time for the spare unit. A numerical example is used to determine the optimal spare‐ordering policy numerically and to examine the sensitivity of the model parameters.     

Number-dependent replacement model with random lead-time for systems under maintenance continuously

This article considers a number-dependent replacement policy where a system has two types of failures and is replaced at the nth type I failure (minor failure) or first type II failure (catastrophic failure), whichever occurs first where type I and type II failures are age dependent. Type I failures can be removed by restoration without any cost, since the maintenance work is executed continuously during the operation of the system. However, type II failure can be removed only through a replacement at a replacement cost. A spare unit for replacement can be delivered upon order and is available only when the random lead-time is finished. A model is developed for the average cost per unit time and is based on the stochastic behaviour of the assumed system and reflects the cost of storing a spare as well as system downtime. The optimal number for a minimum-cost policy is described and discussed. It is shown that the optimal number n* which minimises the cost rate is given by a unique solution of the equation under certain conditions.     

Degradation prediction and rolling predictive maintenance policy for multi-sensor systems based on two-dimensional self-attention

Traditional preventive maintenance policy gradually failed to guarantee the security and economy of current mechanical systems. This paper proposed a highly efficient rolling predictive maintenance (RPdM) policy for multi-sensor system, to make maintenance decisions. In this policy, to cope with the uncertainty of remaining useful life (RUL) prediction, the degradation process of the system is first divided into four intervals according to the inspection interval and spare parts lead time. Then, the two-dimensional self-attention (TDSA) method, which extract time dimensional and feature dimensional features by parallel computation, is developed to predict the probabilities of system RUL in the four intervals instead of the point of RUL. In addition, the output probabilities of the TDSA method are utilized to calculate the maintenance cost rate of the current inspection point and future point. The maintenance decision including spare parts ordering time and maintenance time is determined by comparing the maintenance cost rate of each inspection point, and the decision is updated at the next inspection point. To verify the effectiveness of the proposed RPdM policy, the C-MAPSS dataset provided by NASA is employed to implement degradation prediction and maintenance decision. Experiment results show that the cost rate of RPdM policy is lower than preventive maintenance policy, and only 27.7% higher than ideal maintenance policy which is impossible in real engineering. Moreover, the impact of different out-of-stock costs and corrective costs are explored and shows the good robustness of the RPdM policy.     

Preventive maintenance scheduling for repairable system with deterioration

Maintenance as an important part in manufacturing system can keep equipment in good condition. Many maintenance policies help to decrease the unexpected failures and reduce high operational cost such as conventional preventive maintenance. But these conventional preventive maintenance policies have the same time interval T that may easily neglect system’s reliability, because the system deteriorates with increased usage and age. Hence, this study has developed a reliability-centred sequential preventive maintenance model for monitored repairable deteriorating system. It is supposed that system’s reliability could be monitored continuously and perfectly, whenever it reaches the threshold R, the imperfect repair must be performed to restore the system. In this model, system’s failure rate function and operational cost are both considered by the effect of system’s corresponding condition, which helps to decide the optimal reliability threshold R and preventive maintenance cycle number. Finally, through case study, the simulation results show that the improved sequential preventive maintenance policy is more practical and efficient.     

Optimal age-replacement time with minimal repair based on cumulative repair cost limit and random lead time

In this article, we consider an age-replacement model with minimal repair based on a cumulative repair cost limit and random lead time for replacement delivery. A cumulative repair cost limit policy uses information about a system's entire repair cost history to decide whether the system is repaired or replaced; a random lead time models delay in delivery of a replacement once it is ordered. A general cost model is developed for the average cost per unit time based on the stochastic behaviour of the assumed system, reflecting the costs of both storing a spare and of system downtime. The minimum-cost policy time is derived, its existence and uniqueness is shown, and structural properties are presented. Various special cases are included. Because the framework and analysis are general, the proposed model extends several existing results. Finally, a numerical example is provided for illustration.     

A generalized age replacement policy with random delivery time and inspection

This paper is concerned with an age replacement policy that is more general because spare part random delivery time, age-dependent minimal repair and inspection are considered. Introducing the cost due to ordering, repairs, shortage, holding and inspection, we derive the expected cost per unit time in the long run as a criterion of optimality and seek the optimum age replacement policy by minimizing that cost. Various special cases are discussed and a numerical example is finally given to illustrate the method.     

Impact of spare parts remanufacturing on the operation and maintenance performance of offshore wind turbines: a multi-agent approach

Offshore wind farms are a growing source of energy, which aims to ensure a clean energy with a low environmental impact. In this context, this paper investigates opportunities of the turbine gearbox end of life-cycle to improve the operation and maintenance strategies. We determine the impact of spare part policy based on the remanufacturing of gearboxes recovered after each replacement. The remanufacturing implementation allows the extension of the gearbox life-cycle and involves a perfect organization and coordination between maintenance, monitoring, operation and spare part supply chain to determine the best way to use each gearbox of each wind turbine. In this paper, we present a multi-agent based approach to analyze the impact of the spare parts remanufacturing strategy on the performance of an offshore wind farm in term of total cost and carbon footprint.