Project Decisions Research Articles

Penerbangan Berkelanjutan: Perlindungan Hak Masyarakat Adat Berbasis Nilai Pancasila

In Indonesia, the protection of indigenous peoples' rights in the context of the aviation industry based on Pancasila values faces complex challenges. Although there have been efforts to establish a legal basis that respects these rights, their implementation in the aviation sector is still limited. This research aims to explore the implementation of Pancasila values in the protection of indigenous peoples' rights in the aviation sector and identify collaborative steps that can be taken by the government, the aviation industry, and other stakeholders. Using a descriptive qualitative approach through literature studies, this study evaluates challenges such as the lack of participation of indigenous peoples in aviation project decisions and the lack of effective law enforcement. The results show that cooperation between governments, airlines, civil society, and indigenous peoples is essential to strengthen the protection of their rights. It is also necessary to evaluate the impact of airport development policies to ensure the protection of land rights and the sustainability of indigenous peoples' culture. The involvement of indigenous peoples in the planning of aviation projects is a concrete step to ensure the alignment of aviation infrastructure development with Pancasila values and the rights of indigenous peoples

Assessing Project Resilience Through Reference Class Forecasting and Radial Basis Function Neural Network

A project needs to be able to anticipate potential threats, respond effectively to adverse events, and adapt to environmental changes. This overall capability is known as project resilience. In order to make efficient project decisions when the project is subjected to disruption, such as adjusting the project budget, reformulating the work plan, and rationalizing the allocation of resources, it is necessary to quantitatively understand the level of project resilience. Therefore, this paper develops a novel approach for forecasting project performance, illustrating the changes in performance levels during the disruption and recovery phases of a project and thus quantitatively assessing project resilience. While there are several methods for assessing project resilience in existing research, the majority of assessment approaches originate from within projects and are highly subjective, which makes it difficult to objectively reflect the level of project resilience. Moreover, the availability of project samples is limited, which makes it difficult to forecast the level of project performance. In view of the fact that the Reference Class Forecasting (RCF) technique avoids subjectivity and the Radial Basis Function (RBF) neural network is known to be better at forecasting small sample datasets, this paper therefore combines the RCF technique and the RBF neural network to construct a model that forecasts the project performance of the current project after experiencing a disruption, further assessing the level of the project resilience. Specifically, this paper first presents a conceptual model of project resilience assessment; subsequently, an RBF neural network model that takes into account project budget, duration, risk level of disruption, and performance before disruption based on the RCF technique is developed to forecast project performance after experiencing disruption; and finally, the level of project resilience is assessed through calculating the ratio of recovery to loss of project performance. The model is trained and validated using 64 completed construction projects with disruptions as the datasets. The results show that the average relative errors between the forecast results of schedule performance index (SPI) and the real values are less than 5%, and the R2 of the training set and the testing set is 0.991 and 0.964, respectively, and the discrepancy between the forecasted and real values of project resilience is less than 10%. These illustrate that the model performs well and is feasible for quantifying the level of project resilience, clarifying its impact on project disruption and recovery situations, and facilitating the decision-makers of the project to make reasonable decisions.

Building ethical awareness to strengthen co-production for transformation

AbstractAwareness of different ethical theories can support transformation-oriented researchers in navigating value-based decisions in co-production. We synthesize and explicitly link the literature on co-production and ethical theories in philosophy to initiate this awareness. Four key decision points in co-production projects are outlined that require value-based actions: (1) what to focus on, (2) who to include, (3) how to co-create and (4) how to continue. To discuss how project actions can be examined from different ethical perspectives, we synthesize the claims of four ethical theories and discuss them in the context of co-production project choices. The four ethical theories are: deontological ethics, utilitarianism, contractualism and virtue ethics. Overall, we argue for embracing pluralistic ethical perspectives when navigating decisions in co-production projects.

VEIDEA: A Comprehensive Framework for Implementing Building Information Modeling-Based Value Engineering Within a Common Data Environment in Construction Projects

The Architecture, Engineering, and Construction (AEC) industry faces significant global challenges, including frequent project delays, budget overruns, and inadequate stakeholder collaboration. To address these issues, Value Engineering (VE) and Building Information Modeling (BIM) have been increasingly used in large-scale, complex construction projects. Although many studies highlight the benefits of integrating VE with BIM, its full practical potential has not yet been realized. This study aims to investigate the integration of VE and BIM within a Common Data Environment (CDE) to improve decision making and project outcomes. A comprehensive framework was developed, consisting of four interconnected modules: (1) Creating the CDE, (2) Developing the BIM Model, (3) Implementing Value Engineering, and (4) Conducting a Value Engineering Study. Central to this framework is the introduction of the VEIDEA” data bank, a structured system based on the OmniClass classification, which stores and organizes VE ideas. Additionally, the framework incorporates the Analytical Hierarchy Process (AHP) to automate the evaluation phase, assisting designers and VE teams in making data-driven decisions on design alternatives. Empirical results from a case study of an office building show significant cost savings, with a 20% reduction in reinforced concrete (RC) slab costs and a 39% reduction in flooring material costs. These findings demonstrate the potential for integrating VE and BIM to enhance cost-effectiveness and overall project performance. This study offers a novel approach to optimizing project collaboration, decision making, and efficiency in the AEC industry.

Creating a comprehensive framework for design, construction and management of healthy buildings

While healthy buildings are investigated in academic research, practice, and policy, they often fail to address the needs and gaps due to insufficient knowledge transfer between them. Moreover, there is no general definition of healthy buildings that could act as a reference for different stakeholders of the building sector. Therefore, this paper presents a novel framework for healthy buildings and a definition for compiling existing knowledge and addressing the needs and gaps. The holistic approach of the framework was developed through an extensive literature review and tested with 12 building case studies in the major European climate zones. The framework outlines five different dimensions (improving mental and physical health, designed for human needs, sustainably built and managed, resilient and adaptive, empowering people), including 24 indicators and sub-indicators. The validation of the framework revealed that it is possible to assess different building projects against different indicators under each dimension. However, challenges remain due to issues such as lack of time, cost of data collection, or lack of documentation. The developed framework provides a balanced approach that promotes both individual well-being and broader sustainability goals. It provides necessary guidance to inform practitioners and policy makers as well as other building sector stakeholders to integrate healthy buildings thinking into their project development, assessment, and decision making.

A Survey of Data-Driven Construction Materials Price Forecasting

The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent fluctuations in construction material prices. This paper reviews the application of data-driven techniques, particularly machine learning, in forecasting construction material prices. The models are categorized into causal modeling and time-series analysis, and characteristics, adaptability, and insights derived from large datasets are discussed. Causal models, such as multiple linear regression (MLR), artificial neural networks (ANN), and the least square support vector machine (LSSVM), generally utilize economic indicators to predict prices. The commonly used economic indicators include but are not limited to the consumer price index (CPI), producer price index (PPI), and gross domestic product (GDP). On the other hand, time-series models rely on historical price data to identify patterns for future forecasting, and their main advantage is demanding minimal data inputs for model calibration. Other techniques are also explored, such as Monte Carlo simulation, for both price forecasting and uncertainty quantification. The paper recommends hybrid models, which combine various forecasting techniques and deep learning-advanced time-series analysis and have the potential to offer more accurate and reliable price predictions with appropriate modeling processes, enabling better decision-making and cost management in construction projects.

Crucial factors for decision-making in international real estate development projects: Evidence from Chinese developers

This study aims to investigate the crucial factors influencing decision-making in international real estate development projects. The identification of these factors was initially derived from a comprehensive literature review and further refined through expert input from Chinese real estate developers. Subsequently, questionnaires were administered to these experts in three focus groups. The key determinants for decision-making in international real estate development projects were identified as follows: political stability condition of the host country, international diplomatic relations between the host and home country, macro-economic situation of the host country, maturity of real estate market in the host country, project financing, and international experience of the enterprise. The most significant categories encompassed political, economic, and project factors. This study addresses the scarcity of literature on international real estate development, with a specific focus on developers originating from developing nations. It has the potential to enrich the discourse surrounding international real estate practices and contribute valuable insights for future academic investigations.

Towards more realistic evaluations: The impact of label delays in malware detection pipelines

Developing and evaluating malware classification pipelines to reflect real-world needs is as vital to protect users as it is hard to achieve. In many cases, the experimental conditions when the approach was developed and the deployment settings mismatch, which causes the solutions not to achieve the desired results. In this work, we explore how unrealistic project and evaluation decisions in the literature are. In particular, we shed light on the problem of label delays, i.e., the assumption that ground-truth labels for classifier retraining are always available when in the real world they take significant time to be produced, which also causes a significant attack opportunity window. In our analyses, among diverse aspects, we address: (1) The use of metrics that do not account for the effect of time; (2) The occurrence of concept drift and ideal assumptions about the amount of drift data a system can handle; and (3) Ideal assumptions about the availability of oracle data for drift detection and the need for relying on pseudo-labels for mitigating drift-related delays. We present experiments based on a newly proposed exposure metric to show that delayed labels due to limited analysis queue sizes impose a significant challenge for detection (e.g., up to a 75% greater attack opportunity in the real world than in the experimental setting) and that pseudo-labels are useful in mitigating the delays (reducing the detection loss to only 30% of the original value).

Failure analysis of Nehbandan granite under various stress states and strain rates using a calibrated Riedel–Hiermaier–Thoma constitutive model

This study presents a comprehensive procedure for determining, calibrating, and validating the Riedel–Hiermaier–Thoma (RHT) material model parameters for granite. The process involves collecting a comprehensive dataset of conventional mechanical tests conducted on various types of granite worldwide. Based on this dataset, one set of RHT material model parameters is determined. Additionally, a specific granite sample from Iran, known as Nehbandan granite, is characterized through physical and mechanical testing to obtain another set of parameters. The challenging task of determining the third set of parameters, which are difficult to obtain analytically and experimentally, is accomplished through a calibration process that iteratively adjusts the parameters based on comparisons between numerical simulation results and experimental data. To validate the determined parameters, a series of tests, including uniaxial compressive strength (UCS), triaxial compressive strength (TCS), Brazilian tensile strength, and dynamic Brazilian using split Hopkinson pressure bar (SHPB) tests, are conducted on the Nehbandan granite. These tests are also simulated using LS-Dyna software, and the numerical simulation results are compared with the corresponding experimental test results. The comparison between the numerical and experimental data serves as a means of validating and verifying the accuracy and reliability of the determined RHT material model parameters for granite. The results demonstrate the successful determination and calibration of the RHT material model parameters for granite. The model exhibits effectiveness in predicting the behavior of granite under various loading conditions. The validation process confirms the accuracy and reliability of the determined parameters through a close agreement between numerical simulations and experimental data. The findings contribute to a better understanding of granite's mechanical response and provide a reliable tool for simulating and predicting its behavior in engineering applications. The validated RHT material model parameters offer a robust framework for accurate numerical simulations, enabling engineers to make informed decisions in rock engineering projects.

Space Efficiency of Tall Buildings in Singapore

Space efficiency in Singaporean tall buildings results from a complex interplay of historical, architectural, engineering, technological, socioeconomic, and environmental factors. The city-state’s innovative and adaptive approach has enabled it to overcome the challenges associated with skyscraper construction, leading to the development of some of the most advanced and sustainable high-rise structures in the world. However, there is currently a lack of detailed analysis on space utilization in Singaporean high-rise buildings. This study addresses this gap by examining 63 cases. The main findings of this research: 1. Residential functions, central core layouts, and prismatic shapes are the most frequent. 2. Concrete material with a shear-walled frame system is the preferred structural choice. 3. Average spatial efficiency is 80%, and the core-to-GFA (Gross Floor Area) ratio averages 17%. These metrics vary from a minimum of 68% and 5% to a maximum of 91% and 32%, respectively. These insights offer valuable guidance for Singaporean construction professionals, particularly architects, helping them make informed design decisions for high-rise projects.

Investment in CCUS under technical uncertainty considering investor's risk aversion: An exotic compound real-options approach

Carbon capture, utilization, and storage (CCUS) technology is effective and value-adding solution for reducing emissions. However, the development and commercialization of these technologies are challenging due to high investment costs and several uncertainties. This study develops a novel comprehensive real-options-based model to evaluate investment in CCUS projects considering the technical risk and the investor's risk aversion. This study proposed an exotic compound real options model that combines American and barrier options. First, applying the Poison process, the technical risk is explicitly modeled. Secondly, the investor's risk aversion is defined as a barrier level for the barrier option part of the proposed model. Thirdly, the value of the project is evaluated through the exotic compound real option. Finally, we assess the economic viability of the project under multiple scenarios. The results of implementing the model for a real case show that the integrated technical risk assessment and the barrier option appropriately address investors' risk aversion. Furthermore, the comparison indicates that the proposed compound real options model is more effective than the traditional NPV (Net Present Value). Regarding policymaking, the results reveal that setting an appropriate carbon tax that considers the costs of carbon capture would be more beneficial. Further, the model provides investors helpful guidance to make proper investment decisions for CCUS technology projects under uncertainties.

Demonstrating clean energy transition scenarios in sector-coupled and renewable-based energy communities.

Energy communities facilitate several advantages, including energy autonomy, reduced greenhouse gas emissions, poverty mitigation, and regional economic development. They also empower citizens with decision-making and co-ownership prospects in community renewable projects. Integrating renewable energy sources and sector coupling is a crucial strategy for flexible energy systems. However, demonstrating clean energy transition scenarios in these communities presents challenges, including technology integration, flexibility activation, load reduction, grid resilience, and business case development. Based on the system of systems approach, this paper introduces a 4-step funnel approach and a 4-step reverse funnel approach to systematically specify and detail demonstration scenarios for energy community projects. The funnel approach involves four steps. First, it selects demonstration scenarios promoting energy-efficient state-of-the-art renewable technologies and storage systems, flexibility through demand side management techniques, reduced grid dependence, and economic viability. Second, it lists all existing and planned project technologies, analysing energy flows. Third, it plans actions at different levels to implement the demonstration scenarios. Fourth, it validates the strategies using key performance indicators (KPI) to quantify the effectiveness of the planned measures. Furthermore, the reverse funnel approach delves deeper into the demonstration scenarios. The four steps involve identifying stakeholder perspectives, describing scenario scopes, listing conditions for realisation, and outlining business models, including value chains and economic assumptions. This approach provides a detailed analysis of the demonstration scenarios, considering actors, objectives, boundary conditions, and business assumptions. The methodologies are exemplified in three diverse European energy communities extending across residential, commercial, tertiary, and industrial establishments, allowing power-to-x and sector coupling opportunities. The paper also suggested thirteen KPIs for validating renewable-focused energy community projects. Finally, the paper recommends increased collaboration between energy communities, knowledge sharing, stakeholder engagement, transparent data collection and analysis, continuous feedback, and method improvement to mitigate policy, technology, business, and market uncertainties.

Text summarization for pharmaceutical sciences using hierarchical clustering with a weighted evaluation methodology

In the pharmaceutical industry, there is an abundance of regulatory documents used to understand the current regulatory landscape and proactively make project decisions. Due to the size of these documents, it is helpful for project teams to have informative summaries. We propose a novel solution, MedicoVerse, to summarize such documents using advanced machine learning techniques. MedicoVerse uses a multi-stage approach, combining word embeddings using the SapBERT model on regulatory documents. These embeddings are put through a critical hierarchical agglomerative clustering step, and the clusters are organized through a custom data structure. Each cluster is summarized using the bart-large-cnn-samsum model, and each summary is merged to create a comprehensive summary of the original document. We compare MedicoVerse results with established models T5, Google Pegasus, Facebook BART, and large language models such as Mixtral 8×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}7b instruct, GPT 3.5, and Llama-2-70b by introducing a scoring system that considers four factors: ROUGE score, BERTScore, business entities and the Flesch Reading Ease. Our results show that MedicoVerse outperforms the compared models, thus producing informative summaries of large regulatory documents.

An integrated multi-criteria decision making approach for selecting the starting location of railroad projects

PurposeThe successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.Design/methodology/approachBased on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.FindingsThe study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.Originality/valueThis study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

ESG-Driven Investment Decisions in Photovoltaic Projects

As global climate change intensifies and environmental awareness increases, investing in renewable energy has become a primary economic and social development priority. Photovoltaic (PV) projects, as a clean and sustainable energy technology, have garnered significant attention due to their notable environmental and economic benefits. However, traditional investment evaluation methods such as net present value (NPV) analysis fail to adequately capture the flexibility and future uncertainties inherent in PV project investments. This paper presents a case study analysis proposing a delay option model that incorporates environmental, social, and governance (ESG) factors, providing a more scientific and flexible investment decision framework for PV projects. The case study results indicate that considering ESG factors significantly enhances the investment value of PV projects. This model not only provides comprehensive support for PV project investment decisions but also underscores the importance of establishing stringent carbon trading markets and policy incentive mechanisms to promote the widespread adoption and sustainable development of renewable energy projects.

Leader and Employee Behavioral Decision-Making in Construction and Demolition Waste Recycling Projects under Psychological Contract Theory

Although the beneficial role of enterprises in the management of construction and demolition waste (CDW) should not be overlooked, existing relevant studies have neglected to address the specific effects of the behavioral decisions of leaders and employees within enterprises in CDW recycling projects. This study creatively introduces psychological contract theory into the field of CDW management and aims to reveal the mechanism of recycling participation behaviors between leaders and employees in CDW recycling projects. Using the Stackelberg game method, a model consisting of leaders and employees in the construction enterprise was constructed to analyze the optimal decision-making of the enterprise under two cases of whether or not the employees contributed additional effort. The conclusions of this study are as follows: (1) The profits of construction enterprise leaders are always positively correlated with the degree of employee additional effort. Unlike the case of leaders, the effect of additional effort on employee profits hinges on the coefficient of employee additional effort. When the coefficient of employee additional effort is below the threshold, excessive additional effort can negatively impact their own profits. When the additional effort coefficient exceeds the threshold, the employee profits are positively related to the degree of additional effort. (2) Similar to the change in employee profits, the change in the total profit of the construction enterprise with the degree of additional effort is influenced by the additional effort coefficient. However, as the additional effort coefficient increases, the total profit of the enterprise shows a significant increase before employee profits. This study enriches the theoretical study of psychological contracts and provides guidance for decision-making between leaders and employees in the management of CDW recycling projects.

Ecological Design for Urban Regeneration in Industrial Metropolitan Areas: The Santa Cruz Refinery Case

Ecological design is crucial in shaping contemporary, resilient, and livable cities. The Santa Cruz de Tenerife Refinery, a prominent landmark in the Mid-Atlantic, serves as an exemplary case study for understanding advanced metropolitan processes and integrating trans-scalar, transdisciplinary, and nature-based solutions (NBS) practices into urban contexts. This article explores the challenges of transforming obsolete industrial areas, including the refinery’s decommissioning process, its port, and industrial heritage value, and their relationship with the sea, into vibrant urban cores. It examines innovative strategies for land use, decontamination, and urban resilience, which are vital for fostering adaptability and recovery from natural and anthropogenic disasters. By emphasizing the refinery’s connection to Santa Cruz de Tenerife and its metropolitan area, as well as its coastal interface, this study proposes a comprehensive methodology to assess the territorial impacts of urban processes and guide project decisions toward enhancing the quality of life for the region’s residents.

Sequential investment decisions for mining projects using compound multiple volatility real options approach

Managers evaluating investment decisions in mining projects have reported using the net present value approach (NPV). However, NPV has the drawback of collapsing variations from uncertainties in future project cash flows into a fixed expected project value today. Ignoring future uncertainties and contingencies can lead managers to make incorrect up-front binary decisions, such as investing in or abandoning the project now. The real options analysis approach (ROA) instead captures variations from uncertainties as volatilities in project value and allows flexibility in investment decisions to be contingent on information as it is revealed. Managers we consulted agreed that ROA is superior to NPV in principle, but too complex to apply in practice. The advanced mathematics and restrictive assumptions required by analytical ROAs make them especially impractical for real-world projects exposed to multiple uncertainties. Furthermore, real-world projects are often multistage and involve valuing a compound option, which is an option on an underlying option, for sequential investment decisions. Extant numerical compound ROAs have been applied to such projects but suffer from various drawbacks. Amongst other issues, they combine variations from multiple uncertainties into a consolidated volatility of project value. This conceals the impact of each source of uncertainty, resulting in inaccurate project valuation and incorrect investment decisions. We present an innovative compound multiple volatility real options approach (C-MVR) to value a multistage project while accommodating separate volatilities of project value arising from multiple uncertainties for making sequential investment decisions. The resultant value is termed the compound enhanced net present value. Implementing C-MVR for a real coal mining project demonstrates how other ROAs can be seen as inaccurate simplifications that produce erroneous investment decisions. C-MVR provides a rigorous and versatile approach that can be applied to many investment decisions across various industries.

Decision-making in major investment projects with a life cycle cost: improvement with sensitivity analysis and sustainability assessment

This study focuses on compressor station investments in Germany’s natural gas infrastructure, offering insights applicable to machinery, energy, and maintenance cost-driven decisions. A life cycle cost (LCC) analysis can guide investment choices; however, uncertainties in input data and future developments pose risks. The LCC-based studies encounter questions impacting their results and optimal selections. These uncertainties may lead to misallocations, emphasizing the need for careful consideration of investment decisions to avoid potential consequences and efficiently allocate limited funds. Various measures are available to mitigate the uncertainties and risks in LCC analyses. Recognized measures are deterministic and probabilistic. Seven case studies on investments in the natural gas infrastructure in Germany were analyzed in this context. In addition to the executed case studies, a case study from a scientific journal (published in 2001) was included in the analysis. The case studies were conducted by transmission system operators from 2005–2015, and a retrospective view made it possible to recognize whether the best options (due to the LCC analysis) were identified. Simulations were conducted with generated models using real historical input data such as energy costs. The re-calculation of the net present value or better discounted cumulated expenditure with real input data shows that the LCC analysis results are significantly dependent on the reliability of the input data and the prediction of their development. Therefore, validating the results using appropriate measures is mandatory. This study illustrates how sensitivity analysis can be used as a deterministic method to evaluate the LCC analysis results. A company’s success is increasingly determined by its sustainability. A pure LCC analysis is insufficient, so social, ecological, and economic sustainability assessments must be conducted. This study demonstrates the effectiveness of the weighted scoring method for sustainability assessments. Although this study relates to LCC in Germany’s natural gas infrastructure, the suggested process can be adopted for other investment projects comprising capital and operational expenditures.

Multi-Objective Resource-Constrained Scheduling in Large and Repetitive Construction Projects

Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still a need for thorough modeling of the practical implications of non-optimal decisions. This study proposes a multi-objective model, which can realistically represent the actual loss from not meeting the resource utilization priorities and constraints of a given project, including parameters that assess the cost of exceeding the daily resource availability, the cost of moving resources in and out of the worksite, and the cost of delaying the project completion. Optimization is performed using Genetic Algorithms, with problem setups organized in a spreadsheet format for enhanced readability and the solving is conducted via commercial software. A case study consisting of 16 repetitive projects, totaling 160 activities, tested under different objective and constraint scenarios is used to evaluate the algorithm effectiveness in different project management priorities. The main study conclusions emphasize the importance of conducting multiple analyses for effective decision-making, the increasing necessity for formal optimization as a project’s size and complexity increase, and the significant support that formal optimization provides in customizing resource allocation decisions in construction projects.