Path Method Research Articles

Modified critical chain scheduling for construction projects

ABSTRACT Project planning and scheduling is a critical stage in the construction industry that still faces significant challenges such as cost overruns and time delays, primarily due to the dominance of traditional network analysis techniques like the Critical Path Method (CPM), Program Evaluation and Review Technique (PERT), etc. Critical Chain Method emerged as an alternative, incorporating new tools to address the limitations of CPM such as path merging, availability of resources and extra safety time. Still, it has shortcomings in addressing resource allocation because there is no proven effective resource allocation algorithm. The current paper integrating the Earliest Late Start (ELS) technique with the critical chain approach for managing resource allocation on construction projects. Concise criteria were illustrated to deal with critical chain scheduling, including the implementation of the ELS resource allocation method. In the meantime, the explicit critical chain scheduling and the visual identification of the critical chain network using an Excel-based model are provided. The methodology of the modification emphasizes addressing the probabilities of the resource leveling calculations in the critical chain scheduling identified through the literature survey. The methodology is validated utilizing benchmark example from the literature and a survey was conducted and distributed among engineers, selected to investigate their feedback on the modified approach. Finally, the results show the potential application of the developed technique to reduce project duration and cost and monitor only one critical chain during the project period.

Best Subset Solution Path for Linear Dimension Reduction Models Using Continuous Optimization.

The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high-dimensional contexts where the number of variables is usually much larger than the number of observations. In this paper, we focus on two multivariate statistical methods: principal components analysis and partial least squares. Both approaches are popular linear dimension-reduction methods with numerous applications in several fields including in genomics, biology, environmental science, and engineering. In particular, these approaches build principal components, new variables that are combinations of all the original variables. A main drawback of principal components is the difficulty to interpret them when the number of variables is large. To define principal components from the most relevant variables, we propose to cast the best subset solution path method into principal component analysis and partial least square frameworks. We offer a new alternative by exploiting a continuous optimization algorithm for best subset solution path. Empirical studies show the efficacy of our approach for providing the best subset solution path. The usage of our algorithm is further exposed through the analysis of two real data sets. The first data set is analyzed using the principle component analysis while the analysis of the second data set is based on partial least squareframework.

Detecting a suitable path method for assisted localization techniques in transmission of data over wireless sensor networks

Detecting a suitable path method for assisted localization techniques in transmission of data over wireless sensor networks

Project Planning Analysis with the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT): Study on the Development of Facilities and Infrastructure of SMK Negeri 1 Garut (DOOM)

Success in implementing a project often hinges on thorough planning and effective control. This study examines the infrastructure development project at SMK Negeri 1 Garut (DOOM), applying the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) to estimate project completion time, costs, probability, and potential planning alternatives. The goal is to achieve maximum efficiency and project success. The research began by determining the activities, their sequence, and duration using the PERT and CPM methods, with POM QM as the data processing tool. Based on the PERT method, the estimated project duration is 128 days with a 50% probability. The critical path, identified through the CPM method, includes activities B through P and predicts a project time of 116 days with a 0.13% probability. Additionally, Crashing the project reduces the time to 97 days, with a minuscule probability of 3.095359E-15. The probability of meeting the contract deadline of 126 days is 31%. To achieve a 95% probability, the project time extends to 130 days with an acceleration cost of IDR 2,575,898,910.73. The study suggests that when making addendums, companies should not only focus on cost changes but also consider adjustments to project timelines to meet the expected deadlines. It is recommended that companies incorporate CPM and PERT as alternative planning methods to select the most optimal cost-effective approach for project acceleration, thereby minimizing the risk of financial losses.

Analysis of Time Control Strategies Using The Critical Path Method (CPM) In The Development of The Java Residence Cluster For Mass-Product

The increasing complexity and scale of industrial and construction projects demand efficient management techniques to ensure timely delivery, cost-effectiveness, and adherence to quality standards. This study focuses on the implementation of the Critical Path Method (CPM) in managing the construction of the Cluster Java Residence in Sidoarjo to enhance time efficiency. The research investigates the effectiveness of CPM in identifying and mitigating delays by analyzing project activities, their interdependencies, and schedule flexibility using forward and backward pass calculations. Secondary data from project schedules and primary data from field observations were analyzed to identify critical paths and optimize project timelines. Results indicate that the implementation of product quality control in the construction of housing units in the Java Residence Cluster is still under control, although the average number of defects or complaints per housing unit is relatively high. The issues of defects or complaints in the construction of housing units in the Java Residence Cluster are caused by factors related to humans, machines or tools, materials, and work methods.

Measurement of Project Cost and Time Efficiency with Earned Value Approach In The Implementation of Baggage Handling System at Sultan Hasanuddin International Airport Makassar

The installation of the Baggage Handling System (BHS) at Sultan Hasanuddin International Airport, Makassar, faces challenges related to cost and time inefficiencies, particularly delays in project completion. This study aims to evaluate these inefficiencies by applying the Earned Value Method (EVM) to assess project performance and identify areas for improvement. Utilizing EVM metrics, the research identifies that the Schedule Performance Index (SPI) is below 1, indicating a delay in the project timeline, with the actual duration extending to 556 calendar days compared to the planned 546 days. This delay underscores the need for a more robust project management approach. The study employs the Critical Path Method (CPM) in conjunction with Microsoft Project to improve scheduling accuracy, alongside recommendations for project acceleration using the Crashing Program method. The findings highlight that early implementation of EVM can help mitigate discrepancies in cost and time, enabling better control and monitoring throughout the project lifecycle. The study concludes that integrating advanced scheduling techniques and real-time performance metrics is essential to achieving efficiency and minimizing delays in similar infrastructure projects.

A Comprehensive Review of Machine Learning and Multi-Criteria Decision Analysis in Construction Delay Management

Construction delays remain a critical challenge globally, significantly affecting project performance metrics such as cost, schedule adherence, quality, and safety. Traditional methods like the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) are widely used but lack the predictive capabilities and adaptability required for dynamic project environments. Machine Learning (ML) and Multi-Criteria Decision Analysis (MCDA) have emerged as innovative tools for addressing these limitations. ML excels in predicting delay impacts by analysing historical data and uncovering hidden patterns, while MCDA provides a structured framework for prioritizing delay factors based on their influence on project performance. This paper provides a comprehensive review of the application of ML and MCDA in construction delay management, highlighting their strengths, limitations, and potential integration. The review identifies research gaps, including the need for hybrid frameworks that combine predictive insights with decision support. It proposes future directions to develop real-time tools for delay mitigation, ultimately enhancing construction project outcomes through data-driven decision-making.

Numerical investigation on degradation mechanism of reinforced concrete structures under close-in explosion

In current practice, the analysis of progressive collapse in building structures widely employs the alternate load path method. However, there are very few collapse tests that can be utilized under blast loads, especially for investigating the degradation mechanism of progressive collapse resistance in post-blast building structures. In this study, the degradation mechanisms and methods for damage assessment of reinforced concrete structures under blast loads were investigated through numerical analysis. A substructure model derived from explosion loads based on a drop-hammer testing machine was validated by comparing with test results, and the established model accurately captured three typical failure modes in the substructure columns and confirmed the axial tensile effects of the columns on the adjacent components. A substructure damage assessment method was used to assess the degree of damage in a 5-story reinforced concrete frame structure under different explosion scenarios. The results show that the degree of damage distribution of the reinforced concrete frame structure exhibited an S-shaped distribution under corner column blast scenarios while an arch-shaped distribution was observed for middle column blast scenarios. Furthermore, empirical formulas based on the explosive mass and distance ( M- R) curves were established. These empirical formulas can help to rapidly predict the damage levels of RC frame structures for a given explosion scenario within a certain scope.

Energy-Efficient Route Planning Method for Ships Based on Level Set.

To reduce the fuel consumption of ships' oceanic voyages, this study incorporates the influence of ocean currents into the traditional level set algorithm and proposes a route planning algorithm capable of identifying energy-efficient routes in complex and variable sea conditions. The approach introduces the influence factor of ocean current to optimize routing in dynamically changing marie environments. First, models for the energy consumption of ships and flow fields are established. The level set curve is then evolved by integrating the flow environment and energy consumption gradient, solving the Hamilton-Jacobi equation with energy consumption parameters. The optimal path is subsequently determined through backtracking along the energy consumption gradient, enabling energy-efficient route planning from the starting point to the endpoint in complex ocean conditions. To verify the effectiveness of the proposed algorithm, its performance is evaluated through two case studies, comparing energy consumption under different environmental conditions. The experimental results demonstrate that, compared to the shortest path method based on the level set algorithm, the proposed approach achieves an energy saving rate of approximately 2.1% in obstacle-free environments and 1.4% in environments with obstacles.

Identifying priority areas for conservation based on the evaluation of ecological network resilience in the Hyrcanian Forest ecosystem

Abstract Habitat loss and fragmentation in forest ecosystems are serious threats that lead to reduced resilience. The integrity and stability of the ecosystem is fostered by recognizing and protecting areas that are essential to maintaining the resilience of the ecological network. Research in the field of ecological network resilience has garnered attention in recent years, although the necessity of developing various assessment methods for network resilience is evident. Taking Hyrcanian Forest ecosystem as a case study, this research aimed to identify the most important areas of the ecological network in maintaining and enhancing the resilience. To achieve this goal, first, a combination of the morphological spatial pattern analysis (MSPA) method and the assessment of the significance of ecosystem services were used to extract ecological source areas. Next, utilizing circuit theory and the least-cost path method, a network connecting sources was constructed, and pinch points were identified. After that, high-risk areas in ecological sources were found using the habitat risk assessment method. Using this integrated approach leads to the identification of valuable areas that are vulnerable to human threats and disturbances. Finally, the node removal method coupled with the calculation of network resilience indices, connectivity, and efficiency was employed to prioritize conservation areas. The results of the study indicated that the most important nodes were located in the northern edges of the forest, which have been under threat in recent years. Additionally, the region ranked moderately in terms of connectivity, indicating the importance of focusing on the conservation of forest patches before the complete fragmentation of the area. Furthermore, our findings underscore the importance of considering landscape connectivity and ecological network resilience in conservation planning for policymakers and managers aiming to protect biodiversity in the Hyrcanian Forest ecosystem.

Waterbirds habitat mapping using unmanned aerial vehicle in Belawan Mangrove Ecosystems, North Sumatera, Indonesia

Waterbirds are ecologically dependent on wetlands. Monitoring waterbird species diversity and habitat conditions is very important to protect natural resources from threats and habitat destruction. The use of Unmanned Aerial Vehicle (UAV) technology can overcome survey constraints for preserving mangrove ecosystems, which have difficult access and take a long time. This study aims to identify waterbirds' habitat in mangrove ecosystems in the Belawan Bird Island area of Medan City using Unmanned Aerial Vehicles (UAV). This research was conducted in June-August 2022 in the Bird Island area on the coast of Medan City, North Sumatra Province, Indonesia. The method used in the observation of waterbirds is using the path method. Field surveys for vegetation analysis use the census plot method. Furthermore, aerial photo processing and Object-Based Image Analysis (OBIA) of the mapping results through UAVs are carried out. Image processing with OBIA goes through the stages of segmentation, classification, and accuracy testing. We found 4 bird species found in Burung Island, namely, Egretta garzetta, Egretta alba, Phalacrocorax sulcirostris and Bubulcus ibis. The results of mapping using UAVs obtained a mangrove forest waterbird habitat covering an area of 1,01 hectares. The results of aerial photo identification found that the number of birds in the study area was 23,454 birds with a density of about 2 birds/m2. Vegetation found on bird islands are Avicenia marina, Rhizophora apiculata, and Nypah fruticans. Waterbirds in the Belawan mangrove area are local birds. The accuracy test results of waterbirds' habitat mapping with the OBIA method obtained an overall Overall Accuracy result of 77%. We conclude that the results of UAV mapping of waterbird habitats are reliable for use as final classification results.

Research on deep learning-based fault diagnosis methods for aero engine gas path

Research on deep learning-based fault diagnosis methods for aero engine gas path

Pile installation effects in natural soft clays: A semi-analytical solution using strain path method

Pile installation effects in natural soft clays: A semi-analytical solution using strain path method

Progressive Collapse of Steel Structures Under Blast Loads Considering Soil‐Structure Interaction

This study focuses on the effect of soil‐structure interaction on the occurrence of progressive collapse due to an external blast. Therefore, a 3‐story fixed base steel moment‐resisting frame structure was first modeled and subjected to an external blast load. Then, the same structure was modeled considering soil‐structure interaction. In another stage of this study, the effect of plan irregularity on progressive failure resulting from the explosion was investigated, assuming the presence or absence of soil‐structure interaction. The direct simulation method and alternative load path method are all employed to simulate the progressive collapse process of steel frames using the commercial finite element program LS‐DYNA. The results of the numerical simulations demonstrated that the presence of soil‐structure interaction, significantly influences the response of the structure to the blast and this interaction increases the likelihood of progressive failure occurring even at symmetric and asymmetric structures. It was also shown that silty sand soil yields more critical conditions than other types of soil in the event of failure after the occurrence of an explosion in the structure.

Exploring Critical Path Solving Methods under Neutrosophic

Over the past few decades, the traditional critical path method and its various generalizations have become the most popular technique for managing complex projects. It plays a crucial role in differentiating between critical and non-critical tasks to enhance project schedules. For the first time in the literature, our proposed model implements two algorithms for the study of the critical path method, each addressing an advanced framework in the form of a single-valued triangular neutrosophic. The proposed algorithm 1 utilizes Python to extended Dijkstra’s algorithm under the neutrosophic framework, while the proposed algorithm 2 employs linear programming for optimality checks, which is solved using LINGO. Our comparison with previous research on the critical path method shows that the proposed algorithms are better at dealing with uncertainty, making project schedules more reliable and flexible. The findings lead to the proposed algorithm framework, combined with Python and LINGO, to enhance decision-making and improve the accuracy and efficiency of critical path identification in complex project environments.

Comparative Analysis of Optimization Methods for Grey Fuzzy Transportation Problems in Logistics

This study aims to explore the Grey Fuzzy Transportation Problem, which describes the decision-making processes under uncertainty in the transportation problem, which is an especially important study problem for the logistics sector and academic studies. Comprehensive analyses and suggestions are made to contribute to the effective solution of the Grey Fuzzy Transportation Problem and better control of transportation problems which contain uncertainty. In the research, four different optimization methods for the Grey Fuzzy Transportation Problem (GFTP), the Closed Path Method, Interval Optimization, Robust Optimization and Interval Optimization with Penalty Function, are comparatively analyzed. The analyses are done on a total of 40 test problems with four different problem sizes, small, medium, large and extra-large. The results revealed that Interval Optimization and Robust Optimization performed the best in terms of solution quality and computation time. In particular, extensive analyses on the Interval Optimization with Penalty Function method verified that this is an effective and consistent solution approach for GFTP.

Metaheuristic Multi-Objective Optimization Approach for Repetitive Construction Project Scheduling

The optimization of schedules for repetitive projects is a crucial phase in establishing an effective project timeline concerning both duration and cost. This study presents an innovative metaheuristic multi-objective optimization model tailored for repetitive projects. Distinguished by its original features, this model empowers schedulers to identify an optimal schedule that concurrently minimizes project duration, total cost, and interruption time. It encompasses various constraints and factors, including learning and forgetting effects, inter-activity soft logic, limited work interruption allowances, multiple crew formations, multiple construction methods per activity, while retaining all Critical Path Method (CPM) scheduling capabilities. The effectiveness of this new approach was validated through application to two case studies, demonstrating its capability to deliver optimal schedules for repetitive projects with heightened accuracy in minimizing project duration, cost, and interruption. Furthermore, the integration of learning effects, soft logic, and work interruption allowances within the optimization process substantially reduced project duration and cost, thereby yielding more precise and dependable optimal solutions.

Optimisation Methods for Cold Chain Logistics Path Considering Carbon Emission Costs in Time-Varying Networks

Optimisation Methods for Cold Chain Logistics Path Considering Carbon Emission Costs in Time-Varying Networks

Prediction of Lung Adenocarcinoma Driver Genes Through Protein-Protein Interaction Networks Utilizing GenePlexus.

Lung adenocarcinoma, a subtype of lung cancer, is produced by uncontrolled proliferation of somatic cells affected by some tumorigenic factors. The origin of this disease can be attributed to the concept of "cancer driver," which links the occurrence of tumor with specific changes in some key genes. These key genes can be identified at various molecular levels. Our innovative method uses a groundbreaking computing technology called GenePlexus to mine new genes related to lung adenocarcinoma. Initially, a vast network was synthesized from protein-protein interactions. Utilizing GenePlexus, we traversed paths interlinking aberrant genes across different layers and pinpointed emerging candidate genes situated on these trajectories. Finally, the candidate genes that were obtained underwent a series of filtering processes, including a permutation test, interaction test, and enrichment test. Compared with the shortest path method, GenePlexus has identified previously neglected genes involved in lung adenocarcinoma. For example, genes such as EGR2, EPHA3, FGFR4, HOXB1, and HEY1 play key roles at multiple molecular levels, including methylation, microRNA, mRNA and mutation, which affect tumorigenesis and lung cancer progression. These genes regulate various processes, from gene expression and cell proliferation to drug resistance to therapeutic drugs and the progress of lung adenocarcinoma.

Intelligent Decision-Making Method for AUV Path Planning Against Ocean Current Disturbance via Reinforcement Learning

Intelligent Decision-Making Method for AUV Path Planning Against Ocean Current Disturbance via Reinforcement Learning