Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors propose a flexible way to develop typical MFS models, for any number of machines in the workstation, spare machines and maintenance crews. Arena simulation language is used to understand a specific MFS, create the corresponding MFS model and analyze most common performance measures.

Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors explore and present a possible way to construct a MFS model using Arena simulation language, where some of the most common performance measures are identified, calculated and analysed. Nevertheless this paper would concentrate on the two most important performance measures in maintenance systems: system availability and maintenance total cost. As far as these two indicators are concerned, it was then quite clear that they assumed different behavior patterns, specially when using extreme values for periodic overhauls rates. In this respect, system availability proved to be a more sensitive parameter.

Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors explore and present a possible way to construct a MFS model using Arena simulation language, where some of the most common performance measures are identified, calculated and analysed.

Abstract
In industry, spare equipments are often shared by many workplaces with identical equipments to assure the production rate required to fulfill delivery dates. These types of systems are called “Maintenance Float Systems”. The main objective of managers that deal with these types of systems is to assure the required capacity to deliver orders on time and at minimum cost. Not delivering on time has often important consequences; it can cause loss of costumer goodwill, loss of sales and can damage organization's image. Maintenance cost is the indicator more frequently used to configure maintenance float systems and to invest in maintenance workers or spare equipments. Once the system is configured, other performance indicators must be used to characterize and measure the efficiency of the system. Different improvement initiatives can be performed to enhance the performance of maintenance float systems: performing preventive maintenance actions, implementation of autonomous maintenance, improvement of equipments maintainability, increase of maintenance crews’ efficiency etc. “Carrying out improvement based on facts” is a principle of Total Quality Management (TQM) in order to step to business excellence. It requires monitoring processes through performance measures. This work aims to characterize and highlight the differences and relationships between three types of performance measures equipment availability, equipment utilization and workplace occupation, in the context of mainte- nance float system. Definitions and expressions of these three indicators are developed for maintenance float systems. The relationship between maintenance float systems efficiency and the referred indicators is shown. Other indicators are also proposed and compared with the first ones (number of standby equipments, queue length etc.). © 2009 by Taylor & Francis Group, LLC.

Abstract
Distribution lines are one of the most critical assets in distribution networks. In fact, overhead lines and subterranean cables are subjected to numerous internal and external factors that can cause failures and degradation over time. To prevent customer disconnection and to ensure continuous electricity delivery, the Distribution System Operator (DSO) strives to minimize the number of distribution line failures by carrying out inspection and preventive maintenance actions. Typically, HV and MV networks cover wide areas of the territory and comprise many lines of different types (overhead & subterranean) and equipment (e.g. conductor, isolator, poles), which makes it difficult to predict when failures will occur, which distribution line will fail and its location. The latter information is especially relevant since some distribution lines can cover a considerable distance. Motivated by a real-world application, this work presents a methodology to predict and locate future HV and MV distribution line failures. The methodology encompasses clustering techniques to group lines sharing similar characteristics, identifying the most relevant factors on the lines' degradation. In addition, historical records are leveraged by a prediction algorithm to estimate the number of failures and the failed section of the line. The approach was validated using data from the Portuguese HV and MV DSO (E-Redes). The results highlight the advantages of the proposed method compared with benchmark approaches. © 2021 The Institution of Engineering and Technology.

Abstract
In many industries, the employed maintenance policies contributed to the concentration of asset replacements in a short period of time. Thus, the number of O&M activities increases, leading to rising operational costs that are not compatible with the available resources. Moreover, these assets encompass multiple failure modes, which reduce asset availability and influence its longevity. Because asset degradation is stochastic, a considerable amount of uncertainty is associated with this problem. The recent technological advances in monitoring technology may foster a reduction in degradation uncertainty but the extra effort regarding the investment plan must be carefully planned. Bearing this in mind, we propose a methodology to determine the investments in the installation of monitoring equipment accounting for the impact in maintenance budget for O&M activities for a resource-dependent asset portfolio with multiple failure modes. The budget is shared between multiple assets and must be determined, a priori, and managed throughout an established time horizon. Since investing in monitoring equipment requires substantial capital due to the system size, DMs have to define which and when a given asset monitoring technology will be installed. Hence, not every asset may have the same monitoring technology and, consequently, the same degradation uncertainty. We formulate the problem as a stochastic optimization problem to capture the dynamic uncertainty in the assets’ condition. Due to its inherent complexity, we employ a meta-heuristic based on a co-evolutionary genetic algorithm to achieve high-quality solutions under reasonable computational time for real world-sized systems. The approach is validated in a case study in the electricity distribution in which a system operator has to manage a portfolio of power transformers operating under different operational conditions. © ESREL 2021. Published by Research Publishing, Singapore.