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This paper deals with imperfect preventive maintenance (PM) optimisation problem. The system to be maintained is typically a production system assumed to be continuously monitored and subject to stochastic degradation. To assess such degradation, the proposed maintenance model takes into account both corrective maintenance (CM) and PM. The system undergoes PM whenever its reliability reaches an appropriate value, while CM is performed at system failure. After a given number of maintenance actions, the system is preventively replaced by a new one. Both CM as well as PM are considered imperfect, i.e. they bring the system to an operating state which lies between two extreme states, namely the as bad as old state and as good as new state. The imperfect effect of CM and PM is modelled on the basis of the hybrid hazard rate model. The objective of the proposed PM optimisation model consists on finding the optimal reliability threshold together with the optimal number of PM actions to maximise the average availability of the system. A mathematical model is then proposed. To solve this problem an algorithm is provided. A numerical example is presented to illustrate the proposed maintenance optimisation model. [ABSTRACT FROM AUTHOR]

This study deals with the problem of dependence between production and failure rates in the context of a multi-product manufacturing system. It provides an answer about how to produce (i.e. the production rates) and what to produce (i.e. which product) over a finite horizon of H periods of equal length. We consider a single randomly failing and repairable manufacturing system producing two products Pa and Pb. The product Pa is produced to supply the strategic demand d(k) of the principal customer via a buffer stock S over k periods (k = 1, 2, ... , H). The second product Pb is produced to meet a secondary but very profitable demand. It is produced during a given interval at the end of each period k. We develop a genetic algorithm to determine simultaneously the optimal production rate of the first product during each period k and the optimal duration of the production interval of the second product, maximising the total expected profit. [ABSTRACT FROM AUTHOR]

Multi-state systems (MSS) are systems whose stochastic degradation process is characterised by several performance levels varying from nominal functioning to complete failure. MSS arise naturally in many application areas. MSS reliability evaluation and estimation has received much attention from researchers and a wide range of papers dealing with MSS have been published. In this paper, an approach based on Kronecker algebra combined with stochastic processes is proposed to evaluate the reliability of a series–parallel MSS. The main advantage of the proposed approach is that the mathematical expressions of the MSS reliability indices are derived from data of individual elementary components without generating the whole, possibly huge, MSS state space. Furthermore, the approach is well formalised and easy to implement thanks to Kronecker algebra operators. Examples are given to illustrate the proposed approach. [ABSTRACT FROM PUBLISHER]

Nowadays companies have to consider the impact of their production systems on the environment. This paper presents a condition-based maintenance policy for a production system under environmental constraints. The studied system is subject to random failures that can cause production losses and have a negative influence on the environment. Such failures cause the release of a refrigerant gas that induces a heavy penalty cost from a given threshold level. A mathematical model is developed in order to reduce environmental degradation where the average cycle cost per time unit is considered as the objective function. A numerical example and a sensitivity study are given to illustrate the proposed model. [ABSTRACT FROM PUBLISHER]

This article, inspired by an industrial problem, develops efficient maintenance and just-in-time production policies in a subcontracting environment according to two orientations. The first invokes subcontracting with the objective of satisfying a constant customer demand knowing that our production system, composed of a machine M1, cannot satisfy the totality of demand. Subcontracting is represented by a machine M2 which has a constant failure rate, while three maintenance policies for M1 are tested and evaluated. The second orientation takes the perspective of our production system as a supplier which is obliged to allocate part of its production capacity to subcontracting so as to satisfy a constant demand. We consider a production system made up of two machines, both of which produce a single type of product, are subject to breakdowns and can carry out subcontracting tasks. The objective of this part of the article is to prove the efficiency of the so-called integrated maintenance policy, which combines production and maintenance decisions in a subcontracting environment. [ABSTRACT FROM AUTHOR]

In this paper, we develop a joint quality control and preventive maintenance policy for a randomly failing production system producing conforming and non-conforming units. The considered system consists of one machine designed to fulfil a constant demand. According to the proportion l of non-conforming units observed on each lot and compared to a threshold value lm, one decides to undertake or not maintenance actions on the system. In order to palliate perturbations caused by the stopping of the machine to undergo preventive maintenance or an overhaul, a buffer stock h is built up to ensure the continuous supply of the subsequent production line. A mathematical model is developed and combined with simulation in order to determine simultaneously the optimal rate, [image omitted] and the optimal size h* which minimize the expected total cost per time unit including the average costs related to maintenance, quality and inventory. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the problem of joint maintenance and production management under a subcontracting constraint. We will study a system with a machine M which undergoes corrective maintenance actions together with preventive maintenance actions, both of which have random durations. A stock S with a capacity h is developed at a rate α = Umax - d, where Umax = production rate, d = constant demand, and Umax > d. In addition, machine M is allocated to carry out subcontracting tasks (ST) at a frequency A1, for an effective duration A2, during which time it cannot be used to fulfil our system's demand d. We initially study the system analytically when the machine M is forced to carry out a single subcontracting task during each cycle T. We thereafter present a generalization of the subcontracting constraint which entails assigning the machine M to subcontracting several times during each cycle. [ABSTRACT FROM AUTHOR]

This paper investigates an integrated strategy of inventory control and preventive maintenance for a randomly failing production unit subject to a minimum required availability level. The production unit is submitted to a maintenance action as soon as it reaches a certain age m or at failure, whichever occurs first. A buffer stock h is built up at time A from the start of a production cycle in order to allow a continuous supply of the subsequent production unit at a constant rate during repair and preventive maintenance actions whose respective durations are random. A mathematical model and a numerical procedure are developed to find simultaneously the optimal values of the three decision variables (m*, h*, A*) which minimize the total average cost per time unit and satisfy the availability constraint. [ABSTRACT FROM AUTHOR]

The paper describes a new preventive maintenance approach for manufacturing systems under environment constraints. The manufacturing system under consideration consists of a machine M1 that produces a single product in a Just-in-Time context. To satisfy a constant demand d, the system called upon another machine M2 (the subcontractor), comprising the so-called environment, which produces at a certain rate the same type of product as M1. Both machines are subjected to random failures. Whereas machine M2 is uncontrollable from the maintenance point of view, an age-limit policy is used for preventive maintenance of machine M1. It is proved that the best age to perform preventive maintenance depends on the history of machine M1 and the state of M2. A numerical example is used to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

A marked graph-based control synthesis method is proposed for discrete event systems. The proposed approach solves the forbidden state problems characterized by a set of General Mutual Exclusion Constraints (GMEC) and takes into account unobservable transitions. It exploits the structural dependence of places involved in GMEC constraints. For each case, analytical expression of the GMEC in its worst case, in terms of reachable marking, is given according to the observable firing sequence of the system. These constraints are then evaluated on-line by the supervisor. The proposed approach is illustrated on a real example. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated method for preventive maintenance and inventory control of a production line, composed of n machines (n ≥ 1) without intermediate buffers. The machines are subject to failures and an age-dependent preventive maintenance policy is used. Approximate analytical results are proposed for the one machine case. Simulation software is used to model and simulate the behaviour of the production line of n machines under various maintenance and inventory control strategies. A methodology combining the simulation and genetic algorithms is proposed jointly to optimize maintenance and inventory control policies. Results are compared with the analytical solutions. [ABSTRACT FROM AUTHOR]

In this paper a condition-based maintenance model is proposed for a single-unit system of production of goods and services. The system is subject to random deterioration which impacts not only the product quality but also the environment. We assume that the environment degrades whenever a specific level of system deterioration is reached. The proposed maintenance model aims to assess the degradation in such a way to reduce the deterioration of the environment. To control this deterioration, inspections are performed and after which the system is preventively replaced or left as it is. Preventive replacement occurs whenever the level of the system degradation reaches a specific level threshold. The objective is to determine optimal inspection dates which minimize the average total cost per unit of time in the infinite horizon. Cost function is composed of inspection and maintenance costs in addition to a penalty cost due to environmental deterioration. The maintenance optimization model is formally derived. On the basis of Nelder-Mead method, inspection dates as optimal solutions are computed. A numerical example is provided to illustrate the proposed maintenance model. [ABSTRACT FROM AUTHOR]

The purpose of this study is to propose an integrated strategy to determine jointly efficient business and maintenance plans. The studied system is subject to random failures with a dynamic failure law. It must perform a set of missions (among M possible missions) over a finite planning horizon. Each mission may have different characteristics that depend on operational and environmental conditions. The determination of a business plan consists in choosing and scheduling the missions to be performed. To maximize the net profit (profits generated by the achievement of missions minus maintenance costs), two meta-heuristics based on genetic algorithms are developed. The first genetic algorithm is used to determine the business plan and the second one generates an efficient maintenance plan. Two maintenance policies are studied: a minimalist policy which involves only corrective maintenance actions and another policy, called sequential, which involves several imperfect preventive maintenance activities performed at predetermined times. Two cases are studied for the latter strategy. The first one considers the maintenance effectiveness factor as being the same for all preventive maintenance actions and we search for the best factor. In the second case, we consider maintenance actions with different efficiency factors and we look for the optimal value of each factor. Finally, a numerical example illustrates the proposed approach and the difference between the maintenance policies. [ABSTRACT FROM AUTHOR]

The purpose of this study is to propose and model periodic and sequential preventive maintenance policies for a system that performs various missions over a finite planning horizon. Each mission can have different characteristics that depend on operational and environmental conditions. These proposed preventive maintenance policies are defined and modeled mathematically. The study of these two policies is based on a dynamic system failure law that takes into account the different missions performed. The first step is to determine the optimal business plan to achieve, i.e. the set of missions to perform in order to maximize the profit of missions minus maintenance costs. Thus, for each plan, we determine the maintenance planning considering two policies. The first preventive maintenance policy is periodic and the objective is to determine the optimal number of preventive maintenance to achieve. For the second policy, namely sequential, we calculate the optimal number of preventive maintenance intervals and the duration of these different intervals. [ABSTRACT FROM AUTHOR]

Background: Adverse Childhood Experiences (ACEs) have become an alarming phenomenon exposing individuals at a great risk of developing mental health issues. To date, there remains a lack of adequate researches on ACEs especially among adolescents. This survey aimed to describe the prevalence of childhood adversities and to determine their role in developing risky behaviours amongst Tunisian youth. Methods: We conducted a cross sectional study among schooled adolescents in the governorate of Mahdia (Tunisia) from January to February 2020. Exposure to violence was assessed using the validated Arabic version of the WHO ACE questionnaire. The Internet Addiction Test (IAT) and the Hospital Anxiety and Depression scale (HAD) were used as a screening tool for internet addiction, anxiety and depression. Results: A total of 1940 adolescents were recruited. The majority of them (97.5%) reported experiencing at least one ACE. Emotional neglect (83.2%) and witnessing community violence (73.5%) were the most reported ACE categories. According to gender, males had higher rates of exposure to physical abuse and social violence than females. The most common risky behaviour was internet addiction (50%) followed by substance use (16.6%). After adjustment for anxiety and depression, the dose-response association between ACE and risky behaviours was well established. Conclusions: These results among youth have both public health and societal implications. Key messages: The prevalence of exposure to adverse childhood experiences as well as risky behaviors are alarming among Tunisian adolescents. Screening addictive behaviors among youth with a history of childhood adversities is needed. [ABSTRACT FROM AUTHOR]