Project Duration Research Articles

A hybrid metaheuristic and simulation approach towards green project scheduling

AbstractThis research tackles the environmental concern of greenhouse gas emissions in the execution of projects, with a focus on multi-site projects where the transportation of resources is a major source of emissions. Despite growing consciousness among consumers and stakeholders about sustainability, the domain of project scheduling has often overlooked the environmental impact. This paper seeks to bridge this oversight by exploring how to reduce greenhouse gas emissions during both project activities and resource transportation. A novel approach is proposed, combining a simulation model with an improved non-dominated sorted genetic algorithm. The simulation model incorporates the stochastic nature of emission rates and costs. This method is further refined with innovative techniques such as magnet-based crossover and mode reassignment. The former is a genetic algorithm operation inspired by magnetic attraction, which allows for a more diverse and effective exploration of solutions by aligning similar ’genes’ from parent solutions. The latter is a strategy for reallocating resources during project execution to optimize efficiency and reduce emissions. The efficacy of the proposed method is validated through testing on 2810 scenarios from established benchmark libraries, 100 additional scenarios adhering to the conventional multi-site problems, and a case study. The Best-Worst Method (BWM) is applied for identifying the best solution. The findings indicate substantial enhancements compared to traditional methods with a 12.7% decrease in project duration, 11.4% in costs, and a remarkable 13.6% reduction in greenhouse gas emissions.

Heuristic approaches for a multi-mode resource availability cost problem in aircraft manufacturing

A multi-mode time-constrained project scheduling problem with generalized temporal constraints arising in aircraft manufacturing is studied in the paper. We propose a priority rule-based heuristic (PRH) and a biased random-key genetic algorithm (BRKGA) for its solution. A serial generation scheme (SGS) is used for computing schedules from a priority order of the tasks with given resource capacities and mode assignments. The SGS cannot guarantee that the maximum project duration and maximum time lags are respected. Starting with the highest possible resource capacities, the PRH performs the SGS in a repeated manner, reducing the least used resource capacity by one unit until the schedule becomes infeasible. Different priority rules are used for determining both mode assignments and task priority orders. We encode these two decisions as well as the resource capacities in the BRKGA and apply the SGS for decoding. Project duration and maximum time lag violations are penalized in the fitness function. Extensive computational experiments based on problem instances motivated by settings found at a large aircraft manufacturer demonstrate that the BRKGA outperforms the PRH under almost all experimental conditions, especially for problem instances with more complex networks and shorter maximum project durations.

Evaluating the impact of construction delays on project duration using machine learning and multi-criteria decision analysis

Evaluating the impact of construction delays on project duration using machine learning and multi-criteria decision analysis

PENERAPAN CRITICAL PATH METHOD DALAM PENJADWALAN PROYEK PEMASANGAN TANGKI STORAGE ST-1812, STUDI KASUS PADA PT. MSM

The installation of the ST-1812 storage tank aims to improve production efficiency at PT. MSM by saving costs in the use of steam from 16 hours to only 4 hours, which is used to heat the final product to temperature of 63°C according to work instructions. Critical Path Method is used to determine the optimal construction time, because this method can identify the sequence of tasks that are most critical or that have the most impact on the duration of the entire project. The purpose of this study is to determine the optimal duration for implementation and completion of the ST-1812 storage tank installation project at PT MSM. The analysis of the CPM application showed that the project took 90 days to complete, with 19 activities included in the critical path. The addition of working hours (overtime) on activities on the critical path makes the project can be completed in 73 days, 17 days faster than the initial duration of the project. The initial cost of the project was IDR 66,555,000, and the cost after accelerating the project with crashing calculations was IDR 74,886,429, with a cost slope of IDR 8,331,429.

The need for carbon finance schemes to tackle overexploitation of tropical forest wildlife.

Defaunation of tropical forests, particularly from unsustainable hunting, has diminished populations of key seed dispersers for many tree species, driving shifts in forest community composition toward small-fruited or wind-dispersed trees with low wood density. Such shifts can reduce aboveground biomass, prompting calls for overexploitation to be included in bioeconomic policy, but a synthesis of existing literature for wildlife impacts on carbon stores is lacking. We evaluated the role of wildlife in tropical forest tree recruitment and found that it was critical to tropical forest carbon dynamics. The emerging financial value of ecosystem services provided by tropical forest fauna highlights the need for carbon-based payments for ecosystem services schemes to include wildlife protection. We argue for three cost-effective actions within carbon finance schemes that can facilitate wildlife protection: support land security opportunities for Indigenous peoples and local communities; provide support for local people to protect forest fauna from overexploitation; and focus on natural regeneration in restoration projects. Incorporating defaunation in carbon-financing schemes more broadly requires an increased duration of carbon projects and an improved understanding of defaunation impacts on carbon stores and ecosystem-level models. Without urgent action to halt wildlife losses and prevent empty forest syndrome, the crucial role of tropical forests in tackling climate change may be in jeopardy.

Navigating Escalation Patterns in Road Construction Projects in Maharashtra

This research delves into the nuanced dynamics of escalation amounts in construction projects, differentiating their implications on residential versus road construction. Detailed analyses of four diverse projects expose distinctive patterns: Project No.1 witnesses peak steel escalation, while material, labour, and POL exhibit constancy. Project No.2 reveals material escalation as dominant, with recuperated steel and cement costs. Conversely, Project No.3 portrays negative escalations for material and POL, dominated by labour impacts. Project No.4 experiences negative escalations attributable to duration shifts. The study unveils a correlation between project duration and escalating costs, highlighting steel, material, and labour as pivotal in residential projects and labour, material, and POL in road projects. The research underscores the critical role of escalation clauses in contracts, elucidating their significance in mitigating direct impacts on project costs. The findings emphasize project-specific factors such as duration, material price fluctuations, project nature, client-contractor dynamics, and governmental policies shaping escalation outcomes.

Schedule Acceleration Planning in Construction Project (Case Study: Japek II Selatan Tollroad)

This study focuses on the remaining work at stations (STA) 53+950 to 54+550 in Jakarta – CikampekII Selatan Toll Road Package III project to find the critical path, completion durations, and projectcosts before and after acceleration. The CPM Technique was used to compile network planningdiagrams, determine the critical path and project duration. Crashing Project was applied to crashschedule through the total project duration and cost whose changes due to the implementation of thecrashing method. The critical path was found in mobilization, tree felling, clearing, dig and haul,borrow material, subgrade preparation, base course (A grade), subbase course (B grade), drainagelayer, lean concrete (t=10 cm), concrete pavement, concrete barrier type B, and signage. In the CPM,the duration of project completion is 214 days. The total project budget under normal circumstancesis Rp22.073.412.654. After accelerating with the Crashing Project by proposed overtime was obtainedthe reduced total cost of Rp32.004.882 from the total normal cost of Rp22.073.412.654 toRp22.041.407.771 with an optimum duration of 183 days

Proposal and appraisal of novel indicators and performance metrics for project monitoring

Project monitoring is a critical process for guaranteeing its excellence, ensuring the delivery of activities both on planned time and budget. Despite its importance, numerous discussions have arisen regarding which indicator is the most appropriate, and how and when to measure it. The primary objective of this article is to evaluate novel duration indicators based on the Earned Value Management (EVM), Earned Schedule Management (ESM) and Earned Duration Management (EDM) methodologies, and to assess new performance metrics. This involves the determination of the limits of the control charts through extensive Monte Carlo Simulations and their performance under several out-of-control scenarios. The robustness of the control charts is tested using performance metrics within a statistical control approach. The secondary objective consists of evaluating the impact of new probability distributions that describe the project activities, such as the Gamma and Conway–Maxwell–Poisson (CMP) distributions. The contributions of this study are threefold: firstly, the Duration Performance Index (DPI) from the EDM is a reliable indicator for monitoring the overall project duration. Secondly, the probability of overreaction (PO) and the accuracy (Ac) are the performance metrics that most produced robust indicators. Lastly, it is observed that different probability distributions, specifically Gamma and Conway–Maxwell–Poison (CMP) distributions, seem to interfere with the correct generation of control charts signals, although they do not significantly affect the overall duration of the entire project.

Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development

Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfactory outcomes. Although existing research has focused on task scheduling, developer recommendations, and reward mechanisms, there has been insufficient attention to the support of platform moderators, or copilots, who are essential to project success. A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. This paper introduces an innovative machine learning approach designed to automate the prediction of project duration on CSD platforms. Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. Zero-shot learning algorithms exhibit superior performance, with an average accuracy of 92.76%, precision of 92.76%, recall of 99.33%, and an f-measure of 95.93%. The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction.

Nanoscale Understanding on CO2 Diffusion and Adsorption in Clay Matrix Nanopores: Implications for Carbon Geosequestration.

Carbon capture and storage (CCS) in subsurface reservoirs represents a highly promising and viable strategy for mitigating global carbon emissions. In the context of CCS implementation, it is particularly crucial to understand the complex molecular diffusive and adsorptive behaviors of anthropogenic carbon dioxide (CO2) in the subsurface at the nanoscale. Yet, conventional molecular models typically represent only single-slit pores and overlook the complexity of interconnected nanopores. In this work, finite kaolinite lamellar assemblages with abundant nanopores (r < 2 nm) were used. Molecular dynamics simulations were performed to quantify the spatial distribution correlations, adsorption preference, diffusivity, and residence time of the CO2 molecules in kaolinite nanopores. The movement of the CO2 molecules primarily occurs in the central and proximity regions of the siloxane surfaces, progressing from larger to smaller nanopores. CO2 prefers smaller nanopores over larger ones. The diffusion coefficients increase, while residence times decrease, with the pore size increasing, differing from typical slit-pore models due to the pore shape and interconnectivity. The perspectives in this study, which would be challenging in conventional slit-pore models, will facilitate our comprehension of the CO2 molecular behaviors in the complex subsurface clay sediments for developing quantitative estimation techniques throughout the CCS project durations.

The Use of Participatory Modelling Methods in Agri-Environmental Research – A Systematic Mapping Dataset

Participatory modelling (PM) is a transdisciplinary research approach that involves stakeholder in a modelling process to develop or improve qualitative or quantitative models. To better understand the potential uses of PM in the emerging field of agroecology living lab research, I conducted a systematic search of the peer-reviewed literature and describe 78 participatory agri-environmental case studies in this dataset. Bibliographic data are included and each case study is described in terms of main goal(s), PM method(s) used, involved stakeholders and their contributions and the level of stakeholder participation in the project. I also extracted key metadata (if available), such as publication type and year, study location (country), funding source, project duration and outcomes beyond publications. This dataset adds value by revealing clusters and associations of methods and goals, by showing the dominating role of researchers in this type of participatory research, by pointing out the absence of PM in certain areas of agricultural production, such as hydroponic farming or viticulture and by providing a comprehensive foundation for the related research article. It also contained data not used in the related research article that could provide a basis for future research, such as linking methods, goals and stakeholder involvement with other metadata or comparing this agri-environmental research and other areas.

Organizational models for the decommissioning of nuclear power plants: Lessons from the United Kingdom and the United States

With nuclear reactor fleets continuously aging, the decommissioning of closed reactors is gaining increasing attention. In nuclear decommissioning, technical, organizational, and regulatory challenges lead to long project durations and cost escalations. This paper attempts to examine the organizational efficiencies in nuclear decommissioning by applying the framework of the "system good" analysis and assessments based on new institutional economics. It compares the former parent-body-organization model practiced in the United Kingdom with current approaches observed in the United States, highlighting potential gains and policy recommendations for private sector involvement in nuclear decommissioning. Inefficiencies experienced in the United Kingdom include regulatory issues, private actors' opportunism, and information asymmetries. Comparatively, the models observed in the United States show fewer such issues, but regulators should address potential profit-driven shortcuts and funding adequacy.

Developing a Robust Multi-Skill, Multi-Mode Resource-Constrained Project Scheduling Model with Partial Preemption, Resource Leveling, and Time Windows

Real-world projects encounter numerous issues, challenges, and assumptions that lead to changes in scheduling. This exposure has prompted researchers to develop new scheduling models, such as those addressing constrained resources, multi-skill resources, and activity pre-emption. Constrained resources arise from competition among projects for limited access to renewable resources. This research presents a scheduling model with constrained multi-skill and multi-mode resources, where activity durations vary under different scenarios and allow for partial pre-emption due to resource shortages. The main innovation is the pre-emption of activities when resources are unavailable, with defined minimum and maximum delivery time windows. For this purpose, a multi-objective mathematical programming model is developed that considers Bertsimas and Sim’s robust model in uncertain conditions. The model aims to minimize resource consumption, idleness, and project duration. The proposed model was solved using a multi-objective genetic algorithm and finally, its validation was completed and confirmed. Analysis shows that limited renewable resources can lead to increased activity pre-emption and extended project timelines. Additionally, higher demand raises resource consumption, reducing availability and prolonging project duration. Increasing the upper time window extends project time while decreasing the lower bound pressures resources, leading to higher consumption and resource scarcity.

Multimodal Hong Kong: Documenting soundscape and smellscape at places of intangible cultural heritage

The sensory cultural heritage creates identity and cohesion in a community. The Multimodal Hong Kong (MMHK) started in January 2023 and aims to build a database of soundscape and smellscape at culturally significant sites. This paper will introduce the project and its objectives, present results from a pilot survey, and describe the development of a field work methodology. Data collection methods include Ambisonics audio, 360 video, and 'smellprints', together with on-site observations based on the Soundscape Indices survey protocol and the Dravnieks-McGinley-Zarzo 'smell atlas'. Currently having data from 20 sites, MMHK aims to reach 100 within the project's duration, as well as conduct outreach such as sensory walks, interviews, and workshops with stakeholders. The overall goal is to produce in-depth knowledge of how information, via multiple sensory modalities, constitute the perceived quality of places, and thence, how an intangible cultural heritage emerges in Hong Kong. This knowledge will enable future research and applications in urban spatial design, experiencescape, and virtual tourism.

FOBICS: Assessing project security level through a metrics framework that evaluates DevSecOps performance

Context:In today’s software development landscape, the DevSecOps approach has gained traction due to its focus on the software development process and bolstering security measures in projects, a task in light of the ever-evolving cybersecurity threats. Objective:This study aims to address the lack of metrics for quantitatively assessing its efficacy from both security and business logic perspectives. Methods:To tackle this issue, the research introduces the Framework of Business Index Concerning Security (FOBICS), a set of metrics designed to enable transparent evaluations of project security. FOBICS considers various perspectives relevant to DevSecOps practices. It includes factors such as project duration and financial outcomes, making it appealing for implementation in business settings. Results:The effectiveness of FOBICS is validated theoretically and empirically via its application in two real-world projects: the results from these implementations show a correlation between FOBICS metrics and the security strategies employed as the development methodologies adopted by diverse teams throughout the projects. Conclusion:Hence, FOBICS emerges as a tool for assessing and continuously monitoring project security, offering insights into areas of strength and areas that may require enhancement. FOBICS is shown to be effective in assessing the level of DevSecOps implementation. The ease of calculating FOBICS metrics makes them easily interpretable and continuously verifiable. Moreover, FOBICS summarizes most of the other quantitative and qualitative metrics in the literature.

Time Management with Quality Control in Construction Projects by Critical Chain Project Management

The construction industry consistently grapples with challenges related to project delays, budget overruns and quality control, particularly in the residential sector. Critical Chain Project Management (CCPM) emerges as a promising approach to address these issues by integrating resources availability with project scheduling. This literature review explores the application of CCPM in residential construction, highlighting its effectiveness in improving time management and project out comes. By analysing existing research, case studies and practical examples the findings demonstrate that implementing CCPM can lead to significant reductions in project duration and enhancements in resource utilization. The review aims to provide insights into the unexplored areas of CCPM in scheduling, ultimately emphasizing its potential to transform time management practices in the construction industry.

Stability analysis of deep foundation pit with a double-row cast-in-place piles and diagonal steel lattice braces under sloped excavation conditions

Existing deep foundation pit support structures are commonly composed of external earth-retaining structures, internal horizontal bracings, and vertical columns. A closed bracing system, often formed by a horizontal support through a bracket board, frequently impedes vertical excavation and soil removal operations in the foundation pit, and the processes of assembly and dismantling are complex and time-consuming. This study presents a combined support system and construction method consisting of cast-in-place piles and diagonal steel lattice braces. For sloped excavation, diagonal braces were constructed by slotting through the reserved soil, allowing the use of a single layer of support within the excavation depth. This approach significantly optimizes the construction process, reducing both project duration and overall cost. The field monitoring results indicated that the support method effectively controlled the lateral displacement of the pile bodies. Field monitoring results demonstrated that the proposed support system effectively controlled the lateral displacement of the pile bodies. The adoption of a support-first, excavation-second approach significantly controlled the settlement of the ground surface around the foundation pit, thereby preventing excessive increments in the axial force of the supports due to the large longitudinal depth excavation. The calculation results of the three-dimensional finite element model for foundation pit excavation and support indicate that the proposed support method results in a decreasing ratio of the maximum lateral deformation depth of the pile body, denoted as δh-m, to the excavation depth He as the excavation depth increases. This implied that the displacement of the pile body was strictly controlled. When the depth of the foundation pit excavation exceeded 10 m, the maximum lateral deformation occurred below 10 m along the pile shaft. The diagonal steel lattice braces transferred the load to the top of the cast-in-place piles at the bottom of the pit, where the stress concentration occurred. During construction, special attention must be paid to the strength of the connection between the pile top and the connecting beams.

Project Acceleration Analysis Using the Crashing Method with The Alternative of Additional Labor and Additional Working Hours in Drainage Work

Project work delays often occur due to differences in site conditions, design changes, weather influences, and planning errors. The problem faced is the project duration, where if the project duration is accelerated or crashes, there will be several options to cut the project duration. This is the problem that becomes the topic of the thesis/journal, namely "Analysis of Project Acceleration with the Crashing Method with Alternatives for Adding Manpower and Additional Working Hours in Drainage Works". The author uses the primary method, with sources of RAB, Time Schedule, working drawings and analysis of city unit prices. As well as secondary research methods with sources, from relevant books, and project data such as price lists and wages. From the results of the study it can be concluded that the total wages of workers under normal conditions is IDR. 16,864,000.00 with the duration of project work for 30 working days. From the results of the analysis in this study, the total wages of workers with alternative labor additions of IDR. 19,672,000.00 with the duration of project construction work for 25 working days or 15.15% faster than the normal duration. Meanwhile, the total wage for workers with the alternative of additional working hours (overtime) is IDR. 25,052,300.00 with the duration of the project construction work for 25 working days or 15.15% faster than the normal duration. A more economical alternative to completing the project is the addition of manpower because it saves IDR. 2,228,000.00 from additional working hours (overtime), from normal wages.

Evaluating the Impact of Artificial Intelligence in Managing Construction Engineering Projects

This study evaluates the impact of utilizing artificial intelligence (AI) in managing construction engineering projects. With the increasing complexity and scale of construction projects, AI offers promising solutions to enhance efficiency, accuracy, and decision-making processes. The study investigates the potential benefits, challenges, and practical applications of AI through detailed case studies. The study employs a mixed-methods approach, combining qualitative and quantitative research methods. Data was collected through literature reviews and case studies where AI had been successfully implemented. The analyses included comparisons between projects that used AI and those that did not. The findings demonstrated significant improvements in project efficiency, cost estimation accuracy, and risk management. For instance, AI-powered systems reduced scheduling errors by 35%, leading to more accurate timelines. Additionally, the integration led to a 20% reduction in project durations due to improved resource allocation and proactive risk management. Furthermore, AI-supported systems contributed to a 25% improvement in stakeholder satisfaction. In terms of cost estimation, AI-powered estimation tools improved the accuracy of cost estimates by 30%, helping to reduce budget overruns by 40%. In risk management, AI-supported tools enhanced the accuracy of risk identification by 45%, leading to the early detection of potential issues and the development of effective mitigation strategies that reduced the impact of risks by 30%. Thanks to these improvements, project success rates increased by 20%. These results demonstrate that integrating AI into the management of engineering construction projects can lead to tangible improvements in project efficiency, cost accuracy, and risk management, thereby enhancing stakeholder satisfaction and contributing to more successful project outcomes.

A data-driven approach for predicting cash flow performance of public owners in building projects: insights from Turkish cases

PurposeThe cash flow from government agencies to contractors, called progress payment, is a critical step in public projects. The delays in progress payments significantly affect the project performance of contractors and lead to conflicts between two parties in the Turkish construction industry. Although some previous studies focused on the issues in internal cash flows (e.g. inflows and outflows) of construction companies, the context of cash flows from public agencies to contractors in public projects is still unclear. Therefore, the primary objective of this study is to develop and test diverse machine learning-based predictive models on the progress payment performance of Turkish public agencies and improve the predictive performance of these models with two different optimization algorithms (e.g. first-order and second-order). In addition, this study explored the attributes that make the most significant contribution to predicting the payment performance of Turkish public agencies.Design/methodology/approachIn total, project information of 2,319 building projects tendered by the Turkish public agencies was collected. Six different machine learning algorithms were developed and two different optimization methods were applied to achieve the best machine learning (ML) model for Turkish public agencies' cash flow performance in this study. The current research tested the effectiveness of each optimization algorithm for each ML model developed. In addition, the effect size achieved in the ML models was evaluated and ranked for each attribute, so that it is possible to observe which attributes make significant contributions to predicting the cash flow performance of Turkish public agencies.FindingsThe results show that the attributes “inflation rate” (F5; 11.2%), “consumer price index” (F6; 10.55%) and “total project duration” (T1; 10.9%) are the most significant factors affecting the progress payment performance of government agencies. While decision tree (DT) shows the best performance among ML models before optimization process, the prediction performance of models support vector machine (SVM) and genetic algorithm (GA) has been significantly improved by Broyden–Fletcher–Goldfarb–Shanno (BFGS)-based Quasi-Newton optimization algorithm by 14.3% and 18.65%, respectively, based on accuracy, AUROC (Area Under the Receiver Operating Characteristics) and F1 values.Practical implicationsThe most effective ML model can be used and integrated into proactive systems in real Turkish public construction projects, which provides management of cash flow issues from public agencies to contractors and reduces conflicts between two parties.Originality/valueThe development and comparison of various predictive ML models on the progress payment performance of Turkish public owners in construction projects will be the first empirical attempt in the body of knowledge. This study has been carried out by using a high number of project information with diverse 27 attributes, which distinguishes this study in the body of knowledge. For the optimization process, a new hyper parameter tuning strategy, the Bayesian technique, was adopted for two different optimization methods. Thus, it is available to find the best predictive model to be integrated into real proactive systems in forecasting the cash flow performance of Turkish public agencies in public works projects. This study will also make novel contributions to the body of knowledge in understanding the key parameters that have a negative impact on the payment progress of public agencies.