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This paper aims to revisit the preventive maintenance scheduling literature. The problem to be solved is the simultaneous scheduling of resource‐constrained preventive maintenance and operations. In particular, the expression that defines the period‐dependent cost function for a preventive maintenance scheduling activity is redefined. A case study is presented from the shipping industry.

In this paper a mathematical theory of differential calculus known as three‐dimensional wave equation is applied. The methodology involves transforming the preventive maintenance cost function that is expressed in terms of several variables into a more precise framework. The motivation for the work is the need to measure the total preventive maintenance scheduling cost more precisely than with the use of the existing linear cost structure.

In this paper the findings from the analysis carried out found evidence that validates the claim of the feasibility of analyzing preventive maintenance cost using the approach proposed.

The paper shows that, in practice, maintenance managers strive to reduce the cost of preventive maintenance activities in order to achieve low cost production of goods. This would encourage a high patronage of customers and prevent decisions being made on wrong data. The approach presented here aims at correcting this weakness by revealing a more precise and reliable method of preventive maintenance scheduling cost computation. This is a scientific tool that should be of immense benefit to maintenance planners, particularly those actively engaged in scheduling functions.

The work in this paper is new, since a novel framework is presented in a way that has not been documented earlier.

The purpose of this paper is to solve the maintenance management problems of generating units under the reliability criterion.

The problem has been formulated as a combinatorial optimization task, with explicit and simultaneous treatment of multiple objectives: maximization of reliability, minimization of fuel costs and minimization of constraint violations. This paper formulates a general generator maintenance management (GMM) problem using a reliability criterion and a novel bio‐inspired search technique, namely, artificial bee colony (ABC) algorithm is applied to determine the optimal generator maintenance schedule.

A novel meta‐heuristic search technique based algorithm has been developed to determine the optimal maintenance schedule of generating units to improve the system reliability.

The contribution of the paper is that an efficient bio‐inspired algorithm based solution technique has been developed to solve a very important problem for a power utility, i.e. the economical and reliable operation of a power system.

This paper describes a method developed to schedule the preventive maintenance tasks in separate and linked cogeneration plants while satisfying the maintenance and production constraints.

The proposed methodology is based on a mixed integer programming model which finds the maximum number of available power and desalting units in separate and linked cogeneration plants. To verify that the model can be implemented for a real system, a case study of scheduling the preventive maintenance tasks of a cogeneration plant in Kuwait is illustrated.

An efficient solution can be achieved for scheduling the preventive maintenance tasks and production in cogeneration plants.

The paper offers a practical model that can be used to schedule preventive maintenance for expensive equipment in cogeneration plans.

The model presented is an effective decision tool that optimises the solution of the maintenance scheduling problem for cogeneration plants under maintenance and production constraints.

The simultaneous scheduling of resource‐constrained maintenance and operations is addressed in this work. The purpose of the paper is to capture the uncertainty in the development of a model that schedules both preventive maintenance and operational activities. Fuzzy logic is employed to transform the human expertise into IF‐THEN rules.

The approach has the advantage of revealing semantic uncertainty with the associated non‐specifying measures. The methodology applied tracks the error values in terms of results in linguistic variable.

The results obtained indicate the feasibility of tracking the uncertain measures in the model discussed. Thus, the study may be applicable to both production system and transportation organizations that are engaged in both maintenance and operational activities.

The research has serious implication in terms of the ability to monitor the imprecision that were introduced in the previous models. This obviously provides a more reliable framework for researchers and practitioners interested in maintenance scheduling activities.

The paper is new in that it demonstrates the application of fuzzy logic in a form that was never documented.

Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems.

The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem.

The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems.

As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues.

The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS.

As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique.

As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

The purpose of this paper is to solve the realistic problem of source maintenance scheduling (SMS) based on reliability criterion. A novel effective optimization technique is proposed to solve the problem at hand.

The problem has been formulated as a combinatorial optimization task, with the goal of maximizing reliability by minimizing the sum of squares of the reserve loads while satisfying unit and system constraints. This paper employs a nature inspired algorithm known as Teaching Learning Based Optimization (TLBO) for solving the SMS problem based on reliability.

The results reveal that optimal maintenance schedules of generating units has been obtained using TLBO algorithm with minimized values of sum of squares of reserve loads while satisfying system and operational constraints. It is also found that the inclusion of resource constraints (RC) in the model have significant effects on the objective function value which provides a deep insight of the proposed methodology.

The contribution of this paper is that an efficient nature inspired algorithm has been applied to solve source maintenance scheduling problem in viewpoint of the planning for future system capacity expansion. The incorporation of exclusion and RC in the model makes the analysis about the impact of SMS on the system reliability more reasonable.

The purpose of this paper is to describe a method that has been set up to schedule preventive maintenance (PM) tasks for power and water plants with all constraints such as production and maintenance.

The proposed methodology relies on the zero-one integer programming model that finds the maximum number of power and water units available in separate generating units. To verify this, the model was implemented and tested as a case study in Kuwait for the Cogeneration Station.

An effective solution can be achieved for scheduling the PM tasks and production at the power and water cogeneration plant.

The proposed model offers a practical method to schedule PM of power and water units, which are expensive equipment.

This proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for power and water units in a cogeneration plant, effectively and complies with all constraints.

Turnaround maintenance (TAM) is a planned stoppage of production for conducting a comprehensive maintenance of equipment or plant with the purpose of improving plant availability and performance. The purpose of this paper is to investigate trends in the operation and management of TAM, as reported in the literature, and identify gaps, in the context of a system approach that views a plant as part of a network of a supply chain.

This literature review is based on over 80 subject-relevant papers and uses content analysis. The literature subjects are classified into several managerial areas that include organization, planning, scope and risk analysis, execution, performance measurement and learning. The gap in the literature is identified in light of the proposed system view for TAM.

The system view of TAM opens new opportunities for new research areas for improving the operation and management of TAM. These areas include optimizing TAM scheduling and developing methods for managing risks along the entire business supply chain. In addition, new approaches for collaboration, sharing knowledge, best practices and expertise within the supply chain become necessary for effective TAM planning and control.

This paper reviews the literature and provides a new classification of TAM. It adopts the system view for TAM that has brought new insights in the operation and management of TAM. New trends for research in the area of TAM are identified.

The purpose of this paper is to work on an analytical approach to test sensitivity of a maintenance‐scheduling model. Any model without sensitivity analysis is a “paper work” without advancing for wider applications. Thus, the simulation of simultaneous scheduling of maintenance and operation in a resource‐constrained environment is very important in quality problem and especially in maintenance.

The paper uses an existing model and presents a sensitivity analysis by utilising an optimal initial starting transportation tableau. This is used as input into the Gantt charting model employed in the traditional production scheduling system. The degree of responsiveness of the model parameters is tested.

The paper concludes that some of these parameters and variables are sensitive to changes in values while others are not.

The maintenance engineering community is exposed to various optimal models in the resource‐constraint‐based operational and maintenance arena. However, the models do lack the sensitivity analysis where the present authors have worked. The work seems significant since the parameters have the boundary values so the user knows where he can apply the model after considering the constraints therein.

The underlying quest for testing the sensitivity of the model parameters of a maintenance scheduling model in a multi‐variable operation and maintenance environment with resource constraints is a novel approach. An optimal solution has to be tested for robustness, considering the complexity of the variables and criteria. The objective to test the model parameters is a rather new approach in maintenance engineering discipline. The work hopefully opens a wide gate of research opportunity for members of the maintenance scheduling community.