Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme

Outsourcing selective maintenance problem in failure prone multi-component systems

In many industrial settings, there are systems designed to perform consecutive missions interspersed with finite breaks during which only a set of component repairs can be carried out due to limited time, budget, or resources. The decision maker then has to decide which components to repair in order to guarantee a given performance level. This is known as the selective maintenance problem (SMP). This paper introduces a new variant of the SMP by specifically taking into account the maintenance outsourcing alternative. A novel integrated non-linear programming formulation where both the in-house and outsourcing maintenance alternatives are accounted for is developed and optimally solved. The effect of the outsourcing alternative on maintenance decisions is investigated through numerical experiments. The overall results obtained demonstrate the validity of the proposed approach. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Selective maintenance for multi-state series-parallel systems under economic dependence

YesThis paper presents a study on selective maintenance for multi-state series-parallel systems with economically dependent components. In the selective maintenance problem, the maintenance manager has to decide which components should receive maintenance activities within a finite break between missions. All the system reliabilities in the next operating mission, the available budget and the maintenance time for each component from its current state to a higher state are taken into account in the optimization models. In addition, the components in series-parallel systems are considered to be economically dependent. Time and cost savings will be achieved when several components are simultaneously repaired in a selective maintenance strategy. As the number of repaired components increases, the saved time and cost will also increase due to the share of setting up between components and another additional reduction amount resulting from the repair of multiple identical components. Different optimization models are derived to find the best maintenance strategy for multi-state series-parallel systems. A genetic algorithm is used to solve the optimization models. The decision makers may select different components to be repaired to different working states based on the maintenance objective, resource availabilities and how dependent the repair time and cost of each component are. © 2013 Elsevier Ltd. All rights reserved.Natural Sciences and Engineering Research Council of Canada (NSERC) and Vietnam International Education Development (VIED

Selective maintenance for multistate series systems with S-dependent components

YesIn this paper, we will consider the selective maintenance problem for multistate series systems with stochastic dependent components. In multistate systems, the health state of a component may vary from perfect functioning to complete failure. The stochastic dependence (S-dependence) between components is discussed and categorized into two types in multistate context. First, the failure of a component can immediately cause complete failures of some other components in the system. Second, as components deteriorate, the reduced working performance rate of a multistate component affects the state as well as the degradation rate of its subsequent components in series structure. The system reliability is evaluated using an approach based on stochastic process. A cost-based selective maintenance model is developed for the multistate system with S-dependent components to maximize the total system profit, which includes the production gain and loss in the next mission as well as possible maintenance costs for the system. Analyses of systems with independent and dependent components are provided. It is observed that ignoring S-dependence in the system may lead to alternative maintenance decision making and an optimistic estimation of the system performance

Joint optimization of the selective maintenance and repairperson assignment problem when using new and remanufactured spare parts

This paper deals with the problem of the selective maintenance (SM) optimization for a series-parallel system. The system performs several missions with breaks between consecutive missions. To improve the system reliability during the next mission, its components are maintained during the breaks. Current models in the SM literature usually assume that when a component is subjected to a replacement, it is done by a new one. This paper introduces a novel variant of the selective maintenance problem (SMP) where a mixture of new and reconditioned/remanufactured parts are used to carry out replacements. It has indeed been proved that remanufacturing processes can extend the life of a product returned from the field. This provides not only economic opportunities but also favours sustainable practices. Accordingly, a novel mixed integer nonlinear programming model of the SMP is developed and optimally solved. Numerical experiments show how using reconditioned spare parts impacts the SM decisions. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Multi-State System Reliability: A New and Systematic Review

AbstractReliability analysis considering multiple possible states is known as multi-state (MS) reliability analysis. Multi-state system reliability models allow both the system and its components to assume more than two levels of performance. Through multi-state reliability models provide more realistic and more precise representations of engineering systems, they are much more complex and present major difficulties in system definition and performance evaluation. MSS reliability has received a substantial amount of attention in the past four decades. This article presents a new and systematic review about multi-state system reliability. A timely review is an effective work related to improving the development of MSS theory. The review about the latest studies and advances about multi-state system reliability evaluation, multi-state systems optimization and multi-state systems maintenance is summarized in this paper

Imperfect Maintenance Models, from Theory to Practice

The role of maintenance in the industrial environment changed a lot in recent years, and today, it is a key function for long-term profitability in an organization. Many contributions were recently written by researchers on this topic. A lot of models were proposed to optimize maintenance activities while ensuring availability and high-quality requirements. In addition to the well-known classification of maintenance activities—preventive and corrective—in the last decades, a new classification emerged in the literature regarding the degree of system restoration after maintenance actions. Among them, the imperfect maintenance is one of the most studied maintenance types: it is defined as an action after which the system lies in a state somewhere between an “as good as new” state and its pre-maintenance condition “as bad as old.” Most of the industrial companies usually operate with imperfect maintenance actions, even if the awareness in actual industrial context is limited. On the practical definition side, in particular, there are some real situations of imperfect maintenance: three main specific cases were identified, both from literature analysis and from experience. Considering these three implementations of imperfect maintenance actions and the main models proposed in the literature, we illustrate how to identify the most suitable model for each real case

A Multi-Objective Approach to Optimize a Periodic Maintenance Policy

The present paper proposes a multi-objective approach to find out an optimal periodic maintenance policy for a repairable and stochastically deteriorating multi-component system over a finite time horizon. The tackled problem concerns the determination of the system elements to replace at each scheduled and periodical system inspection by ensuring the simultaneous minimization of both the expected total maintenance cost and the expected global system unavailability time. It is assumed that in the case of system elements failure they are instantaneously detected and repaired by means of minimal repair actions in order to rapidly restore the system. A non-linear integer mathematical programming model is developed to solve the treated multi-objective problem whereas the Pareto optimal frontier is described by the Lexicographic Goal Programming and the \u3b5-constraint methods. To explain the whole procedure a case study is solved and the related considerations are given

Heuristic optimisation for multi-asset intervention planning in a petrochemical plant

Large infrastructure assets commonly require high intervention costs, but the absence of an effective asset management plan can bring about a massive production loss for a company. Hence, managing these assets is considered a daunting task and is even more complicated if these assets operate collectively to produce an output. This paper explores a pragmatic approach to a multi-asset intervention scheduling problem through a case study of a vessel fleet in a petrochemical plant. After the relationship between the asset configuration and the system output is defined, an optimisation model with an objective to jointly minimise cost and risk is developed. Since the calculation of risk profiles across the fleet requires complex non-linear functions, a genetic algorithm is employed to search for an optimal combination of intervention schedules. Compared to the current run-to-failure strategy, the optimal strategy results in a significant reduction in system failure risk and a substantial improvement in long-term fleet conditions while reducing the total cost