Optimal Maintenance Schedule Research Articles

Monitoring and Improving Aircraft Maintenance Processes Using IT Systems

Aircraft maintenance is a complex, multifaceted process that greatly benefits from IT systems designed to improve supervision, record keeping, and task management. This study focuses on the role of a dedicated mobile application, integrated into the broader IT Aircraft Maintenance Support System, which supports maintenance operations for the M-346 BIELIK training aircraft. Aircraft maintenance is a highly intricate and multifaceted process that significantly benefits from advanced IT systems designed to enhance supervision, streamline record keeping, and optimize task management. This study explores the pivotal role of a dedicated mobile application integrated into the broader IT Aircraft Maintenance Support System, specifically tailored to support the maintenance operations of the M-346 BIELIK training aircraft. By focusing on the analysis of Intelligent Transportation Systems (ITSs), the research highlights how the application contributes to maintenance reliability and operational efficiency, with sustainability considerations in mind. The ITS-based approach assesses maintenance scheduling, tracking, and resource optimization, thereby enhancing the reliability of aircraft operations while reducing unnecessary resource consumption. This alignment with sustainable practices not only improves reliability characteristics and exploitation rates but also positively impacts the efficiency and effectiveness of aviation training. By accurately estimating the time requirements of specific maintenance tasks during periodic inspections, the application aids in identifying and addressing organizational bottlenecks, ultimately supporting both operational sustainability and improved task reliability across maintenance activities.

Effect of repair factors on multi-level optimization of maintenance planning for corroded pipelines considering different failure modes

The use of pipelines for transporting fluids is essential and has been growingsignificantly in the oil and gas industry. Although pipelines are one of the safestmethods to transport these materials, it is necessary to consider the risks of failures inthe design, as such incidents can cause considerable damage to the population, theenvironment, and the infrastructure. In this context, the aging of the pipeline networkhas been extensively studied, and corrosion is one of the main concerns regardingstructural integrity. Therefore, adequate maintenance planning is essential to ensure thesafety and cost-effectiveness of the sector, as effective pipeline integrity managementcan reduce expenses and maintain safety standards throughout the operational life of thepipeline. In this scenario, inspection and maintenance planning research has becomeincreasingly relevant; recent studies have utilized reliability analysis through MonteCarlo simulations to define optimal maintenance schedules for corroded pipelines,aiming to minimize operational costs associated with pipeline management. This paperinvestigates the influence of repair factors on the estimated total costs of inspectionschedules optimized for a given number of inspections, considering only one failuremode and the combination of these (small leak, burst, and rupture). For this, acombination of methodologies already validated and presented in previous works isused, in which a multilevel optimization is carried out by predefining an upper limit forthe maximum failure probability, which will act as a constraint on the optimizationproblem. The reliability analysis will be performed using Monte Carlo simulations,which are commonly used in the literature. Finally, preliminary results indicated that thefailure mode strongly influences the final cost, so the results considering only onefailure mode are expected to differ considerably.

Decision-Making Model for Life Cycle Management of Aircraft Components

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.

Generative AI for Predictive Maintenance: Predicting Equipment Failures and Optimizing Maintenance Schedules Using AI

Predictive maintenance has emerged as a transformative approach to managing equipment health, reducing unplanned downtime, and extending asset lifespan. Leveraging advancements in generative artificial intelligence (AI), this paper explores the role of AI-driven predictive maintenance in predicting equipment failures and optimizing maintenance schedules. Traditional maintenance strategies, such as reactive and preventive approaches, often lead to inefficiencies, increased operational costs, and unexpected breakdowns. Predictive maintenance, powered by AI, offers a proactive alternative that not only anticipates failures but also enhances scheduling efficiency, maximizing equipment uptime and reducing maintenance costs. Generative AI models, including techniques such as Generative Adversarial Networks (GANs) and reinforcement learning, have shown immense promise in learning complex patterns from historical data and simulating potential equipment failure scenarios. These AI-driven models can analyze vast and diverse data sources—including sensor readings, maintenance logs, environmental conditions, and historical failures—to provide accurate, real-time insights into equipment health. This paper details the architecture and functioning of generative AI models in predictive maintenance, emphasizing their role in both anomaly detection and failure prediction. A systematic comparison of reactive, preventive, and predictive maintenance is provided, underscoring the unique benefits and challenges of predictive maintenance. We discuss the types of data essential for predictive maintenance and present sample data structures used in model training and deployment. Additionally, this paper demonstrates how generative AI models predict equipment failures by identifying anomalous behaviors before they escalate, enabling preemptive actions. A failure probability model is presented to illustrate how failure risks evolve over time, alongside tables showcasing the critical data points in predictive maintenance. The paper also explores the optimization of maintenance schedules using generative AI, where models simulate and compare different maintenance timing strategies, ultimately minimizing downtime and maximizing productivity. However, we also acknowledge the current limitations of generative AI in this domain, including data privacy concerns, computational intensity, and the challenges of model interpretability for practical implementation. Looking forward, we examine future trends such as the integration of Internet of Things (IoT) devices and the emergence of more sophisticated AI models that will likely enhance predictive maintenance applications. This paper concludes by highlighting the transformative potential of generative AI for predictive maintenance, offering insights for industries seeking to innovate their maintenance practices and achieve superior operational resilience.

Integrated optimization of production and maintenance scheduling with third-party worker resource constraints in distributed parallel machines environment

Outsourcing machine maintenance to third parties has been a trend due to the increasing complex of machines and the benefits of maintenance outsourcing. However, this new phenomenon is ignored in previous studies pertaining to the integrated optimization of production and maintenance scheduling (IOPMS) problems, resulting the lack of theoretical guidance for production managers to formulate optimal scheduling schemes under this new trend. In this study, we investigate an IOPMS problem considering maintenance outsourcing in a distributed parallel machine environment, referred as IOPMSTW, which requires the use of third-party worker resources to perform preventive maintenance. Makespan and total cost are two optimization objectives. We first formulate the problem by developing a mixed integer linear programming. Then a memetic algorithm incorporating iterated greedy method (IG) is proposed to solve the IOPMSTW, in which an improved decoding method and a problem-dependent local search operator based on IG are designed to respectively ensure the feasibility of new generated individuals and improve the searching efficiency. The validity of proposed mathematical model is verified by CPLEX based on eight small instances. Based on 240 constructed instances, a set of comprehensive experiments are conducted. The results demonstrate that the local search operator improved the searching performance of the proposed algorithm by 100%. Comparison results with other well-known algorithms show that the proposed algorithm achieved the best results on more than 85% of the tested instances.

تحسين جدول صيانة المعدات الدوارة باستخدام معيار التكلفة المتوقعة الكلية ومعيار تباين التكلفة

Inspection and maintenance of rotating equipment have gained credit in petrochemicals, refineries and oil and gas industries over the past decades due to the hazard failures resulting from overpressures and fluctuated temperatures surrounding the industrial zone. Therefore, any equipment operating under the harsh operational conditions should take the cost and time of inspection and maintenance into account. Maintenance of rotating equipment is an expensive event in terms of the cost and time, which are the driving forces to improve the maintenance performance. The paper aimed at optimizing the maintenance schedule for the grouped pumps in the refinery plant using the expected cost criterion and expected cost-variance criterion. This is characterized by the application of techniques to prolong equipment life, minimize downtimes, and enhance all aspects of reliability, availability and maintainability. The results showed that the cost and time of maintenance could optimize based on the rotating equipment, which could implement according to the preventive and corrective maintenance policy. The results analysis has revealed that applying optimization of maintenance schedule could help practitioners in decision-making to estimate the optimal cost and time, and reduce planning efforts in the future. Keywords: Pumps equipment, Maintenance policy, Cost and time of maintenance.

Comparative analysis of offshore wind turbine blade maintenance: RL-based and classical strategies for sustainable approach

This study compares traditional methods like Corrective Maintenance (CM), Scheduled Maintenance (SM), and Condition-based Maintenance (CbM) with Reinforcement Learning (RL)-based offshore wind turbine (OWT) blade maintenance strategies. In order to address the dual challenge of minimizing carbon output while managing maintenance costs and operational efficiency, the study presents a mathematical model intended to estimate carbon emissions associated with OWT maintenance activities. The ability of the RL-based strategy to reduce the risk of fatigue failure in OWT blades and account for wind speed variability in maintenance schedule optimization is assessed. In order to provide a sustainable maintenance solution this strategy balances the trade-offs between economic profit and environmental effect. The findings demonstrate how RL can provide a balanced approach to maintenance that enhances both operational performance and environmental sustainability.

Failure rate analysis and maintenance plan optimization method for civil aircraft parts based on data fusion

In the face of data scarcity in the optimization of maintenance strategies for civil aircraft, traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design. This study addresses this issue by presenting a novel combined data fusion algorithm, which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information. Through a comprehensive analysis of two different maintenance projects, this study illustrates the application process of the algorithm. Building upon the analysis results, this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization. The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20% in terms of inspection efficiency ratio. This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs. The findings of this study open up novel perspectives for airlines grappling with data scarcity, offer fresh strategies for the optimization of aviation maintenance practices, and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.

Stochastic Modeling of Crack Growth and Maintenance Optimization for Metallic Components Subjected to Fatigue-Induced Failure

Abstract The degradation of metallic systems under cyclic loading is subject to significant uncertainty, which affects the reliability of residual lifetime predictions and subsequent decisions on optimum maintenance schedules. This paper focuses two main challenges in developing a reliable framework for the lifecycle management of fatigue-critical components: constructing a stochastic model that captures uncertainties in crack growth histories, and presenting a computationally efficient strategy for solving the stochastic optimization associated with maintenance scheduling. Polynomial chaos (PC) representation is proposed to propagate uncertainty in the fatigue-induced crack growth process, using a database from constant amplitude loading experiments. Additionally, an optimization strategy is implemented based on Gaussian process surrogate modeling to solve the stochastic optimization problem under maximum probability of failure constraints. The sensitivity of the optimum solution to different probability of failure thresholds is examined. The proposed framework offers a decision support tool for informed decision-making under uncertainty, aiming to mitigate fatigue failure.

Optimal Preventive Maintenance Scheduling of Multi State System. A Comparative Study of Different Meta-heuristic Algorithms

This work presents a comparative study of different meta-heuristic algorithms which are commonly used in the literature, such as Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony and Grey Wolf Optimizer. These algorithms are applied to solve a multi-state system maintenance optimization problem considering time and system availability constraints. The objective is to find the optimal inspection and maintenance intervals for each component of the system in order to minimize the preventive maintenance cost of overall the system. The performances of the algorithms are compared using different metrics, regarding the quality and stability of the results and the speed of convergence, in order to determine the most efficient algorithms.

Binary Pareto Multi-Objective Whale Optimization Algorithm for Power Plant Maintenance Scheduling

Maintenance scheduling is a critical challenge in all major industrial sectors, such as aeronautics, automotive manufacturing, and power generation plants. The goal of power plant maintenance scheduling is to establish a schedule plan for carrying out preventive maintenance shutdowns for each unit within a defined planning horizon. In this paper, a mathematical model for optimizing the maintenance schedule was established to maximize the supply, minimize fuel costs, and CO2 emissions from generating units. A developed Whale Optimization Algorithm called Binary Pareto Multi-Objective Whale Optimization Algorithm BPMOWOA is proposed and implemented to find optimal maintenance scheduling for a power plant. The proposed algorithm uses two different approaches. The first approach includes binary encoding, in which each generating unit and each time interval are represented in binary form. Then, in the second approach, a fixed-sized repository is integrated into the WOA for saving and retrieving the Pareto optimal solutions, and a grid mechanism is integrated into the WOA to maintain diversity in the population of non-dominated solutions. A case study was conducted on fourteen generating units adapted from a real-world power plant to validate the algorithm's efficiency. The results illustrate that the proposed algorithm was effective in optimizing the maintenance schedule in terms of coverage and non-dominated solution and improved the power plant performance by increasing electricity generation by 11.48% and decreasing fuel expenses by 6.56%, which are the main goals of the considered power plant. Index Terms— maintenance scheduling, multi-objective, whale optimization algorithm, power plant.

DivideMerge: A multi-vessel optimization approach for cooperative operation and maintenance scheduling in offshore wind farm

The optimization of maintenance scheduling and routing in offshore wind farms is crucial for the intelligent operation and maintenance of offshore wind energy. It involves determining the optimal timing, assigning vessels, planning routes, and efficiently completing maintenance tasks with maximum efficiency and minimal cost. Traditional branch-and-bound search methods, often used in multi-vessel cooperative maintenance scheduling models, face limitations in finding optimal solutions efficiently, while classic heuristic methods may compromise on global optimality. This paper introduces DivideMerge, a high-performance hybrid algorithm that synergizes heuristic and exact algorithms to address large-scale multi-vessel cooperative maintenance scheduling challenges in offshore wind farms. Initially, a maintenance task constraint decomposition heuristic method is utilized to break down the collaborative optimization scheduling problem into individual vessel scheduling sub-problems, ensuring adherence to the constraints of the original problem. Subsequently, the CP-SAT solver is employed to sequentially solve these sub-problems rapidly. The solutions for individual vessel scheduling are then merged to form a comprehensive solution for the multi-vessel cooperative maintenance scheduling problem. Computational results validate the effectiveness and robustness of DivideMerge, demonstrating a solution speed nearly 1000 times faster than the commercial Gurobi solver, thus offering a significant advancement in the field of offshore wind farm maintenance optimization.

Incremental analysis of load handling device deflection considering lubrication degradation for predictive maintenance

In the Industry 4.0 era, unmanned automated factories heavily rely on load handling devices (LHDs), which are indispensable but susceptible to failures. Deflection is a crucial health indicator, with excessive deflection causing issues such as tilting and reduced positioning accuracy. This underscores the necessity for reliable deflection prediction methods in LHDs, a currently under-researched area, essential for informing predictive maintenance strategies. This study introduces an incremental analysis method that addresses the impact of internal cumulative wear and lubrication degradation on deflection increase. It involves iteratively updating a multiparametric model to represent the cumulative wear process and utilizes an empirical model based on the Stribeck curve to describe lubrication degradation. To enhance accuracy, particle filtering (PF) is employed to revise friction coefficients derived from the empirical model. Experimental data from an LHD operating in a load-carrying mission validates the accurate and robust nature of this method, particularly when compared with non-friction coefficient revising counterparts. As a typical application case study for preventive maintenance, this method is utilized to simulate various scenarios of lubrication replenishment and quantify the effect of timing on LHD lifespan enhancement through lubricant replenishment. The results facilitate the optimization of maintenance schedules and offer a new approach for updating lubrication-related parameters in digital twin models of equipment-level products, in line with preventative maintenance strategies.

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

Maintenance modeling of serial-parallel multi-station manufacturing system with failure-induced damage and assembly parts

This paper studies the joint optimization of maintenance scheduling and spare part management for Serial-Parallel Multi-station Manufacturing Systems (SPMMS) with failure-induced damage, which implies the failure of one component may induce the damage of other structurally-connected components. In the proposed hybrid spare part usage policy, either the assembly part, which is a pre-assembled spare part module, or the combination of multiple unassembled single parts can be used to retore the failed station. On the basis of the introduced reliability model, the hybrid usage policy is combined into the joint analysis of spare part inventory and corrective maintenance actions. Then, an opportunistic maintenance policy with the greedy algorithm is developed to reduce the computing complexity. The numerical examples show that the cost advantage of assembly parts and the hierarchical inventory sharing of spare parts can jointly affect the cost effectiveness of the SPMMS. The proposed hybrid spare part usage policy is more cost-effective than the ordinary usage policy, the substitution policy and the component-level policy. The policy comparisons among different hierarchical sharing policies also show that both the division criterion of failure states and the usage priority of spare parts have significantly impacts on the cost effectiveness of the SPMMS.

Optimization of Network Pavement Life-Cycle Cost: A Piecewise Linearized Approach

Optimal management of pavement assets becomes important because of the escalating challenges in this field. Managing the network of paved roadways in the United States necessitates the introduction of optimization tools, such as mathematical optimization. Although several efficient optimization techniques are available, an effective approach requires a special structure of the problem. The optimization of a pavement maintenance and rehabilitation schedule poses a complex challenge, primarily because of two factors: nonlinearity and the presence of integer decision variables. Nonlinearity exists as a result of multiple sources. One source is pavement condition, commonly measured by pavement roughness. This study introduces a method that uses piecewise linearization of the pavement roughness progression function. Circular shift was used to linearize the resulting optimization model. A hypothetical city, the size of Cook County in Chicago, was used as a case study. Both agency cost and user cost were considered in the model. Agency cost was determined from consultations with professionals and online data, whereas user data on existing models. The study demonstrated that increasing the agency cost by investing one dollar per lane mile per year has a high return on investment until a certain threshold, beyond which allocating more budget does not lead to a reduction in life-cycle cost.

Reliability modeling and warranty optimization for products with self-healing under a dynamic shock environment

As modern products evolve in intelligence, self-healing mechanisms have been gaining prominence in the field of reliability. In actual operation, the performance of a product is inevitably influenced by external environments and user interactions. However, existing warranty studies often overlook these factors, leading to oversimplifications in product reliability modeling. This paper offers a comprehensive and realistic view of the product failure process by integrating the dynamic shock environment, inherent self-healing property, and post-sale consumer utilization. The constructed reliability evaluation model accounts for fatal and non-fatal shocks with a dynamic time-varying failure threshold. We introduce the concept of self-healing trigger/delay time, considering a generalized self-healing process. The approximate product reliability is derived, and its accuracy is validated using Monte Carlo simulations. Then, derived from real-world practices, we propose a novel two-stage warranty policy which combines lemon law principles with preventive maintenance. By minimizing the expected cost rate throughout the warranty service period, a preventive maintenance schedule optimization model is developed from the manufacturer's perspective. Finally, a numerical case is presented to reveal the influences of different factors on product reliability and demonstrate the existence of the optimal solution.

Coordinated risk-averse distributionally robust optimization for maintenance and generation schedules of offshore wind farm cluster

The coordinated optimization of maintenance and generation schedules of multiple offshore wind farms (OWFs) bring great benefits in reducing the maintenance and operation cost of OWF cluster. Firstly, a two-stage coordinated risk-averse distributionally robust optimization (CRDRO) model for maintenance and generation schedules of wind turbines (WTs) in OWF cluster is proposed. This model takes into account uncertainties related to the prediction errors in WTs’ maximum available active power outputs, the travel time of maintenance vessels, and the high operational cost risks associated with these uncertainties. A feasible route generating algorithm is designed to obtain all feasible maintenance combinations for WTs and the corresponding routes of maintenance vessels for the CRDRO model. The risk-averse term, which is a weighted sum of expected operation costs and GlueVaR value, is used to measure the tail risk of the worst probability distribution. To solve the two-stage CRDRO model, the risk-averse term is simplified into the form of expected value calculation, which can be further transformed into the tractable form by using the strong duality theorem. Finally, the two-stage CRDRO model is solved by the column-and-constraint generation algorithm, with the nonconvex sub-problem being addressed by the outer approximation algorithm. Case studies on an real-world cluster comprising three OWFs verify the correctness and efficiency of the proposed method, and demonstrate that the CRDRO model enhances the overall economic efficiency and reduces the high operational cost risk under extreme scenarios.

Quantitative systems pharmacology-based exploration of relevant anti-amyloid therapy challenges in clinical practice.

Addressing practical challenges in clinical practice after the recent approvals of amyloid antibodies in Alzheimer's disease (AD) will benefit more patients. However, generating these answers using clinical trials or real-world evidence is not practical, nor feasible. Here we use a Quantitative Systems Pharmacology (QSP) computational model of amyloid aggregation dynamics, well validated with clinical data on biomarkers and amyloid-related imaging abnormality-edema (ARIA-E) liability of six amyloid antibodies in clinical trials to explore various clinical practice challenges. Treatment duration to reach amyloid negativity ranges from 12 to 44, 16 to 40, and 6 to 20 months for lecanemab, aducanumab, and donanemab, respectively, for baseline central amyloid values between 50 and 200 Centiloids (CL). Changes in plasma cerebrospinal fluid Aβ42 and the plasma Aβ42/ Aβ40 ratio-fluid biomarkers to detect central amyloid negativity-is greater for lecanemab than for aducanumab and donanemab, indicating that these fluid amyloid biomarkers are only suitable for lecanemab. After reaching amyloid negativity an optimal maintenance schedule consists of a 24-month, 48-month and 64-month interval for 10mg/kg (mpk) lecanemab, 10mpk aducanumab, and 20mpk donanemab, respectively, to keep central amyloid negative for 10 years. Cumulative ARIA-E liability could be reduced to almost half by introducing a drug holiday in the first months. For patients experiencing ARIA-E, restarting treatment with a conservative titration strategy resulted in an additional delay ranging between 3 and 4 months (donanemab), 5 months (lecanemab), and up to 7 months (aducanumab) for reaching amyloid negativity, depending upon the timing of the incident. Clinical trial designs for Down syndrome patients suggested the same rank order for central amyloid reduction, but higher ARIA-E liability especially for donanemab, which can be significantly mitigated by adopting a longer titration period. This QSP platform could support clinical practice challenges to optimize real-world treatment paradigms for new and existing amyloid drugs.

Enhancing Project Performance: Particle Swarm Optimization for Optimal Budget Allocation and Maintenance Scheduling

This study aims to help project-oriented organizations manage their portfolios and arrive at an optimum budget allocation. In this study, the budget allocation process in a portfolio of projects is mathematically formulated. The interactions between the projects as well as the operational and budget constraints are considered. Then, the optimal budget allocation is determined using the particle swarm optimization algorithm. To evaluate the performance of the proposed approach, it is implemented on a portfolio of maintenance projects. In this article, we will explore the application of PSO in portfolio management and how it can help investors make better investment decisions. According to the results, the operational constraints and budget restrictions will significantly affect the optimum budget allocation solution and consequently the revenue of a portfolio. Lack of access to detailed information about the maintenance schedule of dam projects might be a limitation for future application of the proposed approach. It is believed that the proposed approach provides a powerful and efficient tool of optimal budget allocation in portfolio of projects. This research addresses several shortcomings of previous studies for optimal budget allocation.