Maintenance Scenarios Research Articles

Mixed-model assembly line balancing problem considering preventive maintenance scenarios: MILP model and cooperative co-evolutionary algorithm

Considering preventive maintenance (PM) scenarios in assembly line balancing (ALB) problem has been proven to be an effective way to promote line efficiency. However, previous research only focused on the single-model assembly line and ignored the mixed-model assembly line, resulting in the line cannot meet the growing customization needs of customers. This paper addresses the mixed-model ALB considering PM scenarios (MALB_PMs). A mixed-integer mathematical formulation is proposed to optimize the cycle time and task alteration. The cooperative co-evolutionary algorithm is introduced to simplify the large-scaled cases via the powerful divide-and-conquer architecture and is enhanced with four improvements. An archive is generated to save complete solutions with better performance and evaluate the fitness of solutions. A mixed-model variable step-size decoding method is designed to speed up the decoding process of the algorithm. One inter-population crossover operator is designed to enhance the communication among sub-problems. Four objective-oriented neighbor search operators are proposed to promote the convergence performance of the algorithm. Finally, three small-scale instances are employed to illustrate the application of the mathematical model, and 39 cases are designed to test the performance of the developed algorithm. Experiment results demonstrate that the proposed mathematical formulation can obtain the Pareto solutions of small-scaled instances; The developed algorithm outperforms other algorithms on three criteria; The Pareto front obtained by this algorithm is closer to the True Pareto front.

Estimating greenhouse gas emissions arising from the maintenance of sewer networks

As the push to reduce global carbon dioxide emissions intensifies, infrastructure managers are rethinking how maintenance is planned and executed to minimise greenhouse gases (GHGs) without sacrificing the performance of their infrastructure networks. This paper addresses this need. The work includes a detailed description of the estimation of GHG emissions that arise from common sewer maintenance interventions. These detailed estimates were subsequently used to project emissions from three maintenance scenarios for a large sewer network. The study found that emissions from maintenance mainly originate from materials and that emissions from transport, traffic congestion and machinery were of secondary importance. The current practice promises the lowest emissions up to 2035 but does not address the ageing pipe network. As a consequence, the network condition and performance deteriorate further as failures rapidly accumulate. Embracing preventive maintenance coupled with circular economy thinking is shown to be an effective solution to improve the network condition and performance while at the same time offering savings of 6222 t carbon dioxide a year. These findings provide infrastructure managers with a detailed example of the potential of circular economy thinking in reducing long-term emissions along with insights into the expected trade-offs that arise with network performance and resource utilisation.

EE50 Olaparib As Maintenance Therapy for Women with Ovarian Cancer and Homologous Recombination Deficiency in Brazil: What Clinical and Cost Data Have Highest Impact on Cost-Effectiveness Analysis?

In Brazil, it is estimated that OC(ovarian cancer) impacts 7/100,000 habitants every year. Until recently, platinum-based therapies were considered standards of care and few innovations could significantly reduce disease progression or survival status(PFS). PARPi(Poly ADP-ribose polymerase inhibitor), such as olaparib, has shown to improve PFS by 51% compared to placebo in first-line maintenance scenario. This result was significantly better in homologous-recombination deficiency(HRD+). New oncology drugs require significant resource allocation, so we assessed which parameters have highest impact on cost-effectiveness analysis(CEA) of olaparib as maintenance therapy for women with HRD+OC, in the Brazilian private health care system.

Towards Road Sustainability—Part II: Applied Holistic Assessment and Lessons Learned from French Highway Resurfacing Strategies

Roads are major transportation infrastructure whose sustainability of maintenance practices has never been holistically assessed due to a lack of a proper method. This paper applies a newly developed assessment method (see article part I) on a 10-km-long section of French highway to fully compare the performance of various types of pavement resurfacing policies, for all the maintenance stakeholders, and considering pavement–vehicle interaction (PVI). After presenting the highway section and the parametrization of the model, four alternative resurfacing frequencies are compared to the French standard maintenance scenario over the pavement lifespan. Results show that increasing resurfacing frequency generates gains in terms of domestic production and employment, environmental damage (health, biodiversity, resources), user budgets, and local residents’ health damage created by traffic noise. Conversely, it entails financial losses for the road operator and government (tax revenues and net present value), as well as time losses for users. On the contrary, the consequences of a decrease in this frequency are the opposite. Excess fuel consumption due to PVI governs the scale of the environmental and financial gains or losses of highway maintenance policies. Optima in terms of health returns on investment and user savings appear to be around a 50% increase in maintenance funding: for each additional euro spent by the operator, there is a user gain of 3.5 euros and a human health gain of 710 euros. Sensitivity analyses indicate that the marginal gains are highly sensitive to the thickness of the resurfacing technique for macroeconomic indicators, global Net Present Value, and operator savings, while the gains are proportional to the traffic and International Roughness Indicator deterioration speed for tax revenue, users’ savings, time savings, noise, and environmental metrics. The other indicators are either slightly or not sensitive to these parameters. To conclude, the entire road maintenance system must be redesigned, from the tax system and funding schemes to the prioritization of road “green practices”, to align all the stakeholders’ interests towards a globally more sustainable road system.

Towards fluid force estimation of a water‐jetting aerial robot with hybrid kinematics‐force model

AbstractIn this paper, a three‐dimensional hybrid kinematics‐force (HKF) model for fluid force estimation coupled with position optimization of an aerial robot capable of high‐pressure fluid ejection is presented. Motivated by the archerfish's unique (water‐jetting) hunting mechanics, the HKF model comprises of two sub‐models (hybrid kinematics and fluid force estimation) to maintain a specific fluid force at the point‐of‐contact (POC) during high altitude fluid jetting for high‐risk maintenance (cleaning) scenarios. Industries with high‐risk cleaning applications seek to employ robots to assist cleaners in routine operations on infrastructures or equipment at a height such as photovoltaic e‐glasses, impact‐sensitive objects, and many others. For a fluid jet, the stand‐off distance represents the length of the fluid trajectory between the nozzle orifice and POC, and is also a vital variable in fluid force control. If the stand‐off distance between the ejection source and the target is large, the fluid stream loses energy along the trajectory and the force at the POC is weakened thus losing some of the jet pressure at the target surface. The HKF model exploits this by estimating and maintaining a specific force at the POC through position optimization of the onboard nozzle coupled with the knowledge of the fluid parameters. To innovate high structure cleaning, the HKF model can be employed on an aerial robot with fluid ejection capabilities for accurate fluid force control at the POC. For high‐pressure fluid ejection (on horizontal and vertical structures), the HKF model is able to predict the (high pressure) fluid force at the POC with less than 10% error. The proposed HKF model is tested and verified through simulations and the experimentation results are validated by flying the physical prototypes in actual deployment scenarios.

Removal of the Homolog Tellurium of Polonium by SiO2 Nanofiber Filter for Lead Alloy-Cooled Reactors.

The lead–bismuth eutectic (LBE) can be easily activated by neutron radiation to produce the radionuclide 210Po. It is therefore necessary to establish an effective method to remove vaporized polonium in the cover gas to prevent its release into the air in scenarios of reactor maintenance and coolant leakage accidents. This paper presents a SiO2 nanofiber membrane prepared based on the electrostatic spinning and calcination process. The SiO2 nanofiber membrane had the advantages of good flexibility, high-temperature resistance, and corrosion resistance. In the trapping experiments, the SiO2 nanofiber membrane filters showed excellent filtration performance at 300~400 °C, and the filtration efficiencies for Te, Pb, and Bi could reach 99%, 99%, and 98%, respectively. Proper filtration temperature and gas flow rate are important to maintain high filtration efficiency. After five cycles, the SiO2 nanofiber membrane filter still exhibited excellent cycle-use performance. In the density functional theory (DFT) calculations, PbPo and PbTe had strong interactions with amorphous SiO2, having adhesion energies of −2.96 to −2.83 eV/molecule.

Factors Influencing Building Maintenance Sourcing Decision in Nigeria Southwest Universities

Appropriate decision making on either to insource or outsource maintenance services in universities is a strategic task. Such a decision-making process is usually complex and challenging. In the sourcing of maintenance services, different sourcing option suits different maintenance scenarios, hence the need to study the factors influencing decision to insource or outsource maintenance services in universities. Through a cross-sectional survey, data were collected from 112 respondents. The relative influence index and ANOVA test were employed as statistical tools for the descriptive and inferential data analysis respectively. The results show that the development of in-house maintenance staff, technological requirements uncertainty, and the difficulty in getting trustworthy contractors are factors influencing maintenance insourcing, while the need for specialized expertise, strategic alliance with contractors, and the need for specialized management influence maintenance outsourcing. The study concludes that management factors influence insourcing decisions while outsourcing decision are influenced by strategic and technological factors.

A novel tool for cost and emission reduction related to ship underwater hull maintenance

International shipping plays a vital role in the world's transport system and economy. However, shipping faces challenges in terms of reducing its environmental and health impact, namely emission of greenhouse gases, air pollutants, and chemical substances to the marine environment. In particular, the roughness condition of underwater surfaces of a ship hull affects the ship's energy efficiency, with marine growth (biofouling) and mechanical roughness leading to propulsion powering penalties. Measures to control biofouling, using antifouling coatings and in-water hull cleaning, may also be associated with significant impacts to the marine environment. In the current study, a new tool is presented, HullMASTER (Hull MAintenance STrategies for Emission Reduction), which aims at enabling the shipping industry and authorities in the Baltic Sea region to make evidence-based decisions on hull maintenance strategies. HullMASTER simulates emissions to air and water, to calculate the differences in economic cost for operators, as well as health- and environmental damage costs between different hull maintenance scenarios. Validation of HullMASTER predictions against 40 vessel-years of in-service performance data on propulsive performance, with operations in the Baltic Sea region, shows good agreement, averaging within 5 percentage-point difference in propulsion penalty. Further, a scenario-based demonstration of HullMASTER on a general cargo vessel shows that, in the comparison between a silicone foul-release coating and business-as-usual scenario of a biocidal coating, retrofitting the coating to a foul-release coating can result in significant savings for society, i.e., along with marginal savings in cost for ship operators. Results for such comparisons and analysis will however be dependent on specific vessel cases and operational profiles, thence the value of an interactive tool such as HullMASTER.

An Insight in to the Automation of the Dairy Industry: A Review

The dairy industry was gaining enormous technical advances to meet out the targeted production quantity by converting the raw material into finished goods. Automation techniques have been implemented to ensure intense quality and safety for the desired food product. By entering this, automation technology for processing has gained great potential for improved safety, quality and profitability by optimizing process parameters and control. Automation technique involves multidisciplinary technologies such as robotics, Artificial Intelligence and several other mechanical, electrical, electronic devices and computers usually in combination. Among the various applications of automation in the dairy industry, Automatic milking systems or milking robots are very no table. It is also the most successful and significant in the current scenario of Livestock management and maintenance. However, there is a broad range of applications for automation in the dairy industry for continuous operation without any deviations and adjustments with desired consistency and shape according to the nature of the product, which cannot be done manually.

Optimizing Financial Allocation for Maintenance and Rehabilitation of Munster’s Road Network Using the World Bank’s RONET Model

This paper applies the Road Network Evaluation Tools (RONET) model to assess the economic impacts of urban pavement maintenance and rehabilitation in the city of Munster, Germany. The city’s road network includes main roads, main access roads, residential roads, and paved areas for pedestrians, cyclists, and parking spaces. The specific traffic loads applied to Munster’s network demand several different pavement materials, structures, and intervention procedures. This study aims to support stakeholders’ decision-making by assessing current expenditures, network conditions, and country-specific data to determine the appropriate financial allocation for recurrent maintenance, periodic maintenance, rehabilitation, and new pavement construction. Six scenarios comprising distinct pavement structures and maintenance strategies are modeled in RONET to perform the analysis. The outcomes include the future deterioration of pavements under different maintenance scenarios, the current and projected asset value of the network, and the total costs (road agency costs + user costs) of the network to society, considering each scenario being applied over a 20-year evaluation period. The RONET model also provides the annual average cost of each maintenance procedure and the additional costs to society while using a budget scenario other than ‘Optimal.’ The results indicate that Munster’s current investment program is in line with the ‘Optimal’ budget scenario proposed by RONET. In addition, the model suggests that performing recurrent and periodic interventions is more cost-effective than neglecting the conservation of pavements for an extended period and endorsing more extensive interventions in the future, such as rehabilitation or reconstruction.

An improved multi-objective multifactorial evolutionary algorithm for assembly line balancing problem considering regular production and preventive maintenance scenarios

• ALBP considering PM scenarios promotes the productivity and smoothness. • Multi-task optimization benefits the simultaneous optimization of all scenarios. • The matrix reduction method is proposed to reduce the decoding time. • The neighborhood search operator is designed to expedite the convergence speed. • Statistical results indicate IMO-MFEA with two improvements outperforms others. Assembly line balancing considering regular production and preventive maintenance scenarios (ALBP_RP/PM) is a new and effective way to simultaneously handle the assembly production and equipment maintenance. It generates multiple allocation plans in advance. If a workstation is to be maintained, the corresponding allocation plan is applied to bypass this workstation and enable production continuity within the assembly line. However, the ALBP_RP/PM was generally solved as a single-task optimization problem, implying the coupling relationship of allocation plans under different scenarios was essentially ignored. For this reason, it is hereafter treated as a multi-task optimization problem under the premise that the assembly line balancing under each scenario is an independent task. On this basis, an improved multi-objective multifactorial evolutionary algorithm with two extra improvements is proposed to minimize cycle times and operation alterations. Specifically, the native evolutionary operators are reformulated to encourage the inter-scenario sharing of effective knowledge fragments among allocation plans to accelerate the simultaneous optimization of all the tasks; a matrix reduction method is proposed to reduce the decoding time in generating operation sequence; a neighborhood search operator is designed to strengthen the convergence performance of the algorithm. Comprehensive computational results demonstrate that the inter-scenario sharing mechanism in the multi-task optimization expedites the convergence speed and improves the operation allocation plans effectively. Besides, the proposed algorithm clearly outperforms the other five single-task optimization algorithms and four multi-task ones under the same computational limits.

Comparative life cycle cost assessment of (lean) duplex stainless steel in wastewater treatment environments

Some unit operations in wastewater treatment plants (WWTPs), such as settling tanks and pipes for aeration or sludge transfer, are composed of austenitic stainless steel (EN 1.4307 or EN 1.4404) instead of galvanised or painted carbon steel to reduce the maintenance costs. The sensitivity to pitting and crevice corrosion of austenitic grades in certain WWTP environments has also led to the use of duplex grades. The purpose of this study is to evaluate the maintenance of piping systems (WWTPs) and its effect on their life cycle environmental impacts and costs (LCC) for both austenitic and duplex stainless steel grades. The final objective is to aid grade selection for piping in a WWTP environment. The considered functional unit (FU) is a complete piping system. Conventional austenitic stainless steel grades (e.g., EN 1.4404) are studied alongside duplex ones (e.g., EN 1.4362 and EN 1.4462). The calculated environmental impacts are the Global Warming Potential (GWP) and Primary Energy Demand (PED). The production, manufacturing, transport, use including maintenance activities, and end-of-life (burdens and credits) phases are included in the life cycle assessment (LCA). The maintenance activities consist of the required replacements of stainless steel piping during the lifespan of the WWTP. Thus, the service lives of the pipes included in the considered WWTP environment are determined based on long-term corrosion prediction models (power law), which predict the evolution of pit or crevice depth as a function of time. The model parameters are estimated based on own experimental results, supplemented by the existing literature. The corrosion rates determine the number and frequency of replacements, i.e., define the different scenarios of maintenance. The LCA, LCC and corrosion prediction models are then combined into a user-friendly tool, which can be used in industry for an appropriate grade selection for pipes in a WWTP environment. The tool includes several degrees of freedom such as piping distribution, water pressure, chloride content, replacement criteria, etc. The results show that using duplex stainless steel grade EN 1.4462 leads to lower GWP and PED at the end of the WWTP's service life of 40 years. This is mainly due to multiple replacements of the system's parts in wastewater with high levels of chloride (>3000 ppm) if more conventional austenitic stainless steel alloys such as EN 1.4404 are used. Leaner duplex stainless steel grades were also included in this LCC assessment. The duplex grade EN 1.4062 showed the lowest total LCC, thanks to its leaner chemical composition (i.e., lower nickel content) combined with good localized corrosion resistance.

Life Cycle Analysis of a Steel Railway Bridge over the Operational Period considering Different Maintenance Scenarios: Application to a Case Study

In the context of bridge management, three main types of maintenance actions can be considered. Maintenance actions can be taken preventively before the predefined limit condition is reached, or as a corrective measure in case those limits have been reached. The third possibility corresponds to the so‐called “doing nothing” scenario, in which no action is taken on the bridge. To be able to implement preventive maintenance, it is necessary to know the current condition of the bridge, as well as to be able to predict its performance. On the other hand, it is also important to be able to identify potentially threatening events that might occur in the analysis life period. This paper describes an integrated methodology to help bridge managers in defining an efficient maintenance program, considering the specific case of a railway bridge. The novelty of the methodology is focused on updating an existing methodology proposed by COST TU1406, by extending it to railway bridges and also by including the resilience analysis in case of a sudden event occurrence. The analysis considers a multi‐hazard future scenario, in which a flood event occurs while corrosion phenomena were already in place. The results show the feasibility of the proposed methodology as a support for the establishment of an efficient maintenance schedule to prevent bridge severe degradation, as well as to establish recovery plans in case of a sudden event.

MILP of multitask scheduling of geographically distributed maintenance tasks

Due to its proactive impact on the serviceability of components in a system, preventive maintenance plays an important role particularly in systems of geographically spread infrastructure such as utilities networks in commercial buildings. What makes such systems differ from the classical schemes is the routing and technicians' travel times. Besides, maintenance in commercial buildings is characterized by its short tasks’ durations and spatial distribution within and between different buildings, a class of problems that has not been suitably investigated. Although it is not trivial to assign particular duties solely to multi-skilled teams under limited time and capacity constraints, the problem becomes more challenging when travel routes, durations and service levels are considered during the execution of the daily maintenance tasks. To address this problem, we propose a Mixed Integer Linear Programming Model that considers the above settings. The model exact solution recommends collaborative choices that include the number of maintenance teams, the selected tasks, routes, tasks schedules, all detailed to days and teams. The model will reduce the cost of labor, replacement parts, penalties on service levels and travel time. The optimization model has been tested using different maintenance scenarios taken from a real maintenance provider in the UAE. Using CPLEX solver, the findings demonstrate an inspiring time utilization, schedules of minimal routing and high service levels using a minimum number of teams. Different travel speeds of diverse assortment of tasks, durations and cost settings have been tested for further sensitivity analysis.

Hybrid Dynamic Bayesian network method for performance analysis of safety barriers considering multi-maintenance strategies

Safety barriers play a critical role in preventing unintentional hydrocarbon flow leaking from reservoir to external environment or another formation during different offshore operation stages, and such a leakage has the potential to trigger cascading events and may lead to catastrophic consequences. The present study aims at the development of a hybrid DBN-based approach for dynamic performance analysis of safety barriers in the prevention of subsea downhole leakage incidents. Events in operation, such as different types of maintenances and process demand are taken into account to enhance the safety barrier performance. These factors could be analyzed by reflecting inspecting and repair activities of safety barriers with multistate-based multiphase Markov process. In order to obtain a dynamic and synthetic risk analysis of subsea downhole leakage, a dynamic Bayesian network-based model is proposed, incorporating the failure analysis of safety barriers and downhole multiple leakage pathways. Such analysis allows determining the dynamic risk characteristic of leakage events, and key safety barriers under different maintenance scenarios. Dynamic performance of such safety barriers is evaluated with respect to four aspects: preventive maintenance and imperfect repair, degradation effects, process demand and maintenance cost. The approach is tested through the application to a case study with an offshore oil and gas well. The results the importance of safety barrier performance in controlling the expected leakage scenarios.

Evolution of a flexible pavement deterioration, analyzing the road inspections results

For each policy of road maintenance, there are two methods of assessing the pavement condition, the environmental inspection which is generally based on a visual survey to define the various surface damages such as: Potholes, pull-outs, and cracks, and another physical one using new technology equipment to evaluate the pavement structural deterioration based on the measurements of the evenness and deflection using the longitudinal profile analyzer and the deflectograph. In this cedi, Pavement condition is an essential parameter for assessing pavement quality at a given time and to prevent future scenarios of possible maintenance. In this regard, the analysis, collection and abstraction of the different road data base is a very complex and essential process to study. In this respect, and in the framework of our collaboration with the Moroccan National Center for Road Studies and Research, we propose in this article a macroscopic evolution study of the pavement surface and structural condition by comparing the inspection results carried out between 2008 and 2016 with the one we carried out in 2018, on a 50 Km length starting from the kilometer point KP 0 + 080 to the KP 0 + 130 belonging to the Moroccan National road number 06, linking the city of Khemissat to Meknes. This operation begins with a section subdivision into 1 km sections, the analysis and abstraction of the Road Data Base (RDB), the representation in the form of a deterioration matrix subdivided into four levels: A, B, C and D, with the aim of quantifying and classifying the deteriorations, providing a picture of the paved surface network condition, identifying the equi-quality areas classified according to the deterioration levels, linear interpolation of the data, and a probabilistic projection of possible future deteriorations.

Anomaly Detection and Trend Prediction in Intelligent Operations Based on Prophet and S-ESD

Anomaly detection and trend prediction based on KPI business indicators play a key role in intelligent operation and maintenance in various fields. The types of indicators analyzed vary greatly for different scenarios of operation and maintenance, but all have time-series characteristics. In this paper, three core indicators are selected based on time series to build anomaly detection and trend prediction models. After preprocessing the index data, a statistical-based S-ESD time series anomaly detection method is firstly implemented. Anomaly detection is performed on three core indexes, and the detected anomalies are extracted and corrected. Use the revised KPI indicator data to further build a Prophet trend prediction model based on decomposable (trend + season + holiday), select MAPE as the evaluation index, perform model training on the indicator data, predict the next three days by step size, and get the fitting Curve and trend for the next three days. After parameter tuning, the MAPE of the Prophet prediction model is less than 0.1, the running time is shorter, and the trend prediction accuracy is higher, which can be practically applied to the field of intelligent operation and maintenance.

Cost-Benefit Analysis of Industrial Robot Gear Condition Monitoring

On the one hand, condition monitoring of industrial robot gears can reduce unexpected downtimes of highly automated production lines and thus save related costs. On the other hand, the implementation and operation of such systems is costly itself. In this context, we present a cost model to compare condition monitoring scenarios with preventive maintenance scenarios for the application of industrial robot gear maintenance. We parameterize this model with data from various sources and use the parameterized model to identify scenarios in which condition monitoring is economically efficient.

An improved preference-based variable neighborhood search algorithm with ar-dominance for assembly line balancing considering preventive maintenance scenarios

For assembly line balancing problem considering preventive maintenance scenarios (ALBP-PM), the production managers’ preferences should be concerned and hence the derived Pareto-optimal solution set (POS) should be advanced towards the corresponding region-of-interest. Otherwise, production managers might select a final solution far from their interests, and then obviously reducing the quality and efficiency of decision-making. Thus, a preference-based multi-objective optimization problem is formulated to simultaneously minimize the cycle times under different scenarios and total task adjustments among scenarios. An improved variable neighborhood search algorithm (IVNS) with novel ar-dominance and three modifications is proposed to obtain a preferred POS. Specifically, ar-dominance is integrated into it to guide the advancement direction of the preferred POS. Enhanced neighborhood structures based on critical stations are proposed to generate better-quality neighbor solutions whose cycle times are mathematically proven smaller. On this basis, a local search strategy is designed to randomly select a neighbor solution to guarantee quality and decrease the computational cost. A problem-specific adaptive restart mechanism is developed to escape from the local optimum. Computational results suggest that the IVNS obtains a better preferred POS than other eight state-of-the-art algorithms in terms of convergence, distribution and closeness to the preferences, and furthermore, incorporating preferences into the ALBP-PM helps production managers make final decisions in a more precise and faster way.

An LCA methodolody for assessing the environmental impacts of building components before and after refurbishment

Refurbishment is one of the most important measures for reducing the environmental impacts of the construction sector in the near future. According to the life cycle assessment (LCA) methodology for buildings, the environmental impacts of refurbishment measures should be assessed within the whole life cycle of the building and reflected in separate modules. However, in practice, refurbishment is often treated as the beginning of a new building life cycle. This leads to difficulties in correctly assessing the environmental impacts for the components that are reused or recycled after the refurbishment. The division of a building's life cycle into two separate life cycles indicates that the environmental impacts must be divided between the life cycle before and the life cycle after the refurbishment for a correct assessment of the environmental impacts and a calculation of the residual value.We propose a newly developed methodology for calculating the environmental impacts and the residual value of refurbishment measures that also involves a division between life cycles. The new methodology is a combination of already exiting methodologies that are innovatively combined and consists of four sequential steps. In the first step, the input, output and reuse flows between the life cycles before and after the refurbishment are defined. In the second step, the environmental impacts are assessed using the chosen allocation approach (i.e., the cut-off, cut-off with module D, avoided-burden, 50:50 and the product environmental footprint (PEF)). In the third step, a maintenance scenario is implemented according to the selected reference-service-life (RSL) database. In the fourth step, the residual value is estimated. The methodology was tested on selected building components. A sensitivity analysis for different allocation approaches and RSL databases was performed to show how the choice of these parameters can influence the results. The differences between the selected allocation approaches emerge if materials with recycled content are used or if the materials are being recycled or reused at the end of their life cycle. The developed methodology reliably estimates the environmental impacts as well as the residual value of the life cycle before and after the refurbishment. We expect that this research will stimulate practitioners to avoid the negligence of previous environmental flows, bringing scientific consistency to future assessments of refurbishment measures.