FPA method for optimal production planning under availability/degradation machine and subcontracting constraint

This work treats an industrial problematic of a manufacturing system M₁ subject to random failure and that satisfies a random demand under required service level. To ensure the satisfaction of the demand, it calls up on a second manufacturing system M₂ that is a subcontractor and has a stochastic service level. This paper aims to determine an optimal production plan taking into account the degradation of the manufacturing system following its production rate and its availability. The unavailability of the machine M₁ is due to the non-negligible preventive and corrective maintenance actions duration. To consider a realistic case of the subcontractor, we consider that is not able to respond to the variation of the demand, and then it assumed that the manufacturer can order a minimum fixed quantity. We then derive an optimal production plan taking into account the degradation of the machine and simultaneously minimising: inventory, degradation and production cost. An algorithm based on finite perturbation analysis is proposed to determine the optimal production plan and the sensitivity study of the availability is presented.     

Maintenance and Condition-Based Maintenance

1IntroductionSystem reliability and availability have been widely studied in the literature because of their preva-lence in industry systems. Maintaining a high or required level of reliability and availability is oftenan essential requisite.Up to the 1950′s a fix it when it breaks′philosophy (BM, break maintenance) was mostly imple-mented due to lack of engineering knowledge in certain areas. The regular inspection regime of time-based maintenance (PM, preventive maintenance) was later in…     

Optimisation of distributed maintenance: Modelling and application to the multi-factory production

This paper concerns the modelling and the cost evaluation of maintenance activities in a distributed context. In this work we study the particular case where the maintenance activities are executed by two workshops: a central maintenance workshop (CMW) and a mobile maintenance workshop (MMW). The CMW concerns the repairing process for the corrective maintenance and the MMW executes all preventive maintenance in several factories according to a defined scheduling. The aim is to take into account the resources (spare parts in the MMW) and maintenance actions for a given operating budget. A modular approach for modelling a multi-site structure is proposed to achieve the aim of improving the availability of facilities on production sites while minimising the cost of maintenance.     

The optimisation of maintenance service levels to support the product service system

The dematerialising concept has formed a new management model: the product service system (PSS). PSS means that enterprises have changed the management concept of ‘product foremost and service second’ to ‘service-based product’ in design products, and the products are the tools produced and innovated for service. Therefore, the physical logistical service replaces the virtual service. However, during the transformation, the product service system also needs to emphasise cost, customers’ satisfaction and environmental protection. This study analysed different types of integrated maintenance service and used multi-attribute utility analysis to discuss the overall utility of maintenance service. The operation cost, CO ₂ emission and service satisfaction indicators of different maintenance service models were used as decision variables to determine the optimal maintenance system. Finally, a case study is described to demonstrate this research.     

Optimum Maintenance and Availability of Series Systems Subject to Imperfect Repair

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Joint optimisation of production rate and preventive maintenance in machining systems

This paper studies an integrated control strategy of production and maintenance for a machining system which produces a single type of product to meet the constant demand. Different from previous research, we assume in this study that during the production, the production rate not only influences the life of cutting tool, but also the reliability of the machine. Both the replacement of cutting tool and the preventive maintenance (PM) of machine are considered in this paper. The machine is preventively maintained at the Nth tool replacement or correctively repaired at the machine failure, whichever occurs first. PM and corrective repair may cause shortage which can be reduced by controlling inventory. There are two decision variables p and N, where p denotes the production rate and N denotes the number of cutting tool replacement before the PM is performed. An integrated model is developed to simultaneously determine the optimal production rate and PM policy that minimise the total expected cost per unit item produced. Finally, an illustrative example and sensitivity analysis are given to demonstrate the proposed model.     

Modeling Failure Process and Quantifying the Effects of Multiple Types of Preventive Maintenance for a Repairable System

In this paper, we consider a repairable system whose failures follow a non‐homogenous Poisson process with the power law intensity function. The system is subject to corrective and multiple types of preventive maintenance. A corrective maintenance has a minimal effect on the system; however, a preventive maintenance may reduce the system's age. We assume the effects of different preventive maintenance on the system are not identical and derive the likelihood function to estimate the parameters of the failure process as well as the effects of preventive maintenance. Moreover, we derive the conditional reliability and the expected number of failures between two consecutive preventive maintenance types. The proposed methods are applied to a real case study of four trucks used in a mining site in Canada. Copyright © 2016 John Wiley & Sons, Ltd.     

Effects of maintenance policy on an imperfect production system under trade credit

The traditional production model development assumed that all products are perfect quality and did not consider maintenance, which is far from reality. In practice, the production process may shift randomly from an in-control state to an out-of-control state during a production run, i.e. process deterioration. This paper considers both preventive maintenance and corrective maintenance which are used to increase the system reliability. The objective of this paper is to determine the optimal production run time and maintenance frequency while minimising the total cost under process deterioration and trade credit. This paper develops a theorem and an algorithm to solve the problem described, provides numerical analysis to illustrate the proposed solution procedure, and discusses the impact of various system parameters. A real case of hi-tech manufacturer is used to verify the model. It predicts a 10.36% decrease in total cost if the preventive maintenance decision is considered.     

Optimal inspection and preventive maintenance of units with revealed and unrevealed failures

The maintenance of a single unit system that alternates operating and idle periods is studied. In the former case the failures are detected as soon as they occur and only by special testing or inspection in the latter. This paper aims at minimizing the cost per unit of time for an infinite time span by selection of a unique interval, for both inspection and maintenance. A special feature of this model is the possibility of a less than perfect testing as the inspections may fail and give a wrong result. It is further assumed that both preventive and corrective maintenance make the unit as good as new with the durations of inspections and maintenances being negligible. The existence of an optimum interval and how it depends on both the cost parameters and the reliability characteristics of the unit is discussed.     

COMPROMISE: AN EFFECTIVE APPROACH FOR CONDITION-BASED MAINTENANCE MANAGEMENT OF GAS TURBINES

A decision-based approach to condition-based maintenance management of rotating machinery is introduced and illustrated by formulating and solving a multiple objective maintenance management problem for a 15 MW industrial gas turbine. The compromise Decision Support Problem approach is used because it provides a convenient way of incorporating both information from condition monitoring and considerations of factors such as machine degradation, operating cost (fuel cost), production loss, maintenance cost, environmental protection, machine availability, etc. The focus in this paper is on explaining the approach rather than on the results per se.     

A decision model for corrective maintenance management

Proper management of maintenance offers many companies significant potential for improving productivity and profitability. Traditional management thinking regards maintenance costs as accidental, rather than planned and controllable. Additionally, research in maintenance management has focused on preventive maintenance and has ignored corrective maintenance even though the latter is also considered to be a critical activity in industry. This study proposes a decision model that could assist in a comparative evaluation of alternative corrective maintenance policies. This decision model consists of a simulation model and economic analysis. The simulation model predicts inventory costs and delivery performance of a corrective maintenance policy in various production systems. Based on simulation results, an economic analysis, consisting of a net present value model and breakeven models, determines the economic value of alternative maintenance policies. A detailed example is offered to evaluate two particular correciive maintenance policies (machine redundancy and worker flexibility) although the decision model can be applied to other options. The results of the example demonstrate the decision model's capability to assist managers in selecting the best corrective maintenance policy.     

Human Factors Effects and Analysis in Maintenance: A Power Plant Case Study

This paper proposes a methodical approach for identifying and reducing human error in maintenance activities, the human factors effect and analysis. Human factors effect and analysis presents a roadmap for selecting significant human factors affecting maintenance management as well as the most effective solutions using cost–benefit analysis. Safety and operational consequences of each human factor are compared to preventive and recovery risk controls to select the preferred risk control method. Because human factor programs are not implemented in many maintenance departments, quantitative data are rare. Thus, expert judgment may help to compare potential solutions. In order to show the applicability of the proposed approach a power plant in Kenya is selected as a case study. Procedure usage, fatigue, knowledge and experience, and time pressure are identified as the most important human factors. Training, task planning /shift management, knowledge management, scheduling as well as incident report programs are the most cost‐effective solutions for performing human factors program. The proposed approach would improve system reliability by recognizing human related failures. Furthermore, unexpected incident and accident may be reduced having knowledge about potential risk factors. Copyright © 2016 John Wiley & Sons, Ltd.     

Determination of Pareto frontier in multi-objective maintenance optimization

The objective of a maintenance policy generally is the global maintenance cost minimization that involves not only the direct costs for both the maintenance actions and the spare parts, but also those ones due to the system stop for preventive maintenance and the downtime for failure. For some operating systems, the failure event can be dangerous so that they are asked to operate assuring a very high reliability level between two consecutive fixed stops. The present paper attempts to individuate the set of elements on which performing maintenance actions so that the system can assure the required reliability level until the next fixed stop for maintenance, minimizing both the global maintenance cost and the total maintenance time. In order to solve the previous constrained multi-objective optimization problem, an effective approach is proposed to obtain the best solutions (that is the Pareto optimal frontier) among which the decision maker will choose the more suitable one. As well known, describing the whole Pareto optimal frontier generally is a troublesome task. The paper proposes an algorithm able to rapidly overcome this problem and its effectiveness is shown by an application to a case study regarding a complex series-parallel system.     

Quality issue in forecasting problem of production and maintenance policy for production unit

In this study, we consider an unreliable deteriorating production system that produces conforming and non-conforming products to satisfy a random demand under a given service level and during a finite horizon. The production system is subjected to a failure-prone machine. The quality of the produced products is affected by the machine deterioration since the rate of defectives increases as the deterioration increases. Preventive maintenance actions can be piloted on the production system to reduce the influence of deterioration and the defective rate. A joint control policy is based on a stochastic production and maintenance planning problem with goals to determine, firstly, the economic plan of production and secondly, the optimal maintenance strategy. The proposed jointly optimisation minimises the total cost of production, inventory, maintenance and defectives. A failure rate and quality relationship are defined to show the influence of the production rates variation on the failures rate as well as on the defective rate. A numerical example and an industrial case study are adopted to illustrate the proposed approach and a sensitivity analysis to validate the jointly optimisation.     

Optimal maintenance plan for two-level assembly system and risk study of machine failure

This paper deals with the optimisation of two-levels assembly system planning. This system is composed of a single machine, inventories at levels 1 and 2 for stock keeping components to assembly and final assembled product. It assumed that the machine processes all assembly operations and is subject to random failure. A mathematical model is developed to incorporate a supply planning for two-level assembly systems under stochastic lead times and breakdowns machine. A preventive maintenance plan is carried out to reduce the frequency of the corrective maintenance actions. This work has double goals. The first one is to find the optimal order release dates for the different components at level 2 and the optimal preventive maintenance plan. The second one is to quantify the risk due to machine failures which have an impact on the lead-time of the finished product. To consider the maintenance actions, preventive maintenance actions are perfectly performed to restore the machine to state “as good as new”, minimal repair is considered at failure. The model minimises the total cost, which is the sum of inventory holding cost for components at levels 1 and 2, backlogging and inventory holding cost for the finished products and maintenance costs. To illustrate the effectiveness of the proposed model, different meta-heuristics are applied; the genetic algorithm shows the most suited to our analytical model, the optimal release date founded by this algorithm allows finding the optimal preventive maintenance plan. The obtained optimal maintenance planning is used in the risk assessment in order to find the threshold repair period that avoids lost profit.     

Spreadsheet modeling of optimal maintenance schedule for components in wear-out phase

This paper addresses a method for determining the optimum maintenance schedule for components in the wear-out phase. The interval between maintenance for the components is optimized by minimizing the total cost. This consists of maintenance cost, operational cost, downtime cost and penalty cost. A decision to replace a component must also be taken when a component cannot attain the minimum reliability and availability index requirement. Premium solver platform, a spreadsheet-modeling tool, is utilized to model the optimization problem. Constraints, which are the considerations to be fulfilled, become the director of this process. A minimum and a maximum value are set on each constraint so as to give the working area of the optimization process. The optimization process investigates n-equally spaced maintenance at an interval of Tr. The increase in operational and maintenance costs due to the deterioration of the components is taken into account. This paper also performs a case study and sensitivity analysis on a liquid ring primer of a ship's bilge system.     

A study of availability-centered preventive maintenance for multi-component systems

This paper studies preventive maintenance (PM) in simultaneously considering three actions, mechanical service, repair and replacement for a multi-components system based on availability. Mechanical service denotes the activities including lubricating, cleaning, checking and adjusting, etc. which is set to alleviate strength degradation. Repair is defined on that not only slow down the degraded velocity but also restore the degraded strength partly. Replacement is settled to recover a component to its original condition. According to the definitions, the degradation of components is analyzed from its failure mechanisms and the improvements of various actions to it in reliability were measured by using two improved factors. Following the proposed model of reliability, the mean-up and mean-down times of each component are also investigated and the replacement intervals of components are determined based on availability maximization. Here, the minimum one among the intervals is chosen as the PM interval of system for programming the periodical PM policy. The selection of action for the components on every PM stage is decided by maximizing system benefit in maintenance. Repeatedly, the scheduling is progressed step by step and is terminated until the system extended life reaching to its expected life. The complete schedule provides the information, the actions adopted for the components, the availability and the total cost of system on each stage. Validly, a multi-components system is used as an example to describe the proposed algorithm.     

基于设备预诊断的单机预知性维护模型研究

随着企业对设备维护管理的逐步重视,不少学者研究了各类维护模型以满足企业需求.然而,这些模型大多未考虑设备自身衰退特性,易忽略其可靠性能.因此,提出一类基于设备预诊断信息的预知性维护模型,在最小化长期平均总成本的目标下,为企业提供最佳的设备维护阈值和维护周期次数.研究结果表明:该设备预知性维护模型是合理有效的,较好地描述...     

一种基于云平台的包装印刷设备增值服务系统

目的 研究基于Internet的云端通讯、 云数据分析和服务的机联网应用系统, 实现远程维护和工艺指导等设备增值服务。方法 基于凹印和柔印设备的机联网工程实践, 通过Windows Azure云平台,构建包括数据平台和网关、 印刷企业自有云和制造商服务云的三层云框架, 形成以数据网关WCF数据服务组件、 印刷企业云ServiceBus通讯组件为核心的设备运行数据的流程与接口, 并借助Azure的OLAP和机器学习平台, 结合设备运行和工艺管理数据, 形成印刷设备的智能云服务REST API平台。结果 提高了大型包装印刷设备的信息化服务水平, 创新了其云端化管理的应用模式, 提供了实用的系统应用案例。结论 该系统将包装印刷设备与云计算结合起来, 为设备制造商和印刷企业的设备管理及生产实践提供一种新的方案。     

A cost‐based selective maintenance decision‐making method for machining line

Machine line is a type of manufacturing system in which machines are connected in series or in parallel. It is significant to ensure the reliability as well as to reduce the total cost of maintenance and failure losses in the maintenance programs of such systems. Cost‐based selective maintenance decision‐making, which is the best method for a selected group of machines in machine line is presented under limited maintenance durations. Fault losses and maintenance costs of a single machine under different maintenance actions i.e. minimal repair, preventive maintenance and overhaul on the fault rate of the machine are calculated. An algorithm combining the heuristic rules and tabu search is proposed to solve the presented selective maintenance model. Finally, a case study on the maintenance decision‐making problem of a connecting rod machining line in the automobile engine workshop is presented to illustrate the applicability of the proposed method. The end result shows that the fault losses can be further reduced by the optimization of maintenance interval and maintenance duration. Copyright © 2010 John Wiley & Sons, Ltd.