Candidate Projects Research Articles

A data-driven decision-making approach for evaluating the projects according to resilience, circular economy and industry 4.0 dimension

Multi-industry organizations constantly define new projects for development and progression. However, the noteworthy point is that there are many options for defining new projects and allocating resources to them, from which the best choices must be selected among the candidate projects. In today's world, due to extensive changes in business conditions, traditional performance metrics are not suitable for project evaluation, and projects that can enhance organizational resilience and serve communities in line with economic and environmental components should be selected. Moreover, considering the widespread changes in digital transformation over the past decade, paying attention to Industry 4.0 standards in project selection is important. In this context, the main goal of this study is to design a data-driven decision-making model based on machine learning for evaluating and selecting projects in line with resilience, circular economy, and Industry 4.0 criteria. In the first step, 21 indicators were identified, and their weights were determined using the fuzzy best-worst method (FBWM). Then, using the weighted fuzzy inference system method (WFIS), rules for labeling data were established, and finally, a model for predicting and evaluating project performance was defined using the Light Gradient Boosting Machine algorithm. The findings indicate that the indicators “Increase market share using Industry 4.0 technologies” and “Net present value” are the most important in project evaluation. The developed model accurately evaluates projects with more than 93% accuracy, performing better than other algorithms and demonstrating the high performance of data-driven algorithms in project evaluation.

Industrial R&D project portfolio selection method using a multi-objective optimization program: A conceptual quantitative framework

Design/methodology/approach: Research and development (R&D) activities are crucial if companies are to adapt to technology changes, but budget constraints and limited resources often force companies to select a subset of candidate projects through portfolio selection techniques. However, existing models for R&D portfolio selection do not adequately consider interdependencies and types of projects, and this can lead to suboptimal selection and misalignment with corporate objectives.Findings: A Multi-Objective Optimisation Program (MOOP) is suggested transcending from classic manpower, time and financial planning into addition of strategic, skills and commercial objectives. A Pareto front is used as validation mechanism.Research limitations/implications: Project selection processes are widened with select and critical quantitative positions. Potentials remain in areas of team capability, corporate capabilities, deeper skill understanding, and stakeholder engagement.Practical implications: A quantitative validation is often overlooked in PPM project selection over more qualitative or idiosyncratic selection methods.Originality/value: A quantitative validation is often overlooked in PPM project selection over more qualitative or idiosyncratic selection methods.

ОСОБЕННОСТИ ПРИМЕНЕНИЯ ИНФОРМАЦИОННОЙ ТЕХНОЛОГИИ УПРАВЛЕНИЯ ПРОЦЕССОМ РАЗВИТИЯ КОМПЕТЕНТНОСТНОГО ПОТЕНЦИАЛА ПРОЕКТНО-ОРИЕНТИРОВАННОЙ ИТ-КОМПАНИИ

The paper presents the features of information technology for managing the competency development process of a project-oriented organisation. This technology allows planning the advancement of employee’ competencies based on the needs of the candidate projects under consideration and the organisation’s strategic goals, taking into account the current project activities. The ability to adjust the competency development programmes when changing the schedule of projects in which trained employees are involved is supported. The proposed information technology makes it possible to import data from already used information systems for managing projects and employees’ competencies. At the same time, in the process of forming development programmes, adjusted calendar schedules of current projects are built, taking into account employees’ distraction for training. The results of testing information technology in an IT company specialising in developing business analytics solutions are presented. As part of the experiment, the author considers identifying a group of employees among three project groups to develop additional competencies with the assistance of training courses. At the same time, it is necessary to minimize changes in project timetables, based on assessing the labour intensity of tasks. As a result, alternative programmes for developing employees’ competencies are generated, which make it possible to solve the problem. Conclusions are drawn about the applicability of the considered information technology and the advantages of use compared to the conventional approach are noted. Further plans and directions of development are presented.

A congestion-based local search for transmission expansion planning problems

Transmission Expansion Planning (TEP) is a challenging task that takes into consideration future representations of electricity consumption behavior and generation capacity/technology. Besides, the investment in new transmission assets is a capital-intensive task, which motivates a clear and well-justified decision-making process. As the most frequent industry practice relies on cost–benefit analysis with the evaluation of individual reinforcements, Metaheuristic Algorithms (MAs) are the most suitable techniques to evaluate candidate projects efficiently. Likewise, the intrinsic features of the problem can be incorporated into these methods taking advantage of the stochastic knowledge, to build more efficient heuristics instead of considering the solver just as a black box. In this way, this paper proposes a congestion-based local search to improve the performance of metaheuristics when solving the TEP problem. The novelty of the method lies in the utilization of the congestion level of the transmission assets to guide the search procedure. Further, this work also presents an up-to-date comparison between five MAs in solving the TEP problem. The experimental experience is conducted using the mentioned MAs in different test systems, and the results confirm that the novel approach is successful in improving the performance of the solution technique while obtaining better solutions in all test cases.

A Novel Decision-Making Framework to Evaluate Rail Transport Development Projects Considering Sustainability under Uncertainty

One of the constant concerns in public and private organizations is choosing a project from among the multitude of potential projects to be implemented. Due to the limited resources in different sectors, projects should be prioritized in order to obtain the maximum benefit. In national and government projects, it is not necessarily important to pay attention to financial components, and more dimensions should be considered. Sustainability is a component that considers various economic, environmental, and social aspects in the evaluation of projects. In this regard, in this study, the main goal is to evaluate and select rail transportation projects according to sustainability criteria. In general, 15 indicators were identified in three economic, environmental, and social sectors, which were weighted using the best–worst fuzzy method (FBWM). The most important indicators in the evaluation of projects are the investment cost, the rate of internal return from a national perspective, and the lesser impact of the plan on environmental destruction. According to the weighted indicators, the stochastic VIKOR approach is developed for the first time in this article, which was evaluated according to two scenarios of demand changes and cost changes of candidate projects. In the stochastic VIKOR approach, to deal with uncertainty, different scenarios are defined, through which it is possible to respond to different conditions and evaluate projects more realistically. Validation of this method is compared to other multi-criteria decision-making methods. The main contribution of this study is presenting the stochastic VIKOR approach for the first time and considering the uncertainty in project evaluation. The findings show that the projects that have the most economic gains from the national and environmental aspects are selected as the best projects.

Participatory Budgeting Designs for the Real World

Participatory budgeting engages the public in the process of allocating public money to different types of projects. PB designs differ in how voters are asked to express their preferences over candidate projects and how these preferences are aggregated to determine which projects to fund. This paper studies two fundamental questions in PB design. Which voting format and aggregation method to use, and how to evaluate the outcomes of these design decisions? We conduct an extensive empirical study in which 1 800 participants vote in four participatory budgeting elections in a controlled setting to evaluate the practical effects of the choice of voting format and aggregation rule.We find that k-approval leads to the best user experience. With respect to the aggregation rule, greedy aggregation leads to outcomes that are highly sensitive to the input format used and the fraction of the population that participates. The method of equal shares, in contrast, leads to outcomes that are not sensitive to the type of voting format used, and these outcomes are remarkably stable even when the majority of the population does not participate in the election. These results carry valuable insights for PB practitioners and social choice researchers.

Probabilistic cost-based decision-making matrix: IDIQ vs. DBB contracting

Public departments of transportation (DOTs) are increasingly adopting alternative contracting methods (ACMs), such as Indefinite Delivery/Indefinite Quantity (IDIQ) agreements, to shorten project delivery and increase scheduling flexibility. However, the challenge remains to decide when an IDIQ contract would offer better value for money in a project compared to the conventional Design-Bid-Build (DBB) method. This paper proposes an IDIQ project selection framework that incorporates expected construction costs to identify suitable candidate projects. Historical bid data, an alternate cost indexing system, nonlinear regression analysis, and the Monte Carlo simulation technique are used to create and compare IDIQ and DBB probabilistic construction cost estimates. The study demonstrates the effectiveness of the framework using a single-award IDIQ granted through the Minnesota Department of Transportation (MnDOT) and past bidding information for projects awarded between January 2008 and April 2015. Additionally, the paper acknowledges the limitations of the study, including its reliance on MnDOT data, the exclusion of non-cost factors, and the assumption of project homogeneity. The study also provides valuable recommendations for future research to address these limitations and enhance the understanding and practical implementation of IDIQ contracting in the construction industry.

A Multistep Iterative Ranking Learning Method for Optimal Project Portfolio Planning of Smart Grid

Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule.

Reasonable Conjecture and Demonstration of Fama-French Model and Its Applications in Australia and US

Fama-French three-factors model and Fama-French five-factors model have been considered as significant models for calculating stock returns and estimating potential risk events. However, as Fama and French did in 2015, one reasonable conjecture has been come up with: Is there any extra factors could be added into the Fama-French five factors model? This paper raises a reasonable conjecture about the number of factors in Fama-French model and use the method of data analysis process with R software. Then this paper prove that the Fama-French five-factors model is still the most reliable to estimate the returns of portfolios currently based on the statistical data summaries. Besides, this paper also talks about the application and applicability of Fama-French models in Australia and US with some real cases and data analysis process. The result of this study shows that the applicability of Fama-French models is confirmed in both Australia and US. So the investors in Australia and US could use Fama-French models to estimate their expected return for some specific candidate projects and choose the best one to make investment based on the analysis results.

Taxonomy and Ex Ante Metric of Climate Change Adaptation Projects Recorded in the Nationally Determined Contributions (NDCs) as Updated for Conference of the Parties-26 (COP-26)

The authors have proposed a method of reiterating the statistical analysis of the Nationally Determined Contributions (NDCs) of the UNFCCC Parties, which were updated at Conference of the Parties-26. The present analysis confirms the taxonomy developed in 2020, based on 2475 adaptive solutions recorded in 2022 NDCs, and discusses the differences observed. An ex ante adaptation metric is proposed, which allows monitoring of adaptive solutions over time and comparisons between projects in time and space. The fitness coefficient evaluates the ex ante relevance of these adaptive projects in relation to the climate challenges of each country. The authors have proposed a program of continuous improvement instead of a definitive calculation. The authors have developed an algorithm to automate the text analysis and minimize the subjectivity of the analysis. The objective is to assign a level of vulnerability to each project for each hazard in the country. The correspondence analysis was used to derive the most representative dimensions of project category dispersion and vulnerability intensities from a contingency table for each hazard. This coefficient can be made available to experts, project developers, and funders for ex ante evaluation and selection of candidate projects for funding before more in-depth analyses are carried out.

Study on Priorities of South-North Korea Traditional Medicine Cooperation Project

Objectives: The purpose of this study is to draw priorities for candidate projects so that future inter-Korean traditional medicine cooperation can be promoted efficiently and effectively.Methods: This study used the Delphi-AHP method to derive priorities for the inter-Korean traditional medicine cooperation project. First, 33 candidate projects were selected through a data survey. In addition, the priority importance score was calculated through a 2-round mini-Delphi survey of 20 experts. The importance of 33 candidate projects was calculated by three evaluation criteria, and the weights for these three evaluation criteria were derived through the AHP method.Results: As for the weight by item, 'feasibility' was the highest with 0.6749 points, followed by 'social ripple effect' (0.1811) and 'instrumentality' (0.1439). As a result of calculating the importance score by reflecting the weight of the evaluation criteria for each project, the South's sole project, "Understanding the Status of North Korea's Korean Medicine," was the top priority, followed by the "Establishment of Strategy for Inter-Korean Traditional Medicine and Cooperation.”Conclusions: As a result of this study, experts now believe that it is important to prioritize the highly feasible South Korean independent project in the field of traditional medicine between the two Koreas. This will serve as the basis for promoting cooperative projects in the event of future changes in the inter-Korean situation.

Candidate project selection in cross project defect prediction using hybrid method

Cross Project Defect Prediction (CPDP) is a process that develops a defect prediction model on source projects and then applies the same model to the target project. Day by day, new software projects are being developed, so selecting an appropriate training project from existing projects and from new upcoming projects, to train a predictor model is a challenging task in CPDP. In the present study, we have proposed a hybrid selection method to select a candidate project from existing projects and a probabilistic method to select a candidate project from new projects. The proposed hybrid method is a weighted combination of the Collaborative filtering (CF) method and the Content Based (CB) method. The probabilistic method is based on a Naïve Bayes classifier and is used to predict the relation between the target project and the new target project. In the CF method, a usability score is generated for each project by making use of classification techniques, and the CB method calculates the matching score of candidate projects by using the K-dimensional tree. Finally, both the methods are combined by parallelized hybridization design, and weights for the proposed method are estimated with an empirical bootstrapping method. The score generated by the proposed hybrid technique is then used to identify the most suitable candidate project for the new project. The experimental results show that the suggestion list of the best three candidate projects is consistent when employing different classifiers. The recommendation performance is evaluated in terms of F-score and Mean Average Precision (MAP), and the proposed method has shown improved performance as compared to the existing methods in both terms.

An Integrated Fuzzy MCDM Method for the Evaluation of R&D Projects

Research and development (R&D) activities are essential to guarantee continuity of firms, meet customer requirements and keep ahead in competition. R&D project selection constitutes a significant part of project management in order to achieve the desired results and outputs. In this study, an integrated fuzzy multi-criteria group decision making approach is developed for R&D project selection. The problem includes a hierarchical structure of the criteria, uncertainty in evaluating the relative importance of criteria/sub-criteria and rating of candidate projects. The method employs the ordered weighted average (OWA) operator as the aggregation operator, which helps to fully reflect the real behavior of the decision makers in group decision making problems. Fuzzy integral method, which does not require the assumption of the mutual independence of criteria, is used to rank the alternatives. The case study is conducted in a small-sized company in Turkey, which designs and produces special purpose machines. A R&D project selection model is developed to maximize the desired outputs. The results of the analysis show that technological, environmental, marketing, organizational, national and financial issues should be considered simultaneously in the evaluation process. The proposed method is shown to be efficient, generalizable and practical and it has several significant merits compared to the other methods.

A New Decision-Making Strategy for Techno-Economic Assessment of Generation and Transmission Expansion Planning for Modern Power Systems

Planning for the intensive use of renewable energy sources (RESs) has attracted wide attention to limit global warming and meet future load growth. Existing studies have shown that installing projects such as transmission lines, energy storage systems (ESSs), fault current limiters, and FACTs facilitate the integration of RESs into power systems. Different generation and transmission network expansion planning models have been developed in the literature; however, a planning model that manages multiple types of projects while maximizing the hosting capacity (HC) is not widely presented. In this paper, a novel planning framework is proposed to enhance and control the HC level of RESs by comparing various kinds of renewables, ESSs, fault current limiters, and FACTs to choose the right one, economically and technically. The proposed problem is formulated as a challenging mixed-integer non-linear optimization problem. To solve it, a solution methodology based on a developed decision-making approach and an improved meta-heuristic algorithm is developed. The decision-making approach aims to keep the number of decision variables as fixed as possible, regardless of the number of projects planned. While an improved war strategy optimizer that relies on the Runge-Kutta learning strategy is applied to strengthen the global search ability. The proposed decision-making approach depends primarily on grouping candidate projects that directly impact the same system state into four separate planning schemes. The first scheme relies on the impedance of devices installed in any path to optimally identify the location and size of the new circuits and the series-type FACTs. The second scheme is based on optimally determining the suitable types of ESSs. On the other hand, the third scheme optimizes the reactive power dispatched from the ESSs and shunt-type FACTs simultaneously. The fourth scheme is concerned with regulating the power dispatched from different types of RESs. All of the simulations, which were carried out on the Garver network and the 118-bus system, demonstrated the ability of the investigated model to select the appropriate projects precisely. Further, the results proved the robustness and effectiveness of the proposed method in obtaining high-quality solutions in fewer runs compared to the conventional method.

A Hybrid Particle Swarm Optimization-Tuning Algorithm for the Prediction of Nanoparticle Morphology from Microscopic Images

Aggregated nanoparticle structures are quite ubiquitous in aerosol and colloidal science, specifically in nanoparticle synthesis systems such as combustion processes where coagulation results in the formation of fractal-like structures. In addition to their size, morphology of the particles also plays a key role in defining various physicochemical properties. Electron microscopy based images are the most commonly used tools in visualizing these aggregates, and prediction of the 3-dimensional structures from the microscopic images is quite complex. Typically, 2-dimensional features from the images are compared to available structures in a database or regression equations are used to predict 3-dimensional morphological parameters including Fractal dimension and pre-exponential factor. In this study, we propose a combination of evolutionary algorithm and forward tuning model to predict the best fit 3-dimensional structures of aggregates from their projection images. 2-dimensional features from a projection image are compared to the candidate projections generated using FracVAL code and optimized using Particle Swarm Optimization to obtain the 3-dimensional structure of the aggregate. Various 3-dimensional properties including hydrodynamic diameter and mobility diameter of the retrieved structures are then compared with the properties of the aggregate used to form the candidate projection image, to test the suitability of the algorithm. Results show that the hybrid algorithm can closely predict the 3-dimensional structures from the projection images with less than 10% difference in the predicted 3-dimensional properties including mobility diameter and radius of gyration.

A Practical Application of the Analytic Hierarchy Process and Integer Linear Programming for Fuzzy Front-End Project Selection

Purpose. This study applies a novel approach that integrates AHP with integer linear programming (ILP), to address a gap in management literature regarding the need to consider both sustainability and COVID-19 impact on project selection, with a view to avoid implementation failures. Design/Methodology/Approach. A case study approach involving experts in semiconductor manufacturing was conducted, using the Delphi method, to determine weights of various criteria, including additional new criteria associated with both sustainability and COVID-19 issues considered in the selection decision for four candidate projects. Findings. Integrated results revealed two projects to be selected (projects 1 and 3). Whilst AHP results revealed more information about the ranking of all four projects, the ILP model results complemented the findings by indicating that 2 projects (projects 1 and 3) should be selected, taking account of not only resource constraints but also sustainability issues and customer behavior towards selected projects, influenced by COVID-19 impact. Originality/Value. The value lies in not only proposing a novel framework that integrates AHP with ILP but also adding to our understanding of the importance to incorporate both sustainability and COVID-19 impacts on semiconductor industry project selection, both of which have significance for the industry in terms of maximizing implementation success for selected projects.

Predicting Pavement Structural Condition Using Machine Learning Methods

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement’s structural condition. The aim is to be able to predict whether a pavement section’s structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement’s structural condition.

A hybrid approach to supporting physical-change-based renewal projects selection decisions in high-density city: a case study of Chongqing

Purpose The aims of this paper is to establish an appropriate physical-change-based renewal (PCBR) projects selection mechanism capable of selecting the combination of the PCBR projects that can make up an integrated urban renewal program in high-density cities.Design/methodology/approach The research design follows a sequential integrated methodology that combines the calculation algorithms of Fuzzy Analytic Network Process (Fuzzy-ANP) with Zero-One Goal Programming (ZOGP) to support decisions for the selection of PCBR projects. In the first phase, general criteria for assessing the sustainability performance of PCBR projects were collected from relevant literature. In the second phase, the Fuzzy-ANP was used to identify the priority weights of the candidate projects through clarifying the interdependent degree between the criteria and candidate projects. Finally, ZOGP method was selected as a predetermined number of PCBR projects among candidate projects.Findings The feasibility and effectiveness of this hybrid approach is then verified in a case study of Yuzhong District, Chongqing in China. The results of this study indicate that the integrated method is capable of directing the decision maker toward the best compromising solution of PCBR program that can achieve the maximization of sustainable benefits and allocate limited resources most efficiently.Originality/value The novelty of this paper consists in combining the algorithms of the Fuzzy-ANP method with those of the ZOGP model that serves as an effective analysis tool to address practical decision problems. This is the first hybrid algorithms to make PCBR projects selection decision that reach the maximization of the sustainable benefits, both in economic and socio-environmental terms.

Dredged Material Decision Tool (DMDT) for Sustainable Beneficial Reuse Applications

The Dredged Material Decision Tool (DMDT) was developed by the United States Environmental Protection Agency (USEPA) to allow project managers, stakeholders, and communities to quantify environmental, economic, and social considerations of using dredged material for beneficial purposes. Dredged material may be disposed in a confined disposal facility (CDF); however, this option is unfavorable because of the finite capacity problems these facilities pose. A more sustainable option is to use dredged materials beneficially such as construction material, for habitat restoration, or for brownfield remediation projects. This study demonstrates the applicability of the DMDT to three relevant candidate projects: (1) Dog Beach, Greenwood, and Lee Street Beaches (Evanston, IL, USA); (2) New York-New Jersey Harbor (New York/New Jersey); and (3) Poplar Island (Chesapeake Bay). The DMDT requires the project information and then completion of worksheets with each criteria (biophysical environment, economic, governance, social, and built environment) ranked, weighed, and scored. The DMDT is applied for all potential alternatives and the results are then analyzed to select the best beneficial reuse alternative. It was found that for the beaches in Evanston, the most beneficial option was on-beach placement with hydraulic dredging. The best option for the New Jersey Harbor was found to be using for brownfield and landfill remediation. The best option for Poplar Island was the lateral and vertical expansion of 50% uplands and 50% wetlands. Overall, DMDT is found to be a valuable tool to facilitate the evaluation of multi-criteria based on the project-specific data and help select the best beneficial use alternative for the dredged material.

The Importance of Spiritual Ecology in the Qingyuan Forest Mushroom Co-Cultivation System

The ecological value encapsulated in the term “spiritual ecology” is drawing more and more attention from ethnology, folklore, ecology, and other related disciplines. The custom of respecting and pacifying forest spirits has distinct regional and ethnic characteristics, and many scholars have discovered samples from different studies around the world. Qingyuan County, located in the mountainous region of southwest Zhejiang, is a very typical case of the practice of respecting and pacifying forest spirits. The mushroom-cultivation technology invented by the chthonic people there more than 900 years ago made this the global birthplace of artificial mushroom cultivation. The Qingyuan Forest–Mushroom Co-cultivation System (QFMCS) has been listed as an important agricultural heritage system by the Ministry of Agriculture and Rural Affairs of China, Beijing, China and a candidate project by the Food and Agriculture Organization of the United Nations, Rome, Italy for Globally Important Agricultural Heritage Systems (GIAHS). Additionally, the QFMCS is currently an important part of the Baishanzu National Park under construction. The authors made an in-depth field study in the mountainous areas of Qingyuan and used theoretical methods of ecology, anthropology, and folklore to reveal the function spiritual ecology plays in ecological conservation, forest protection, identity, and the maintenance of community interests. In the “traditional-modern” transformation of Qingyuan County, the practice of respecting and pacifying the Spirits is still being propagated, resulting in expansion and social cohesion.