Sensor-driven prognostic models for equipment replacement and spare parts inventory

Accurate predictions of equipment failure times are necessary to improve replacement and spare parts inventory decisions. Most of the existing decision models focus on using population-specific reliability characteristics, such as failure time distributions, to develop decision-making strategies. Since these distributions are unaffected by the underlying physical degradation processes, they do not distinguish between the different degradation characteristics of individual components of the population. This results in less accurate failure predictability and hence less accurate replacement and inventory decisions. In this paper, we develop a sensor-driven decision model for component replacement and spare parts inventory. We integrate a degradation modeling framework for computing remaining life distributions using condition-based in situ sensor data with existing replacement and inventory decision models. This enables the dynamic updating of replacement and inventory decisions based on the physical condition of the equipment.     

考虑制造商设备维修的制造—零售供应链决策与协调

设备可靠性是影响供应不确定的重要因素。为了研究企业内部的维修策略对整条供应链的运营策略产生的具体影响,在考虑设备维修的情况下,针对单个制造商和单个零售商构成的二级供应链,运用Stackelberg博弈的方法研究制造商的最优维修策略和定价策略以及零售商的最优订货量。研究结果表明,制造商的预防性维修次数存在上限且最大维修次数与设备可靠性无关;预防性维修策略有助于提高制造商的产量,降低产品的批发价格,但制造商的最优产出量不会达到企业的最大产能;收益共享契约能够实现供应链的协调;制造商在任何情形下都具有提高设备可靠性的动机,但零售商只会在市场规模较大时才存在为制造商提供设备改造资金的动机。     

带缓冲区的基于时间延迟的可修设备联合优化

考虑到设备停机时间较长对企业经济效益的影响,在设备间设置缓冲区;同时考虑到设备单目标维修决策模型的不足,以及故障次数表达的精确程度对决策的影响,借用了时间延迟理论,建立了可修复设备的联合决策模型。为了满足设备的生产要求,以及最优化缓冲区生产系统的费用水平,将分别以平均单位时间的总停机时间、平均单位时间的总费用作为目标函数,来进行研究。通过时间延迟理论,分析了故障形成的过程,并表达出故障次数。借用更新报酬定理,来表达两目标函数。由离散迭代算法,求解得到：最优的检查周期和额定库存分别为25 d、1 195件时,总费用率在停机时间率为0.199时最小为22 739.95元。另外,进行了敏感性分析来验证最优解,最后由求解结果来指导生产线维修管理。     

Roles of inventory and reserve capacity in mitigating supply chain disruption risk

This research focuses on managing disruption risk in supply chains using inventory and reserve capacity under stochastic demand. While inventory can be considered as a speculative risk mitigation lever, reserve capacity can be used in a reactive fashion when a disruption occurs. We determine optimal inventory levels and reserve capacity production rates for a firm that is exposed to supply chain disruption risk. We fully characterise four main risk mitigation strategies: inventory strategy, reserve capacity strategy, mixed strategy and passive acceptance. We illustrate how the optimal risk mitigation strategy depends on product characteristics (functional versus innovative) and supply chain characteristics (agile versus efficient). This work is inspired from a risk management problem of a leading pharmaceutical company.     

The Graphical Analysis for Maintenance Management Method: A Quantitative Graphical Analysis to Support Maintenance Management Decision Making

This article proposes a logical support tool for maintenance management decision making. This tool is called the Graphical Analysis for Maintenance Management (GAMM), a method to visualize and analyze equipment dependability data in a graphical form. The method helps for a quick and clear analysis and interpretation of equipment maintenance (corrective and preventive) and operational stoppages. Then, opportunities can be identified to improve both operations and maintenance management (short–medium term) and potential investments (medium–long term). The method allows an easy visualization of parameters, such as the number of corrective actions between preventive maintenance, the accumulation of failures in short periods of time, and the duration of maintenance activities and sequence of stops of short duration. In addition, this tool allows identifying, a priori, anomalous behavior of equipment, whether derived from its own function, maintenance activities, misuse, or even equipment designs errors. In this method, we used a nonparametric estimator of the reliability function as a basis for the analysis. This estimator takes into account equipment historical data (total or partial) and can provide valuable insights to the analyst even with few available data. Copyright © 2012 John Wiley & Sons, Ltd.     

Investing in Quality Under Autonomous and Induced Learning

The reduction of variability in product performance characteristics is an important focus of quality improvement programs. Learning is intrinsically linked to process improvement and can assume two forms: (i) autonomous learning; and (ii) induced learning. The former is experientially-based, while the latter is a result of deliberate managerial action. Our involvement in quality and capacity planning with several major corporations in different industries suggested that it would be instructive to devise a model that would prescribe an optimal combination of autonomous and induced learning over time to maximize process improvement. We thus propose such a model to investigate the optimal quality improvement path for a company given that quality costs depend on both autonomous and induced types of learning experienced on a number of quality characteristics. Several properties of an optimal investment path are developed for this problem. For example, it is shown that decisions maximizing short-term gains may actually lead to suboptimal resource utilization decisions when total costs associated with a longer planning horizon are taken into account. Numerical examples are used to assess the sensitivity of the optimal investment plan with respect to changes in several model parameters.     

需求受相关性影响的设备备件库存优化

备件多级库存模型通常基于备件需求相互独立的假设，但随着库存系统层次的增加以及协同管理方式的应用，备件需求的相关性将显著影响库存优化决策。针对需求具有相关性的备件库存问题，以服务响应时间为约束条件，以库存成本及缺货成本最小化为目标建立备件两级库存决策模型。引入Nataf概率变换法，利用已知的备件需求边缘概率密度函数构造满足特定相关性条件的随机需求样本，并将蒙特卡洛仿真与遗传算法相结合求解最优库存分配方案。仿真算例证明，设备备件库存的最优决策随着需求相关性系数的增大而发生变化，根据需求相关性的变化适当地调整库存决策，有利于降低备件库存系统总成本，提高库存系统对顾客需求的响应能力。     

Capacitated dynamic lot sizing with capacity acquisition

One of the fundamental problems in operations management is determining the optimal investment in capacity. Capacity investment consumes resources and the decision, once made, is often irreversible. Moreover, the available capacity level affects the action space for production and inventory planning decisions directly. In this article, we address the joint capacitated lot-sizing and capacity-acquisition problems. The firm can produce goods in each of the finite periods into which the production season is partitioned. Fixed as well as variable production costs are incurred for each production batch, along with inventory carrying costs. The production per period is limited by a capacity restriction. The underlying capacity must be purchased up front for the upcoming season and remains constant over the entire season. We assume that the capacity acquisition cost is smooth and convex. For this situation, we develop a model which combines the complexity of time-varying demand and cost functions and of scale economies arising from dynamic lot-sizing costs with the purchase cost of capacity. We propose a heuristic algorithm that runs in polynomial time to determine a good capacity level and corresponding lot-sizing plan simultaneously. Numerical experiments show that our method is a good trade-off between solution quality and running time.     

Integrated workforce capacity and inventory management under labour supply uncertainty

In a manufacturing environment with volatile demand, inventory management can be coupled with dynamic capacity adjustments for handling the fluctuations more effectively. In this study, we consider the problem of integrated capacity and inventory management under non-stationary stochastic demand and capacity uncertainty. The capacity planning problem is investigated from the workforce planning perspective where the capacity can be temporarily increased by utilising contingent workers from an external labour supply agency. The contingent capacity received from the agency is subject to an uncertainty, but the supply of a certain number of workers can be guaranteed through contracts. There may also be uncertainty in the availability of the permanent and contracted workers due to factors such as absenteeism and fatigue. We formulate a dynamic programming model to make the optimal capacity decisions at a tactical level (permanent workforce size and contracted number of workers) as well as the operational level (number of workers to be requested from the external labour supply agency in each period), integrated with the optimal operational decision of how much to produce in each period. We analyse the characteristics of the optimal policies and we conduct an extensive numerical analysis that helps us provide several managerial insights.     

On reliability criteria and the implied cost of failure for a maintained component

This paper considers age-based replacement and block replacement when reliability is also a decision criterion. We describe how specification of the operational reliability of component function determines the replacement policy, and that setting a value for the cost of failure and specifying an operational reliability requirement are equivalent. This duality then implies a simple method for checking the consistency of the cost of failure and operational reliability measures when they are set system operators and maintainers. A simple expression for the median time between operational failures for a socket subject to age-based replacement is also obtained. These ideas are considered briefly in the context of block replacement. Data from an actual case relating to the maintenance and replacement of train traction motors is used for illustration.     

面向维修的机械系统可靠度预测与仿真研究

机电产品在服役期间因零件失效而产生故障,重组维修破坏了原有的系统可靠性模型,因而需要对设备可靠性问题重新进行研究和评价。基于机电系统中零件的失效时间分布密度函数,研究了在重组维护过程中机电系统服役期间零件年龄结构的分布规律,发展了机电系统可靠性数学模型。通过仿真研究,探讨了系统服役期间年龄结构、可靠度和失效率的发展规律,定量地研究了失效时间分布密度函数的参数对系统可靠度的影响。这对于评估机械系统的可靠性和全生命周期的失效率,制定合理的维修策略具有重要意义。     

Optimal production operations sequencing

The goal of this research is to address the following issues: Under what circumstances should a company perform operations resequencing? Which design leads to the most desirable inventory cost and customer service trade-off? For make-to-stock production–inventory systems with no WIP safety stocks, we establish the optimal production operations sequencing and determine easy-to-use criteria for resequencing when technologically possible. We show that in many cases, resequencing decisions can be made using a graphical decision tool, namely cost–time profiling. For more complex systems with work-in-progress safety stock inventories, we analyze how system attributes drive overall performance. Some specific resequencing opportunities are examined in terms of their effects on cost and on the levels of optimum safety stocks.     

不确定环境下复杂机械装配系统设备维修方法

为保障不确定环境下复杂机械装配系统的连续性并降低其维修成本,提出一种以马尔可夫决策理论为基础的设备维修策略动态选择方法。在综合考虑系统运行成本、缓冲库存成本、设备维修成本及停机损失成本的基础上,构建了装配系统可靠性成本模型。该模型以带有中间缓冲区的二级装配系统为研究对象,以设备状态和缓冲库存量为自变量,以可靠性成本为目标函数。分析了装配系统的不同运行状态,利用模拟退火算法和模糊非线性混合整数目标规划对可靠性成本模型求解,制定装配系统最优维修方法。该方法降低了装配系统停机时间,减少了设备维修次数,可为生产线设计和维修计划的制定提供依据。最后,通过算例分析验证了模型的有效性和可行性。     

Designing data warehouses for equipment management system with genetic algorithms

Effective equipment management is becoming one of the key factors in keeping a competitive advantage in the dynamic business environment since equipment is an important asset for manufacturing companies. Nowadays, maintenance administration has become one of the most important tasks in equipment management, particularly in manufacturing industries. Equipment management system (EMS) aims at reducing maintenance cost and production loss caused by machine breakdown. In addition, EMS can assist equipment engineers to make the right maintenance decisions at the right time, and at the right shop floor. Traditional computerized maintenance management systems (CMMS) have helped equipment engineers to deal with maintenance operations, but they lack decision support capability. In this paper, we design a data warehouse (DW) for EMS to help equipment engineers make maintenance decisions with various equipment related dimensions to improve effectiveness. A set of cubes can be built from EMS DW for the purpose of decision-making. In order to achieve a reasonable query response time under the memory space limit, a mechanism of partial materialization based on genetic algorithms (GAs) is adopted to design data cubes in the EMS DW. From the computational results the proposed GA-based approach for cube design can be applied to effectively select the appropriate multi-dimensional views for equipment management.     

A Modeling Approach to Maintenance Decisions Using Statistical Quality Control and Optimization

Maintenance concerns impact systems in every industry and effective maintenance policies are important tools. We present a methodology for maintenance decision making for deteriorating systems under conditions of uncertainty that integrates statistical quality control (SQC) and partially observable Markov decision processes (POMDPs). We use simulation to develop realistic maintenance policies for real‐world environments. Specifically, we use SQC techniques to sample and represent real‐world systems. These techniques help define the observation distributions and structure for a POMDP. We propose a simulation methodology for integrating SQC and POMDPs in order to develop and valuate optimal maintenance policies as a function of process characteristics, system operating and maintenance costs. A two‐state machine replacement problem is used as an example of how the method can be applied. A simulation program developed using Visual Basic for Excel yields results on the optimal probability threshold and on the accuracy of the decisions as a function of the initial belief about the condition of the machine. This work lays a foundation for future research that will help bring maintenance decision models into practice. Copyright © 2005 John Wiley & Sons, Ltd.     

Integrating reliability optimization into chemical process synthesis

The growing need to achieve high availability for large integrated chemical process systems demands higher levels of system reliability at the operational stage. In these circumstances, it has become critical to consider the reliability aspects of a system and its components at the design stage. Traditional reliability/availability analysis methods and maintenance optimization frameworks, commonly applied at the design stage, are limited in their application, as in most of these methods the designer is required to specify the process system components, their connectivity and their reliabilities a priori. As a result, these traditional methods do not provide the flexibility to reconfigure a process or select initial reliabilities of equipment in a way that maximizes the inherent plant availability at the design stage. In this paper, we developed an optimization framework by combining the reliability optimization and process synthesis challenges and the combined optimization problem is posed as a mixed integer non-linear programming optimization problem. The proposed optimization framework features an expected profit objective function, which takes into account the trade-off between initial capital investment and the annual operational costs by supporting appropriate estimation of revenues, investment cost, raw material and utilities cost, and maintenance cost as a function of the system and its component availability. The effectiveness and usefulness of the proposed optimization framework is demonstrated for the synthesis of the hydrodealkylation process (HDA) process.     

Investigating reliability improvement of second-hand production equipment considering warranty and preventive maintenance strategies

A cost model for optimal reliability improvement of warranted second-hand production equipment is developed. The second-hand production equipment of age x is subjected to an upgrade action of a certain level u before it is sold with a Free Repair Warranty. We look at determining the optimal upgrade level when not performing and when performing periodic preventive maintenance (PM) during the warranty period. Two different PM strategies are considered: (a) periodic PM actions having the same efficiency level; (b) periodic multi-phase PM actions with a maintenance efficiency level which varies according to the phase. The proposed model aims at helping the dealer to find the optimal upgrade level to perform before selling the second-hand equipment, and to assess whether performing PM actions during the warranty period, according to a specific maintenance strategy, is worthwhile in terms of cost reduction. Numerical experimentations considering each PM scenario are performed in order to investigate how each PM strategy impacts the improvement level to be performed and the associated total expected cost. The obtained results showed that the expected total cost incurred by the dealer is governed by a sensitive trade-off between the warranty servicing cost and the costs associated with the reliability improvement, and with the PM performed during the warranty period. It is also found that the proposed new periodic multi-phase PM policy with an increasing maintenance efficiency level yields lower upgrade levels, inducing lower costs for the dealer.     

考虑缓冲区库存分配的并联系统预防维护策略研究

为提高并联系统生产过程的连续性,提出了考虑缓冲区库存分配的并联系统(3M1B)预防维护模型。首先,考虑并联系统每台设备的故障率与维护率,在并联系统中上下游设备之间建立缓冲区,构建3M1B系统。其次,考虑设备不完美生产的可能性,以缓冲区库存量与设备运行周期为决策变量,以设备最小生产成本率为目标函数建立预防维护模型,求解最佳的预防维护策略与缓冲区库存分配策略。针对此维护模型,采用离散迭代算法与遗传算法进行求解,并比较了2种算法的优劣,结果表明,对于此模型,离散迭代算法优于遗传算法。最后,通过算例验证模型的可行性和有效性。     

Planning Procedures for a Process Industry

The purpose of this article is to illustrate the application of planning procedures for a process industry. The procedures deal with the determination of (i) optimal production rates and workforce requirements based on future sales forecasts, (ii) optimal capacity of a plant based on the above optimal production rates, (iii) optimal times for expanding capacities, and (iv) total required investment outlay. The optimal production rates and workforce requirements are established by a modified version of Holt's et al. linear decision rule. The methodology is illustrated by using cost, production, inventory and investment data supplied by a domestic paint industry     

Optimal condition based maintenance with imperfect information and the proportional hazards model

Condition based maintenance (CBM) is based on collecting observations over time, in order to assess equipment's state, to prevent its failure and to determine the optimal maintenance strategies. In this paper, we derive an optimal CBM replacement policy when the state of equipment is unknown but can be estimated based on observed condition. We use a proportional hazards model (PHM) to represent the system's degradation. Since equipment's state is unknown, the optimization of the optimal maintenance policy is formulated as a partially observed Markov decision process (POMDP), and the problem is solved using dynamic programming. Practical advantages of combining the PHM with the POMDP are shown.