Plant-level maintenance decision support system for throughput improvement

In the current global scenario of extreme competition, factors such as productivity, availability, quality and cost of operations play a vital role in the success of a company. A critical component relating to all of the above is maintenance. The conventional maintenance decision support systems have primarily focused on maximising the gains of a single machine system. However, a real life application usually consists of multiple machines, and the operational level decisions are more complex. In this paper, an on line plant-level maintenance decision support system (PMDSS) is developed by combining the short term and long term decision making process to improve the overall system performance while continuously attempting to maximise immediate profits in the short term. The PMDSS works towards two basic aims: (1) unplanned downtime reduction by predicting the remaining useful life of the machines, and (2) efficient utilisation of the finite maintenance and production resources through identifying the throughput-critical machines. The benefits of this approach are presented by considering an industrial case study of an automotive assembly line. The results obtained using this PMDSS approach shows a big throughput improvement as compared to the conventional maintenance policies.     

Simulation platform for anticipative plant-level maintenance decision support system

Global competition and increasing customer expectations are forcing automobile manufacturers to improve their operations. Maintenance, being one of the most critical components in many industries, has a direct impact on the improvement of the overall production performance. In this paper, we introduce an anticipative plant-level maintenance decision support system (APMDSS) that provides guidance on corrective and preventive maintenance priorities based on the equipment bottleneck ranks with the objective of improving daily plant throughput. APMDSS anticipates the plant dynamics (i.e. bottlenecks, hourly buffer levels and likelihood of machine breakdowns) for upcoming shifts using starting state information of the production shift (e.g. equipment maintenance history, operational status of machines, buffer levels and scheduled production model mix). We also evaluate the performance of APMDSS using real data from an automotive body shop experiencing routine throughput difficulties due to frequent machine breakdowns. The results are compared with other methods from the literature and found to be superior in many settings.     

Probabilitically based decision support

Probabilistic Safety Assessment, usually referred to by the acronym PSA, has by now become a recognized tool for safety analysis of nuclear power plants. In recent years, an increasing number of plants have been analysed, and as the technique has matured, the area of application of PSA based analyses has been expanded. Thus, probabilistic methods are now used increasingly in the day-to-day work concerning the safety, maintenance and operation of plants. In this context, the question of interpretation and application of analysis results in various decision situations has become crucial. This paper gives some comments concerning the basis for decision making involving probabilistic analyses.     

An optimization based decision support system for integrated planning and scheduling on dedicated freight corridors

Empty wagon redistribution, train formation, routing and scheduling are complex problems for large railways, many of which currently have or are planning dedicated freight railway corridors (DFC). DFC operations due to their unique characteristics require research and new models for better operations planning. The rolling-stock, being expensive assets, need to be utilised in an optimal manner while meeting service quality levels. Motivated by Indian DFC, we present an integer programming formulation of the dynamic problem of empty distribution and train scheduling in DFC and discuss associated modelling issues. By unifying the separate problems into a single and dynamic model, we have developed a framework for more effective rolling stock utilisation. Based on this optimization model, an interactive decision support system is proposed for better decision-making on rolling-stock allocation and train scheduling. Extensive experiments and systematic analyses for a case of Indian DFC highlight the potentialities and effectiveness of the proposed DSS for DFC operations planning and management.     

Interactive decision support in radiation therapy treatment planning

This paper proposes the use of an interactive decision support system to guide the treatment planning process for external beam radiation therapy. Based on multicriteria optimisation our research treatment planning software CARINA calculates efficient (also called Pareto optimal) treatment plans. These are stored in a database and accessed for evaluation by the treatment planner. The interactive component consists of navigation among the pre-calculated plans using free search, fine search and exact search as well as sensitivity analysis, which extracts dose dependence information for all structures from the plan database. As a result, plan quality is improved by finding advantageous trade-offs in competing treatment plans, trial-and-error is avoided, and effectiveness of treatment planning is increased.     

A holistic decision support tool for remanufacturing: end-of-life (EOL) strategy planning

Remanufacturing is a key enabler for sustainable production due to its effectiveness in closing the loop on material flows, extending product life cycle and reducing production waste and emission. In this paper, a holistic decision support tool to facilitate the product end-of-life (EOL) strategy planning, specifically using remanufacturing as a key strategy is presented. The proposed model incorporates checklist methods to evaluate the viability of conducting remanufacturing for a product and its components. An optimization model for determining the Pareto set of optimal EOL strategies that correspond to maximum economic profit and minimum environmental impact is presented. Since determination of this Pareto set via enumeration of all EOL strategies is prohibitively time-consuming, even for a product with a small number of components, genetic algorithm (GA), specifically NSGA-II has been utilized to achieve rapid calculation of the set of optimum EOL strategies. This NSGA-II method permits extensive sensitivity analysis to understand thoroughly the impact of situational variables, such as reverse logistic cost, technology and replacement part availability, etc., on the EOL decision making, i.e., Pareto front, and thus leading to improved strategy planning and better productdesign. The case study involving EOL treatment of two types of desktop phones is described to illustrate the utility of the proposed methodology.     

Cost and reliability based strategies for fatigue maintenance planning of floating structures

Floating structures are designed in such way that the appearance of fatigue failures cannot be avoided, implying the need for inspections during their life. Their maintenance has to be planned from an economic point of view so as to minimize maintenance costs but satisfying a minimum reliability level. A method is proposed to quantify the repair costs resulting of different reliability-based maintenance strategies. As an application of this approach a side shell structure typical of a floating production unit is analysed and the influence of different parameter with respect to the repair cost is also studied here.     

A fuzzy knowledge based method for maintenance planning in a power system

The inspection planning in electric power industry is used to assess the safety and reliability of system components and to increase the ability of failure situation identification before it actually occurs. It reflects the implications of the available information on the operational and maintenance history of the system. The output is a ranked list of components, with the most critical ones at the top, which indicates the selection of the components to be inspected.In this paper, we demonstrate the use of a fuzzy relational database model for manipulating the data required for the criticality component ranking in thermal power systems inspection planning, incorporating criteria concerning aspects of safety and reliability, economy, variable operational conditions and environmental impacts. Often, qualitative thresholds and linguistic terms are used for the component criticality analysis. Fuzzy linguistic terms for criteria definitions along with fuzzy inference mechanisms allow the exploitation of the operators' expertise.The proposed database model ensures the representation and handling of the aforementioned fuzzy information and additionally offers to the user the functionality for specifying the precision degree by which the conditions involved in a query are satisfied.In order to illustrate the behavior of the model, a case study is given using real inspection data.     

Lagrangian relaxation based heuristic for an integrated production and maintenance planning problem

In this paper, an approach is developed to solve the joint production planning and maintenance problem. Moreover, some propositions and mathematical properties were suggested and applied in the proposed heuristic to solve this integrated problem. It is based on Lagrangian relaxation (Fisher, M.L., 1981 Fisher, M.L. 1981. Lagrangian relaxation method for solving integer programming problem. Management Science, 27(1): 1–18. [Crossref], [Web of Science ®] , [Google Scholar]. Lagrangian relaxation method for solving integer programming problem. Management Science, 27 (1), 1–18) of the capacity constraints and sub-gradient optimisation. At every step of the sub-gradient method, a smoothing procedure is applied to the solution of the Lagrangian problem to ensure the feasibility of solution and to improve it. Computational experiments are carried out to show the results obtained by our approaches and are compared to those of a commercial solver.     

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.     

Value-driven maintenance planning for a production plant

Maintenance involves maintaining and securing the equipment and systems in, or restoring them to, a state in which they can perform the required functions. The challenge for maintenance planning is to identify appropriate objects and tasks for preventive maintenance and ensure that there are adequate resources for the repair actions. In this paper we will present a maintenance planning approach, called value-driven maintenance planning (VDMP), to emphasise the fact that the objectives of the plant are the reference points for specifying functional requirements for the equipment locations and equipment.The three basic phases of the VDMP planning approach are:

(i)

the definition of the fundamental, strategic and maintenance (means) objectives of the production plant,

(ii)

the classification of equipment locations (including one or several equipment items) into maintenance classes, each associated with a functional requirement specification, and

(iii)

the selection of maintenance strategies and maintenance tasks (including timing) for equipment assigned to the equipment locations.

Feedback of the operational experience is crucial for the application of the maintenance planning approach. As a consequence, the approach is developed to support the continuous improvement of the cost effectiveness of maintenance rather than planning of the initial maintenance programme of a new plant. Analysis techniques are presented and challenges of application are discussed. The application of the VDMP approach to a gasification plant in Finland is also presented.     

Fuzzy logic based leanness assessment and its decision support system

Manufacturing organisations have been witnessing a transition from mass manufacturing to lean manufacturing. Lean manufacturing is focused on the elimination of obvious wastes occurring in the manufacturing process, thereby enabling cost reduction. The quantification of leanness is one of the contemporary research agendas of lean manufacturing. This paper reports a study which is carried out to assess the leanness level of a manufacturing organisation. During this research study, a leanness measurement model has been designed. Then the leanness index has been computed. Since the manual computation is time consuming and error-prone, a computerised decision support system has been developed. This decision support system has been designated as FLBLA-DSS (decision support system for fuzzy logic based leanness assessment). FLBLA-DSS computes the fuzzy leanness index, Euclidean distance and identifies the weaker areas which need improvement. The developed DSS has been test implemented in an Indian modular switches manufacturing organisation.     

Optimization for condition-based maintenance with semi-Markov decision process

The semi-Markov decision model is a powerful tool in analyzing sequential decision processes with random decision epochs. In this paper, we have built the semi-Markov decision process (SMDP) for the maintenance policy optimization of condition-based preventive maintenance problems, and have presented the approach for joint optimization of inspection rate and maintenance policy. Through numerical examples, the improvement of this method is compared with the scheme, which optimizes only over the inspection rate. We also find that under a special case when the deterioration rate at each failure stage is the same, the optimal policy obtained by SMDP algorithm is a dynamic threshold-type scheme with threshold value depending on the inspection rate.     

A model for preventive maintenance planning by genetic algorithms based in cost and reliability

This work has two important goals. The first one is to present a novel methodology for preventive maintenance policy evaluation based upon a cost-reliability model, which allows the use of flexible intervals between maintenance interventions. Such innovative features represents an advantage over the traditional methodologies as it allows a continuous fitting of the schedules in order to better deal with the components failure rates. The second goal is to automatically optimize the preventive maintenance policies, considering the proposed methodology for systems evaluation.Due to the great amount of parameters to be analyzed and their strong and non-linear interdependencies, the search for the optimum combination of these parameters is a very hard task when dealing with optimizations schedules. For these reasons, genetic algorithms (GA) may be an appropriate optimization technique to be used. The GA will search for the optimum maintenance policy considering several relevant features such as: (i) the probability of needing a repair (corrective maintenance), (ii) the cost of such repair, (iii) typical outage times, (iv) preventive maintenance costs, (v) the impact of the maintenance in the systems reliability as a whole, (vi) probability of imperfect maintenance, etc. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR was used as a case study. The results obtained by this methodology outline its good performance, allowing specific analysis on the weighting factors of the objective function.     

基于可拓工程方法的维修保障系统效能评估研究

分析了维修保障系统效能的影响因素．应用可拓工程方法建立了维修保障系统效能多指标参数的物元可拓评估模型，以定量的结果对维修保障系统效能进行了评估分级，并与层次分析法和突变评价法的结果进行了比较，结论基本一致．为维修保障系统效能评估问题寻求出一种新的方法，其评估结果可为决策提供定量依据．     

A data-driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

Wheelsets absorb a significant part of the maintenance budget of any train operating company. Although wheel wear has been an extensively discussed topic in the literature, wear rates are very rarely characterized by using degradation data in a real-world case study aimed at identifying optimal maintenance policies including both degradation and recovery modeling. Furthermore, wheel defects, which impose an additional challenge to the modeling of the lifecycle of the wheels, are usually considered separately in the literature. In this study, conducted at a Portuguese train operating company, 17 years of inspection data are used to estimate wheel wear rates and survival curves, which are further incorporated into a Markov decision process (MDP) model. A bidimensional framework considering discrete intervals of wheel diameter along with a quantitative variable (kilometers since last turning/renewal) is used to represent the possible wheel states, while the probability of a defect interfering with the wheel maintenance schedule is modeled by contemplating survival curves derived from a Cox proportional-hazards model. Optimal results in terms of minimal cost policy are discussed in the context of the MDP, but a more realistic and easy-to-implement policy fixing one of the parameters is compared with the optimal policy. Results showed that in practice train operating companies might benefit from using the easy-to-implement policy, which has an associated long-run average cost only about 1% higher than the one suggested by the optimal decision map.     

Risk-based emergency decision support

In the present paper we discuss how to assist critical decisions taken under complex, contingent circumstances, with a high degree of uncertainty and short time frames. In such sharp-end decision regimes, standard rule-based decision support systems do not capture the complexity of the situation. At the same time, traditional risk analysis is of little use due to variability in the specific circumstances. How then, can an organisation provide assistance to, e.g. pilots in dealing with such emergencies?A method called ‘contingent risk and decision analysis’ is presented, to provide decision support for decisions under variable circumstances and short available time scales. The method consists of nine steps of definition, modelling, analysis and criteria definition to be performed ‘off-line’ by analysts, and procedure generation to transform the analysis result into an operational decision aid. Examples of pilots' decisions in response to sudden vibration in offshore helicopter transport method are used to illustrate the approach.     

基于路段时间窗考虑备选路径的AGV路径规划

针对自动化集装箱码头基于卸箱任务的自动导引车（automated guided vehicle,AGV）路径规划问题,结合最优路径数学模型、路径搜索方法和时间窗,提出了一种基于路段时间窗的AGV路径规划方法。首先,在给AGV下派任务的基础上,用最优路径数学模型为AGV规划出最短路径;其次,用路径搜索方法搜索AGV的备选路径,在路径长度相同的情况下,按照路径中转折次数确定备选路径优先级,转折次数少的备选路径优先级高;最后,在各AGV最短路径下,设置各个路段的时间窗,时间窗无重叠则表明AGV无冲突,对于时间窗重叠的路段,采用在原路径上插入时间窗或者在备选路径上插入时间窗的方法,再进行时间窗重叠测试,若还存在重叠的,则继续调整至最终实现多AGV的无冲突路径规划。为了验证方法的有效性,以8台AGV分区同时工作为例,用实例证明所提出的路径规划方法的避碰效果。结果显示该方法能为多台同时工作的AGV规划出一条无冲突优化路径,并且用时较短;在试验中发现选择在备选路径上插入时间窗的方法效果更好。研究表明所提方法能有效实现AGV的避碰,提高AGV利用率和自动化集装箱码头的运作效率。     

基于改进TOPSIS法的维修保障系统效能评估研究

维修保障系统涉及因素众多，对其进行效能评估是一项非常复杂的问题．在分析TOPSIS厦其改进方法的基础上，构建了维修保障系统效能评估指标体系，应用改进TOPSIS法对维修保障系统效能进行了综合评估，并将评估结果与AHP和突变评价法的结果进行比较，结论一致．为维修保障系统效能评估问题寻求出一种新的方法，其评估结果可为决策提供定量依据．     

Integrated decision support framework for distribution network design

In the current business environment of competition shifting from company-to-company to supply chain against supply chain, there is an increasing need for logistics providers (LSP) to gain cost effectiveness with no compromise on service levels. One key initiative that LSP can undertake is to allocate and utilise their storage and transportation assets optimally. The current work is an attempt in that direction and provides a hands-on decision support framework that integrates MCDM, network optimisation, and discrete event simulation to address distribution network design and transport optimisation. The use case of PT Pos Indonesia in the metropolitan area of Greater Surabaya highlights the benefits of combining ICT tools with well-established best practices in supply chain management. Findings of this work highlight that the number of distribution facilities for the case at hand should be reduced from nine to four. Compared to the existing, the identified network configuration unlocks potential cost saving in transportation and warehousing of 18%–22%, reduces CO₂ emissions by nearly 30%, with no deterioration in service level. Managerial implications about transportation policies are highlighted in the conclusive part of this paper.