Cost Estimation Model Research Articles

An artificial neural network (ANN) approach for early cost estimation of concrete bridge systems in developing countries: the case of Sri Lanka

PurposeThe Government’s investment in infrastructure projects is considerably high, especially in bridge construction projects. Government authorities must establish an initial forecasted budget to have transparency in transactions. Early cost estimating is challenging for Quantity Surveyors due to incomplete project details at the initial stage and the unavailability of standard cost estimating techniques for bridge projects. To mitigate the difficulties in the traditional preliminary cost estimating methods, there is a requirement to develop a new initial cost estimating model which is accurate, user friendly and straightforward. The research was carried out in Sri Lanka, and this paper aims to develop the artificial neural network (ANN) model for an early cost estimate of concrete bridge systems.Design/methodology/approachThe construction cost data of 30 concrete bridge projects which are in Sri Lanka constructed within the past ten years were trained and tested to develop an ANN cost model. Backpropagation technique was used to identify the number of hidden layers, iteration and momentum for optimum neural network architectures.FindingsAn ANN cost model was developed, furnishing the best result since it succeeded with around 90% validation accuracy. It created a cost estimation model for the public sector as an accurate, heuristic, flexible and efficient technique.Originality/valueThe research contributes to the current body of knowledge by providing the most accurate early-stage cost estimate for the concrete bridge systems in Sri Lanka. In addition, the research findings would be helpful for stakeholders and policymakers to propose policy recommendations that positively influence the prediction of the most accurate cost estimate for concrete bridge construction projects in Sri Lanka and other developing countries.

Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels

The maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alternative fuels such as natural gas and propane has fostered the natural gas and propane powertrain systems as well as electrification options for heavy- and medium-duty vehicles. A critical barrier to adopting alternative fuel vehicles has been the lack of knowledge on comparative vehicle maintenance/repair costs with conventional diesel. Moreover, the region of operation, the type of vehicle operation, and seasonal temperature changes also affect the duty cycle which impacts the maintenance and repair costs. This study focuses on estimating the cost-per-mile for heavy-duty vehicles using machine learning models such as random forest, xgboost, neural networks, and a super-learner model. The super-learner model achieved an error as low as 0.0068 $/mile for mean absolute error and 0.0086 $/mile for root mean square error with a coefficient of determination/R-Squared of 97.28%. Specifically, the paper investigates the data collected from the maintenance and repair costs associated with delivery trucks using diesel and natural gas fuels. Since the availability of data is the major constraint, we leveraged the data collected by West Virginia University and the partnership with fleet companies. This allows for additional information related to maintenance costs and fleet-specific maintenance practices of alternative fuel vehicles. This study promotes clean fuel technologies and enables fleet management companies to adopt alternative fuel vehicles in case of similar or lower cost of maintenance compared to diesel vehicles resulting in reduced emissions and total cost of ownership.

Minimizing Cost Overrun in Rail Projects through 5D-BIM: A Systematic Literature Review

Mega projects delivering rail infrastructure are constantly seeking cost-effective and efficient technologies to sustain the growing population. Building information modeling (BIM) and BIM for cost management (5D-BIM) have shown great potential in the building industry and have been adopted widely. However, 5D-BIM implementation in rail infrastructure is still in its infancy. This paper presents a systematic literature review of 380 publications related to cost overrun, cost management and 5D-BIM for rail infrastructure, including rail projects. The review identified knowledge gaps and synthesized existing research on cost overrun in rail projects, cost estimation models, and the current use of 5D-BIM. The review revealed that there is no current study integrating 5D-BIM into the rail project lifecycle. This paper highlights the importance of integrating 5D-BIM systematically in the rail project life cycle to avoid/minimize cost overrun. The review provides researchers and practitioners with crucial information for deploying 5D-BIM to minimize cost overruns in rail projects.

Data-driven agile software cost estimation models for DHS and DoD

Problem:Since the Manifesto for Agile Software Development, the cost analysis community has struggled to find the most appropriate size measures for developing accurate agile software development cost estimates at an early phase to establish baseline budgets and schedules, and for the selection of competitive bidders. At this time, common agile sizing measures such as Story Points are not practical since these are reported months after contract award in the Department of Homeland Security (DHS) and Department of Defense (DoD). The problem is compounded with the lack of data to build estimation models for informed decisions. Aims:The primary objective is to investigate how well two new size measures (Functional Story and Issues) accurately relate to total effort, and how these compare and rank against four popular software size measures (Story, Story Point, Unadjusted Function Points, and Simple Function Points). The second objective is to rank the six sizing measures based on how well each estimate software development effort at an early phase or after contract award. Method:The experimental framework relied on an analysis of variance and goodness-of-fit tests to examine and compare the accuracy of effort estimation models using six competing size measures. The analysis is based on data from 17 agile projects implemented between 2014 to 2021. Results:Our two new size measures (Functional Story, Issues) proved to be good predicters of total software development effort. Functional Story is better at predicting total effort at early phase than Function Points and Story. Functional Story and Function Points are better at predicting total effort after contract award than Story Point and Issues. Conclusion:The DHS and DoD cost community can choose one or more of these estimation models to evaluate agile software development cost proposals or track agile developer’s progress after contract award.

Estimating the Cost of Strauss Pile Retaining Wall Works Using the Cost Significant Method

Cost estimation is an important element in a project. In the early stages of a project, information for estimates tends to be less accurate. Therefore, a cost estimation model is needed that can explain most of the projects based on the lack of information. The estimation method used is the Cost Significant Model. The aim is to determine the accuracy of the Strauss Mast Retaining Wall Work cost estimation model against the actual project cost. Data was collected using the sampling method, and 5 (five) similar project packages were obtained from 2019-2021. Produce 2 (two) Significant Cost Items that have cumulative costs (80%), namely Strauss Pile Work (X3) of 32% and Pile Work (X4) of 48%. Then it is analyzed to produce a regression equation formula using the SPSS application, and the equation is Y = 5696.59+5.270 X3-1,389X4 with a Cost Model Factor of 1.11. Estimated accuracy ranges from -7.3% to +8.4%. With an average of +0%. The accuracy rate is in class 1 of the International AACE classification, which has a lower limit of -3% to -10% and an upper limit of +3% to 15%. The estimation model is feasible to use to check the bid estimate.

Preliminary Cost Estimation of Highway Projects Using Statistical Learning Methods

Setting up workable budgets symbolizes the competence of state highway agencies (SHAs) in fulfilling their responsibilities, and unreliable cost estimates can cause economic and political complications. The unclear scope definition and scarcity of project information available at early stages make it hard to generate reliable preliminary estimates. Hence, based on the 1,249 projects retrieved from the Florida Department of Transportation (FDOT) database, this research aimed to develop a cost estimation model using statistical learning methods for SHAs to forecast preliminary costs during the early stages of a transportation project to fulfill different cost control and managerial functions. However, the currently used methods have serious limitations. This study introduced alternative statistical learning approaches to the currently most used methods: least absolute shrinkage and selection operator (LASSO) and general regression neural network (GRNN). LASSO regression, for instance, has proved in other areas of science to be remarkably better in terms of variable selection, interpretability, and numerical stability. In addition, this study also accounted for economic factors in model development because economic conditions are influential on highway construction costs but have received limited attention. Using the same dataset, LASSO and GRNN models were developed, and then their performances were evaluated based on a set of criteria, e.g., the mean absolute error and mean absolute percentage error. In comparison to the current practice with state DOTs, this research contributes to the body of knowledge by introducing a series of objective modeling approaches that can prevent human errors, requiring no substantial experience in preliminary estimating. Besides the introduction of statistical learning methods, this study took economic indicators into account when developing the models because they are important factors but have been ignored in previous studies. In addition, these statistical learning methods can produce reliable estimates in a much faster and more consistent fashion, which is critical, particularly considering the massive workload faced by most SHAs and the allowable time to make a preliminary estimate.

Improving robustness of case-based reasoning for early-stage construction cost estimation

In the long-term use of the Case-based reasoning (CBR) model for early-stage construction cost estimation, a typical issue is the unstable knowledge structure when the actual data distribution does not satisfy the assumed distribution. This study combines Modal Linear Regression (MODLR) with CBR, for comparison with the conventional CBR models using genetic algorithm (GA) and ordinary least squares (OLS), tested by simulated data and a case study of 1610 apartment buildings. The results show the variance of attribute weight in MODLR-CBR is far less than others, validating its superior knowledge stability in dealing with changes in the case-base. This study not only bridges the gap in the robustness of CBR models, but also prepares construction cost practitioners tackle the massive growth in the volume of the cost data. The results can be further referenced to the area of multidimensional optimization in CBR.

Software Development Effort Estimation Using Relational Database and Optimized Learning Mechanism

Accurately estimating the cost of software development is crucial for effective project planning and resource allocation. However, traditional cost estimation methods rely heavily on expert judgment and historical data, which can be time consuming and prone to errors. This study suggests a learning-based cost estimation model that leverages relational databases to improve accuracy. The proposed approach estimates project cost based on the effort required to complete software development, which is a key driver of the project cost. The proposed model is designed to address the challenges posed by the variability in open-source development, including variable team sizes, working hours, and expertise. The study collects and pre-processes data from open-source platforms and selects cost drivers and metrics based on logical rules and SQL queries. Moreover, we propose an optimized Artificial Neural Network (ANN) with augmented topology to automate the selection of neuron units, layers, and adjustment of learnable parameters according to the input variables. The proposed model is evaluated on a 100 open-source software repositories dataset and demonstrates its effectiveness in accurately estimating development cost. The system is implemented using Python and evaluated using performance parameters such as MSE, RMSE, MAE, and MMRE. Results indicate that our proposed model offers a more accurate and efficient approach to software cost estimation, especially for freelancers and outsourcing firms. The proposed model has the potential to save time and resources and improve the reliability and accuracy of software cost estimation.

Cost Model for CubeSat Projects in Mexico

This work focused on designing a cost estimation model in the area of space engineering for the development of nanosatellite projects in Mexico. See employed the use of methodologies such as Hall and Jenkins, software engineering cost estimating methodology, as well as project management methodology. Similarly, the COSYSMO methodology and parametric cost methodology were incorporated to guide this research towards the space sector. The estimation results are based on satellite engineering equations and forecasts of the inflation factor. Finally, a graphical user interface named SACANI was obtained. Allowing the estimating a nanosatellite project by ”integration of components” or by ”research and development”. The first asks the user for the components by subsystem through drop-down lists, giving a breakdown of the estimated costs for the CubeSat, development phases and ground station, according to the data provided by the interested party. The second model is more robust because it requests the type of project, the category, the amount of CubeSat required, the year to estimate the cost, the mass of the payload, among other data. Similarly, both models yield a previous result of the estimate made according to the data entered by the project estimator.

Brazil’s offshore wind cost potential and supply curve

In face of the growing interest in offshore wind in Brazil, the objective of this paper is to assess the Brazilian offshore wind cost potential. A methodological approach is proposed, which combines a spatial suitability analysis with a cost estimation model, building on existing research. First, the available area for development is mapped through the application of spatial constraints. Then, the resulting areas are classified in terms of their Levelized Cost of Energy (LCOE), generating a cost potential map and a cost-supply curve. Results show an available potential concentrated in three hotspots along the Northeast, Southeast, and South coasts, reaching 875.7 GW of installable capacity, an annual yield of 489.3 TWh and with capacity factors ranging from 32% to 65%. From total installable capacity, 294.7 GW require fixed-bottom foundations, while 581.0 GW use floating technology. Estimated LCOE range from 86.5 to 243.4 USD/MWh, with an average of 136.3 USD/MWh. The proposed cost estimation model proved to be an accurate tool and the methodological framework is replicable and adjustable to future cost reductions. Moreover, this paper shows that Brazil is a promising market for offshore wind energy, with a large untapped potential that could be deployed at internationally competitive costs.

Early Highway Construction Cost Estimation: Selection of Key Cost Drivers

Cost estimates in the early stages of project development are essential for making the right decisions, but they are a huge challenge and risk for owners and potential contractors due to limited information about the characteristics of a future highway project. Whereas previous studies were mainly focused on achieving the highest possible estimation accuracy, this paper aims to propose cost-estimation models that can provide satisfactory accuracy with the least possible effort and to compare the perspectives of owners and contractors as the key stakeholders on projects. To determine cost drivers (CDs) that have a high influence on highway-construction costs and require low effort for their establishment, a questionnaire survey was conducted. Based on the key stakeholders’ perceptions and collected data set, cost-estimation models were developed using multiple-regression analysis, artificial neural networks, and XGBoost. The results show that reasonable cost-estimation accuracy can be achieved with relatively low effort for three CDs for the owners’ perspective and five CDs for the contractors’ perspective. Additional inclusion of input CDs in models does not necessarily imply an increase in accuracy. Also, the questionnaire results show that owners are more concerned about environmental issues, whereas contractors are more concerned about the possible changes in resource prices (especially after recent high increases caused by COVID-19 and the Russia–Ukraine war). These findings can help owners and potential contractors in intelligent decision-making in the early stages of future highway-construction projects.

Pemodelan Estimasi Biaya Elemen Struktur Bangunan Rumah Toko Dengan Variasi Lebar Bentang

The development of construction will affect work methods in construction, design and materials. Changes in design, materials and work methods in the construction of a construction project will affect the costs that must be incurred by the owner and the time required to construct a building. Conceptual cost estimation is needed as a reference or guide in determining the policies used to determine the amount of investment that must be implemented. Planning is the initial stage in construction development activities. Effective and efficient project planning is needed in carrying out a project. Along with the need for efficiency, the cost estimation model at the early stages of project planning must be accurate so that the results can be accounted for. This study aims to obtain an estimate of the structural cost of a shop house building with several span variations. Variations in the width of the shop house are 5 m, 6.67 m and 8 m. The research results obtained that the cost of the structure on the 8 m span is Rp. 2,352,485,387, the 8 m span is the span that has the largest cost compared to other spans. The difference between the span of 8 m and the span of 6.67 m and 5 m is Rp. 93,480,110 and Rp. 13,740,263, respectively. Based on these results it was concluded that the larger the span, the greater the costs required. This is influenced because there are differences in the dimensions and specifications of the structures used in each span.

Cost Estimation Model Based on Building Information Modeling and Virtual Reality for Customizing Presold Homes

Presold homes refer to houses that are sold before construction has begun or while the construction is still underway. To facilitate the selection and purchase of houses by clients, construction companies usually use sample houses, models, or videos to show the appearance of new houses being constructed. Clients have numerous opportunities to participate in the spatial planning and spatial allocation of the houses because these houses have not yet been constructed, and clients can direct the style and materials used for interior partition walls and furnishings. However, when discussing housing styles with their clients, construction companies often depend on their imagination to create three-dimensional images depicting the interior layout of houses. Construction companies can neither immediately present new styles when clients request changes to partition walls or styles nor estimate the costs of the changes. Consequently, clients cannot immediately decide on such changes, which then leads to poor communication between the two parties. Accordingly, this study designed a system that integrates building information modeling (BIM) and virtual reality (VR). Through this system, clients can change and immediately view house styles in immersive VR and decide on whether the changes meet their demands. Furthermore, the designed system is linked to a unit price database for materials. When clients make alterations in the system, it automatically calculates the costs of the changes and then helps the client determine whether the changes are within their budgets. The results of this study indicate that the designed system can accurately and rapidly achieve cost estimation. Furthermore, the proposed system ensures consistency between BIM and virtual environment information, thereby promoting the application of BIM in the stages of a building life cycle subsequent to building design.

The South Korean case of deploying rural broadband via fiber networks by implementing universal service obligation and public-private partnership based project

Despite broadband being essential infrastructure for conducting basic socio-economic activities and reducing inequality and the digital divide, expanding broadband coverage in rural areas remains a significant challenge in many countries due to high deployment costs. South Korea, a developed country in terms of fixed broadband penetration, implemented two policies for rural broadband via fiber access networks simultaneously during the COVID-19 pandemic. One is the universal broadband service at 100 Mbps speed introduced in 2020, and the other is the rural broadband project in terms of a public-private partnership (PPP) that proceeded from August 2020 to 2022. While both policies contribute to reducing the broadband gap between the urban and the rural in the country, it is still important to find the optimal cost-effective combination of the plural rural policies as much as possible from the cost perspective. Accordingly, this study proposes an investment cost estimation model for deploying rural broadband via fiber networks in South Korea and evaluates whether implementing two rural broadband policies in the country is cost-effective. The proposed model is designed according to the bottom-up approach, which involves building an efficient network with the latest technologies; thus, it gives a more accurate and reliable estimation of the investment cost of building rural broadband in the South Korean territory. The significant findings of this study are twofold. First, the PPP-based project could make the participating internet service providers (ISPs) deploy broadband at a cost-effective level, similar or lower to the universal service, and then recover the investment cost per building within a reasonable period. Thus, the result implies that governments need to consider the PPP-based project as an effective measure to deploy rural broadband, in addition to the introduction of universal service. Implementing multiple broadband policies to expand rural broadband coverage could be a reasonable and practical approach to improving broadband availability. Second, the governments still needs to make efforts to solve the complicated difficulties in rural areas in South Korea where rural-related broadband policies are still challenging to implement. This study confirmed that in some regions, the PPP-based project was still inferior to universal service in terms of average investment cost. In contrast, universal service had significant constraints to providing broadband because of the end-users cost burden, resulting in low broadband adoption. Therefore, the government needs to consider some measures to increase broadband availability and broadband adoption, such as widening the rural areas under the jurisdiction of participating ISPs, decreasing the proportion of participating ISPs bearing the investment cost of deploying broadband, and improving the cost burden of the end-users of universal service in some limited areas.

Estimating Maintenance Costs of Sewer System

Maintenance costs of all types of buildings are most often ignored since they are incurred in the future. Potential investors are interested in capital costs—construction costs—while maintenance costs are considered as unimportant in the life cycle of a building. If there were a larger number of maintenance cost estimation models, it would be possible to estimate these costs and present them to potential investors more easily, thus making it easier and more effective to apply life cycle cost methods. A study on the characteristics and costs of the maintenance of sewer systems in the Republic of Croatia was conducted, wherein questionnaires were sent to companies operating public sewer systems. The data requested in the questionnaires were general data on enterprises, maintenance, data on sewer systems, quantities of sewer discharge, sewer pipes and data on maintenance costs of sewer systems. It was established that it is possible to use linear regression when creating a model for estimating the maintenance costs of a sewer system.

Lattice Enumeration with Discrete Pruning: Improvements, Cost Estimation and Optimal Parameters

Lattice enumeration is a linear-space algorithm for solving the shortest lattice vector problem (SVP). Extreme pruning is a practical technique for accelerating lattice enumeration, which has a mature theoretical analysis and practical implementation. However, these works have yet to be applied to discrete pruning. In this paper, we improve the discrete pruned enumeration (DP enumeration) and provide a solution to the problem proposed by Léo Ducas and Damien Stehlé regarding the cost estimation of discrete pruning. We first rectify the randomness assumption to more precisely describe the lattice point distribution of DP enumeration. Then, we propose a series of improvements, including a new polynomial-time binary search algorithm for cell enumeration radius, a refined cell-decoding algorithm and a rerandomization and reprocessing strategy, all aiming to lift the efficiency and build a more precise cost-estimation model for DP enumeration. Based on these theoretical and practical improvements, we build a precise cost-estimation model for DP enumeration by simulation, which has good accuracy in experiments. This DP simulator enables us to propose an optimization method of calculating the optimal parameters of DP enumeration to minimize the running time. The experimental results and asymptotic analysis both show that the discrete pruning method could outperform extreme pruning, which means that our optimized DP enumeration might become the most efficient polynomial-space SVP solver to date. An open-source implementation of DP enumeration with its simulator is also provided.

Cost and Material Quantities Prediction Models for the Construction of Underground Metro Stations

This paper addresses the gap in the scientific literature regarding construction cost estimates for the construction of underground metro stations. It provides preliminary cost estimation models using linear regression for use by the Greek underground metro public transport authority for planning future extensions to the Athens and Thessaloniki networks. At the same time, it contributes to the body of knowledge by proposing material quantity prediction models and presents a two-stage preliminary cost estimation model for the construction of civil engineering works of underground metro stations. Stage one uses the construction cost budgets of six metro stations in Greece to develop a multilinear regression equation for the prediction of the overall cost for construction of civil engineering works; stage two provides estimates of material quantities using linear regression, key quantity ratios, and artificial neural networks. The data analyzed are from the prior measurements of quantities for the construction of the Chaidari to Piraeus extension of the Athens Metro Line 3. After comparing the actual values of costs and quantities with the corresponding predictions, acceptable discrepancies are observed. All models provide estimates within ±25% discrepancies, which are acceptable at the conceptual planning phase in order to initiate project funding quests.

An Assessment of the Social Costs of Road Traffic Crashes in Cameroon

This study estimated the social costs of road traffic crashes (RTCs) in Cameroon, motivated by a lack of empirical evidence for economic loss and social suffering associated with RTCs menace in developing countries particularly Sub-Sahara Africa. A model for estimation of cost based on a combination of valuation methods was developed following international guidelines, and can be adapted for other developing countries similar to Cameroon’s context. Five cost components were estimated namely: production loss; human costs; medical costs; property damage costs and administrative costs. Data from the field, secondary databases and transfer values were used together with adjustments for under-reporting of road traffic crash data that is prevalent particularly in developing countries. Total social cost of RTCs in Cameroon in 2018 was USD 3.6 Billion and is equivalent to 3.8% of GDP in 2018. This estimate is way above RTCs cost estimates obtained by studies in Sub-Sahara Africa using the human capital approach, and slightly outside the range of social cost estimates found in Lower- and Middle-Income Countries (LMICs) literature. The estimate is also larger than the conservative figures used for policy purposes such as the current National Road Safety Strategy, implying that under-reporting of RTCs data under-represents apparent socio-economic value of RTCs. The study recommends improvement in the procedures of crash data by operationalizing the recently established centralized RTCs database, as well as adoption of systematic approaches to estimation of crash costs by policy makers.

Vitamin D supplementation is a cost-effective intervention after posterolateral lumbar fusion: a systematic review

Background: This study investigated the role of preoperative supplementation of 25(OH)D, a precursor of the active form of vitamin D, as a cost-effective strategy to decrease pseudarthrosis rates and overall healthcare burden after posterolateral fusion (PLF). Previous literature has emphasized the importance of vitamin D in bone health maintenance, spinal health, and outcomes in spinal fusion. Inadequate preoperative 25(OH)D levels may increase pseudarthrosis rates after PLF. Thus, a cost-estimation model was developed to determine the cost-effectiveness of both selective and nonselective 25(OH)D supplementation in PLF. Methods: Prevalence and cost data were obtained from published literature through systematic reviews. Cost of serum 25(OH)D assay and supplementation were obtained from public-use data. Mean, lower, and upper bounds of 1-year cost-savings were calculated for both supplementation scenarios. Results: Preoperative 25(OH)D screening and subsequent selective 25(OH)D supplementation was calculated to result in a mean cost-savings of $10,978,440 ($9,969,394 to $11,987,485) per 10,000 PLF cases. Nonselective 25(OH)D supplementation of all PLF patients was calculated to result in a mean cost-savings of $11,213,318 ($10,204,272 to $12,222,363) per 10,000 cases. Univariate adjustment projects that selective supplementation is a cost-effective strategy in clinical contexts where revision PLF costs exceed $781.89 and prevalence of 25(OH)D deficiency ≥0.612%. Nonselective supplementation is cost-effective in clinical scenarios where revision PLF cost ≥$198.09 and prevalence of 25(OH)D deficiency ≥0.1645%. Conclusions: This cost-predictive model promotes the role of preoperative 25(OH)D supplementation as a cost-effective mechanism to reduce overall healthcare burden after PLF. Nonselective supplementation appears to be more cost-effective than selective supplementation, likely due to the relatively lower cost of 25(OH)D supplementation compared with serum assays. Level of Evidence: Level III

Meta heuristic intelligent decision support model for uncertainty estimation of life cycle cost in railways rolling stock

In Railways rolling stock, Rate of Occurrence of Failures (ROCOF) of repairable components is time varying and has a failure trend, so the combined process of Reliability, Availability, Maintainability and Safety (RAMS) with Life Cycle Cost (LCC) is a stochastic process. Heuristic approaches have been used to design intelligent decision support model to reduce Life Cycle Cost (LCC) and to manage Maintainability to achieve the required Reliability. As heuristic approaches have major drawback of inability to combine both the exploration and exploitation capabilities to reach all regions in the search space, so we propose Meta heuristic approach by hybridization Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Experimental study proves that our proposed approach significantly avoids this drawback and outperforms the traditional heuristic approaches in reducing LCC while properly maintains the required reliability.