Search results

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

Assessing project criticality in a project portfolio (PP) is of great practical significance to improve robustness from damage. While project criticality assessment has increased diversity in approaches, the understanding of vulnerable project impacts is still limited. To promote a better understanding of assessing project criticality, a vulnerability measurement model is constructed.

First, integrating the tasks, projects and corresponding relationships among them, a project portfolio network (PPN) is constructed. Second, the project's vulnerability is measured by combining the topological structure and functional attributes. Third, project criticality is assessed by the vulnerability measurement results. Lastly, the proposed model is applied in a numerical example to illustrate its suitability and effectiveness.

For academia, this study provides a novel perspective on project vulnerability measurement and expands project criticality assessment tools. For practitioners, the straightforward model provides an effective tool for assessing project criticality and contributes to enhancing project portfolio management (PPM).

The impact of the task on the project is considered in this study. Topological structure and functional attributes are also integrated for measuring project vulnerability due to the impact of random attacks in an uncertain environment, providing a new perspective on the requirements of project criticality assessment and the measurement of project vulnerability.

Selecting and scheduling optimal project portfolio simultaneously is a complex decision-making problem faced by organizations to realize the strategy. However, dynamic synergy relationships among projects complicate this problem. This study aims at constructing a project portfolio selection and scheduling (PPSS) model while quantifying the dynamic synergetic effects to provide decision support for managing PPSS problems.

This study develops a mathematical model for PPSS with the objective of maximal project portfolio benefits (PPBs). To make the results align with the strategy, comprehensive PPBs are divided into financial and non-financial aspects based on the balanced scorecard. Then, synergy benefits evolve dynamically in the time horizon, and system dynamics is employed to quantify them. Lastly, a case example is conducted to verify the applicability of the proposed model.

The proposed model is an applicable model for PPSS while incorporating dynamic synergy. It can help project managers obtain the results that which project should be selected and when it should start while achieving optimal PPBs.

This study complements prior PPSS research in two aspects. First, financial and non-financial PPBs are designed as new criteria for PPSS, making the results follow the strategy. Second, this study illuminates the dynamic characteristic of synergy and quantifies the synergetic effect. The proposed model provides insights into managing a PPSS effectively.

Realizing project portfolio benefits (PPBs) is considered a key challenge faced by enterprises. This challenge can largely be attributed to an unclear understanding of the factors influencing PPBs. However, synergistic relationships create complexity for the management of influencing factors. In response to this dilemma, the objective of this study is to quantitatively investigate the factors influencing PPBs while considering the synergistic effect among factors to provide guidelines for benefits management.

Through an integration of the synergy degree of the composite system model and social network analysis (SNA), a refined model is proposed to explore the factors influencing PPBs. First, a list that includes financial and nonfinancial influencing factors is clarified. Then, the corresponding network links, which represent the synergistic relationships among the factors, are innovatively assessed based on the synergy degree of the composite system. Finally, the influencing factor network is analyzed using both individual and overall indicators of SNA.

The resulting evidence demonstrates that four critical influencing factors exist, namely, “project managers,” “purchasers,” “development capacity” and “tangible resources.” These factors are relatively important and should be prioritized. Furthermore, the factors are divided into three subgroups: participant, resource and governmental factors. A general observation from the results is that factors that share the same subgroup are more likely to have a synergistic effect advantage, which leads to an increase in PPBs.

The value of this paper lies in its proposition of a quantitative model that can be used to measure and analyze the factors influencing PPBs with synergy considerations. This research contributes to the body of knowledge on benefits management by linking synergy with PPBs. It presents new insights for managers on how PPBs may be effectively managed and promoted from the perspective of influencing factors.

Comprehensive project portfolio risk (PPR) analysis is essential for the success and sustainable development of project portfolios (PPs). However, project interdependency creates complexity for PPR analysis. In this study, considering the interdependency effect among projects, the authors develop a quantitative evaluation model to analyze PPR based on a fuzzy Bayesian network.

In this paper, the primary purpose is to comprehensively evaluate project portfolio risk considering the interdependency effect using a systematical model. Accordingly, a fuzzy Bayesian network (FBN) is developed based on the existing studies. Specifically, first, the risks in project portfolios are identified from the project interdependencies perspective. Second, a fuzzy Bayesian network is adopted to model and quantify the interaction relationships among risks. Finally, the model is implemented to analyze the occurrence situation and characteristics of risks.

The interdependency effect can lead to high-stake risks, including weak financial liquidity, a lack of cross-project members and project priority imbalance. Furthermore, project schedule risks and inconsistency between product supply and market demand are relatively sensitive and should also be prioritized. Also, the validity of this risk evaluation model has been proved.

The findings identify the most sensitive risks for guaranteeing portfolio implementation and reveal interdependency effect can trigger some specific risks more often. This study proposes for the first time to measure and analyze project portfolio risk by a systematical model. It can help systematically assess and manage the complicated and interdependent risks associated with project portfolios.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

Inadequate balancing of resources often results in resource conflict in the multiproject management process. Past research has focused on how to allocate a small amount of resources optimally but has scarcely explored how to foresee multiproject resource conflict risk in advance. The purpose of this study is to address this knowledge gap by developing a model to predict multiproject resource conflict risk.

A fuzzy comprehensive evaluation method is used to transform subjective judgments into quantitative information, based on which an evaluation index system for multiproject resource conflict risk that focuses on the interdependence of multiple project resources is proposed. An artificial neural network (ANN) model combined with this system is proposed to predict the comprehensive risk score that can describe the severity of risk.

Accurately predicting multiproject resource conflict risks in advance can reduce the risk to the organization and increase the probability of achieving the project objectives. The ANN model developed in this paper by the authors can capture the essential components of the underlying nonlinear relevance and is capable of predicting risk appropriately.

The authors explored the prediction of the risks associated with multiproject resource conflicts, which is important for improving the success rate of projects but has received limited attention in the past. The authors established an evaluation index system for these risks considering the interdependence among project resources to describe the underlying factors that contribute to resource conflict risks. The authors proposed an effective model to forecast the risk of multiproject resource conflicts using an ANN. The model can effectively predict complex phenomena with complicated and highly nonlinear performance functions and solve problems with many random variables.