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Maintenance of heritage buildings in Egypt is essential for extending their life and preserving them in a good condition for current use and future generations. The purpose of this paper aims to study the significant parameters to be taken into consideration in the decision-making process for maintenance of heritage buildings.

This research identifies and analyzes the parameters affecting maintenance decision-making process using relative importance index method. Sixty-three parameters were collected from the literature and were categorized into six groups. The feedback of 15 experts who represent owners' representatives and consultants in the field of maintenance and preservation of heritage buildings was obtained through a questionnaire survey and analyses were conducted on the results.

According to the highest values of the relative importance index method, the top 10 influencing parameters are determined. A comparison between feedback of the two groups of experts is conducted. Statistical analysis is carried out to test the parameters, revealing a strong correlation between structural and geotechnical groups of parameters.

Parameters affecting decision-making for maintenance of heritage buildings were identified, influencing parameters can be used to compare between heritage buildings in greater need of maintenance than others.

Planning of manufacturing and maintenance activities together, creating a balance between maintenance and production parameters and developments on maintenance will prevent technical and economic losses and increase production efficiency. Optimizing production and maintenance scheduling enable us to see how maintenance parameters (β, η, t_p, t_r, a_[o]) will affect production performance, completion time (Ec.) and maximum machine availability, and shows which maintenance parameters minimum completion time (Ec_min) will be provided. Difference between Ec_min and maximum completion time (Ec_mak) effect to the production costs will be calculated. The purpose of this paper is to show how a genetic algorithm (GA) procedure is successfully applied to the integrated optimization model to determine optimum production policies based maintenance parameters.

GA is used for optimization and a computer program is prepared to make optimization for integrated preventive maintenance and production planning (IPMPP). Using the program, experimental studies are carried out with different number of jobs be done, to optimize production policy taking maintenance parameters into account.

Numerous experiments have been conducted with developed GA computer program and see maintenance parameters (β, η, t_p, t_r, a_[o]) effect to the production performance, Ec and maximum machine availability and at which maintenance parameters Ec_min will be provided, and also operating cost saving and maintenance parameters how affect Ec subjects are examined. Due to optimal preventive maintenance (PM) and production sequence arrangement and application of PM provided by GA, Ec_min is greatly decreased.

In this paper, GA procedure is successfully applied to the integrated optimization model to determine optimum production policies based maintenance parameters.

The purpose of this paper is to review maintenance practices, tools and parameters for marine mechanical systems that can be classified as plant, machinery and equipment (PME). It provides an insight for the maintenance crew on which maintenance parameters and practices are critical for a given PME systems.

The review paper characterizes the various maintenance parameters and maintenance practices used onshore and offshore for PME and identifies the possible gaps.

A variety of maintenance techniques are being used in the marine industry such as corrective maintenance, preventive maintenance and condition-based maintenance. As marine vehicles (MV) get older, the most important maintenance parameters become maintenance costs, reliability and safety. Maintenance models that have been developed in line with marine mechanical systems have been validated using a single system, whose outcome could be different if another PME system is used for validation.

There is a limited literature on MV maintenance parameters and maintenance characterization regarding mechanical systems. The maintenance practices or strategies of marine mechanical systems should be based on maintenance parameters that suit the marine industry for a given PME.

Based on the available literature, the paper provides a variety of maintenance framework, parameters and practices for marine mechanical systems. The paper further gives an insight on what maintenance parameters, strategies and platforms are given preference in the shipping industry.

The purpose of this paper is to analyze parameters that influence direct maintenance cost (DMC) in the civil aircraft operational phase. Reducing direct maintenance cost of civil aircrafts is one of the important ways to improve economy. DMC prediction can provide decision support for the optimization of the design parameters optimization to realize the objection in decreasing the maintenance cost, and it can also improve the aircraft competitiveness.

The paper analyzes some parameters comprehensively, which influence DMC in the civil aircraft’s operational phase. Based on the analysis of the influential parameters and the characteristics of data in the period of civil aircraft’s designing period, the paper presents prediction support method based on fuzzy support vector machine (FSVM) and realizes quantitative forecast of DMC in the aircraft design phase.

The paper presents the process of DMC analysis and model in the aircraft design phase, the DMC prediction model is used in newly developed aircrafts.

The numerical examples using B737NP fleet data in the paper have proved the effectiveness of the proposed method.

The paper establishes the prediction model of civil aircraft DMC based on FSVM. The model can handle fuzzy data and small sample data which contain noise. The results prove that the method can satisfy the demand of the real data in civil aircraft designing.

Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.

To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.

The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.

The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

There is growing evidence of a knowledge gap in the association of maintenance with production activities in bottling plants. Indeed, insights into how to jointly optimise these activities are not clear. In this paper, two optimisation models, Taguchi schemes and response surface methodology are proposed.

Borrowing from the “hard” total quality management elements in optimisation and prioritisation literature, two new models were developed based on factor, level and orthogonal array selection, signal-to-noise ratio, analysis of variance and optimal parametric settings as Taguchi–ABC and Taguchi–Pareto. An additional model of response surface methodology was created with analysis on regression, main effects, residual plots and surface plots.

The Taguchi S/N ratio table ranked planned maintenance as the highest. The Taguchi–Pareto shows the optimal parametric setting as A4B4C1 (28 h of production, 30.56 shifts and 37 h of planned maintenance). Taguchi ABC reveals that the planned maintenance and number of shifts will influence the outcome of production greatly. The surface regression table reveals that the production hours worked decrease at a value of planned maintenance with a decrease in the number of shifts.

This is the first time that joint optimisation for bottling plant will be approached using Taguchi–ABC and Taguchi–Pareto. It is also the first time that response surface will be applied to optimise a unique platform of the bottling process plant.

The purpose of this paper is to define conditions under which improved availability of fleet of G-4 jet trainers is obtained, and optimization of intermediate-level maintenance through imperfect maintenance model application. This research has been conducted based on available knowledge, and experience gained by performing intermediate-level maintenance of Serbian Air Force aircrafts.

Analysis of the data collected from daily maintenance reports, and the analysis of maintenance technology and organization, was performed. Based on research results, a reliability study was performed. Implementation of imperfect maintenance with its models of maintenance policies (especially a quasi-renewal process and its treating of reliability and optimal maintenance) was proposed to define new maintenance parameters so that the greater level of availability could be achieved.

The proposed methodology can potentially be applied as a simple tool to estimate the present maintenance parameters and to quickly point out some deficiencies in the analyzed maintenance organization. Validation of this process was done by conducting a reliability case study of G-4 jet trainer fleet, and numerical computations of optimal maintenance policy.

The methodology of the availability estimation when reliability parameters were not tracked by the maintenance organization, and optimization of intermediate-level maintenance, has so far been applied on G-4 jet trainers. Moreover, it can be potentially applied to other aircraft types.

Availability estimation and proposed optimization of intermediate maintenance is based on a survey of data for three years of aircraft fleet maintenance. It enables greater operational readiness (due to a military rationale) with possible cost reduction as a consequence but not as a goal.

Wear life of mill liners is an important parameter concerning maintenance decision for mill liners. Variations in process parameters such as different ore properties due to the use of multiple ore types influence the wear life of mill liners whereas random order of processing, processing time and monetary value of different ore types leads to variation in mill profitability. The purpose of the present paper is to develop an economic decision model considering the variations in process parameters and maintenance parameters for making more cost‐effective maintenance decisions.

Correlation studies, experimental results and experience of industry experts are used for wear life modeling whereas simulation is used for maximizing mill profit to develop economic decision model. The weighting approach and simulation have been considered to emphasize the contribution of parameters such as ore value and processing time of a specific ore type to a final result.

A model for estimating lifetime of mill liners has been developed based on ore properties. The lifetime model is combined with a replacement interval model to determine the optimum replacement interval for the mill liners which considers process parameters of multiple ore types. The finding of the combined model results leads to a significant improvement in mill profit. The proposed combined model also shows that an optimum maintenance policy can not only reduce the downtime costs, but also affect the process performance, which leads to significant improvement in the savings of the ore dressing mill.

The proposed economic decision model is practically feasible and can be implemented within the ore dressing mill industries. Using the model, the cost‐effective maintenance decision can increase the profit of the organization significantly.

The novelty is that the new combined model is applicable and useful in replacement decision making for grinding mill liners, in complex environment, e.g. processing multiple ore types, different monetary value of the ore type and random order of ore processing.

Key performance indicators (KPIs) of maintenance systems serve as benchmarks to workers and organizations to compare their goals for decision-making purposes. Unfortunately, the effects of one KPI on the other are least known, restraining decisions on prioritization of KPIs. This article examines and prioritizes the KPIs of the maintenance system in a food processing industry using the novel Taguchi (T) scheme-decision-making trial and evaluation laboratory (DEMATEL) method, Taguchi–Pareto (TP) scheme–DEMATEL method and the DEMATEL method.

The causal association of maintenance process parameters (frequency of failure, downtime, MTTR, MTBF, availability and MTTF) was studied. Besides, the optimized maintenance parameters were infused into the DEMATEL method that translates the optimized values into cause and effect responses and keeping in view the result of analysis. Data collection was done from a food processing plant in Nigeria.

The results indicated that downtime and availability have the most causal effects on other criteria when DEMATEL and T-DEMATEL methods were respectively applied to the problem. Furthermore, the frequency of failure is mostly affected by other criteria in the key performance indication selection using the two methods. The combined Taguchi scheme and DEMATEL method is appropriate to optimize and establish the causal relationships of factors.

Hardly any studies have reported the joint optimization and causal relationship of maintenance system parameters. However, the current study achieves this goal using the T-DEMATEL, TP-DEMATEL and DEMATEL methods for the first time. The applied methods effectively ease decisions on prioritization of KPIs for enhancement.

This paper aims to assess the contributions of maintenance practices and extract various significant factors that influence the implementation of maintenance practices in northern Indian small and medium-sized enterprises’ (SMEs) business performance.

In the current study, 216 north Indian SMEs have been extensively surveyed to assess the contributions of different maintenance practices implementation dimensions and manufacturing performance attributes through different statistical techniques. Analysis of variance (ANOVA) was used to statistically validate the hypotheses, while Levene’s experiment and Wilk–Shapiro tests were used to confirm ANOVA’s assumptions. Finally, the discriminating validity test extracts highly successful and moderately successful organizations.

The present research aims to evaluate the contributions of different maintenance practices implementation dimensions on SMEs’ manufacturing performance attributes. The study highlights that strategic maintenance practices such as corrective maintenance (CM), general maintenance issues, preventive maintenance (PM) issues and predictive maintenance (Pd.M) initiatives have enhanced overall equipment effectiveness, overall business performance, quality, cost optimization, safety, delivery and morale in SMEs.

The study validates the capabilities of maintenance practices toward significant improvements of various implementation dimensions such as breakdown maintenance issues, general maintenance issues, PM issues, Pd.M issues, CM issues, computer maintenance management system issues, maintenance scheduling issues and total productive maintenance issues. The study reveals significant implications for SMEs for realizing significantly improved manufacturing performance through strategic maintenance initiatives.