One of the most critical factors used to evaluate the efficiency of the portfolio selection process is the ability of the model to measure interdependencies among projects. Varieties of interactions among projects lead to several kinds of synergies in the whole portfolio, such as re-sources and knowledge interdependencies. There are few studies focused on project portfolio selection accompanied by modeling and estimating the impact of synergies between projects. Hence, this paper presents a model to select the best project portfolio applying a particular model to measure the effects of several types of interdependencies between paired projects. Then, the Promethee II method is used to prioritize projects. Then, the portfolio selection model, which is a non-linear integer model, is solved to find the best set of projects. Finally, numerical examples are addressed to illustrate the method results and validity.

This study aims to identify and evaluate the perception of major stakeholders on factors causing International Development Project (IDP) failure in the context of Afghanistan. The study adopts a quantitative cross-sectional survey research design. Thirty significant IDP failure factors included in the questionnaire were identified and shortlisted through literature reviews and validated by experts and IDP management practitioners. The survey was conducted using a structured questionnaire to investigate the most significant IDP failure factors, and various statistical tools were employed to evaluate the perception of the survey respondents. RII was used to examine the relative importance index of each failure factor. The failure factors were then grouped into five categories: Financial constraints, Ineffective recruitment, External forces, Project leadership, and Project management practices using EFA. The findings of the study will help the international development community and their IDP implementing partners, INGs and project management practitioners manage IDPs proactively and mitigate the risks of project failure. It will also contribute to the IDP management body of knowledge. The research is the first of its kind to examine the possible factors causing IDP failure in Afghanistan.

Entrepreneurial orientation is found to influence Project success along with the moderation of adaptive organizational culture and people centered management in most of the cases. This study, by addressing the moderation brings clarity to the assessment of the relation of entrepreneurial orientation dimensions on project success and is a contribution to the advancement of knowledge enhancement and improvement in Pakistan. A predominantly quantitative research strategy supported by qualitative inputs employed to obtain empirical data from 256 corporate firms in Punjab Pakistan. The gathered data examined by the structural equation modeling through Partial least square method. The results indicate that innovativeness, risk taking and proactiveness reflect the entrepreneurial orientation components. The findings of this study conclude that three dimensions of entrepreneurial orientation Innovation, Risk-taking and Proactiveness have significant and positive influence on project success. When these three dimensions of entrepreneurial orientation are moderated with adaptive organizational culture and People-Centered Management shows moderate to strong relations between different variables with a few insignificant results.

The objective of this study is to understand and evaluate the interactions of the Green Supply Chain Management enablers from a construction project's perspective in Imo State, Nigeria. This paper discusses the mix of practical intuition and determination through an interpretive structural modeling (ISM)-driven methodology. Eight (8) enablers were identified from a literature review, expert consultation, and real-world examples. While Matrice d’Impacts croisesmultipication applique a classement analysis (MICMAC) was used to identify dependence and driving power, it was used as a way to understand the relationship between the enablers. The study found that strong, yet fragile, forces drive GSCM adoption, with enhanced awareness of GSCM, increased market appeal for green construction projects, and government support through incentives and tax rebates.

The boom of real estate has been a motivating force for economic growth in India for the past few years. However, the Real Estate (RE) market of India is still in the embryonic phase and juvenile. In the context of the real estate projects, it is a general situation observed that such projects cannot meet the target as the Indian real estate companies are deficient in scientific management technology to confront the risks. The research paper aims to focus mainly on constraints and demurral of Risk Management (RM) in RE firms of India to investigate findings for the same in the Indian Real estate market and further focusing on RE projects of Ahmedabad. The paper is mostly founded on an overview of individuals who are straightforwardly or firmly identified with the administration and the RE business in India. The questionnaire survey shall be targeted over the five prime territories of Ahmedabad. This research further highlights to concerned identified primary critical risk factors (by Criticality Index Method) influencing the residential real estate market and then developing a framework for assessing the factors carrying out the quantitative analysis using various analytical methods of SPSS software, Factor analysis, ANOVA and Post-Hoc Test. The validation of the results has been done through a survey of experienced experts. The critical risks identified based on the questionnaire survey are modeled through the decision tree diagram.

In the context of globalization and fierce competition in world markets, the high level of investment activity in the country is a key to economic and innovative development. The high level of wear and tear of fixed assets in developing countries gives special relevance to solving the problem of attracting investments for production development. Hence, for the investment management system choosing an optimal variant among several available investment projects is one of the most responsible stages of ensuring the stable operation and sustainable development of an enterprise. In this regard, the aim of the article is to develop a comprehensive multi-criteria approach to choose the best investment option. The article analyzes the existing methodological approaches to assess the economic efficiency of the investment projects, identifies their advantages and disadvantages. A multi-criteria method of investment project evaluation is proposed, which is characterized by the absence of restrictions on the number of individual evaluation indicators and the possibility for the investor to determine the significance of every indicator using weights independently. The use of the proposed methodology by enterprises will improve the quality of management decisions at the stage of choosing the optimal investment option.

This paper was retracted based on the authors' request due to errors in the computations.

The study examines the impact of official development assistance (ODA) in constructing road transport infrastructure on Vietnam’s economic growth. The authors select gross domestic product (GDP) to represent economic growth and test the influence of ODA in constructing road traffic infrastructure on Vietnam's GDP. Based on the references and analysis of previous studies, the authors propose an impact assessment model of ODA in constructing road transport infrastructure, Foreign direct investment (FDI), Domestic Investment (VDT) and Labor Force (Labor) to economic growth through GDP as a dependent variable. The regression results show that the ODA had a positive impact on GDP. Moreover, ODA plays an important role in constructing road transport infrastructure on Vietnam’s economic growth.

Bidding price decision is a key issue for the contractors and construction companies. The success/failure of the contractors in competitive biddings is directly dependent on their bidding strategy. This paper aims to develop a hybrid statistical and mathematical modeling approach for determining the optimum bidding price in construction projects. By statistical analysis of historical data, some uncertain parameters like the number of competitors and the cost of the project are estimated. Then, a scenario-based mathematical model for bidding price decision is proposed. In order to present a model in more accordance with the real-world situations, factors like risk, minimum acceptable rate of return (MARR) and opportunistic behavior are taken into account. In order to achieve an insensitive solution to the change in the realization of the input data from the scenarios, a robust mathematical model is used. The performance of the model is evaluated through some numerical problems. Furthermore, sensitivity analysis of the key parameters and robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model's effectiveness in real-world situations, a case study is analyzed by the proposed approach. Numerical results show that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model in a real-world situation.

For the Government projects, Value for Money (VFM) is an important parameter based on which the projects are procured through Public-Private Partnership (PPP) by the public sector. The objective of this paper is to identify and evaluate perceptions of VFM factors affecting Indian road projects undertaken by PPP. Eighteen VFM factors were shortlisted through a literature survey and were validated with experienced professionals. A questionnaire survey was conducted to investigate the most important VFM factors and was grouped into three categories: financial implications, the expertise of the private sector, and contract efficiency using factor analysis. Important VFM factors were identified using the Importance Index Method. The perception was analyzed with various statistical tools. The findings from this study provide more information and understanding about VFM in an early stage, which will be helpful for decision making about PPP.

Industry 4.0 is a modern approach that aims at enhancing the connectivity between the different stages of the production process and the requirements of consumers. This paper addresses a relevant problem for both Industry 4.0 and flow shop literature: the missing operations flow shop scheduling problem. In general, in order to reduce the computational effort required to solve flow shop scheduling problems only permutation schedules (PFS) are considered, i.e., the same job sequence is used for all the machines involved. However, considering only PFS is not a constraint that is based on the real-world conditions of the industrial environments, and it is only a simplification strategy used frequently in the literature. Moreover, non-permutation (NPFS) orderings may be used for most of the real flow shop systems, i.e., different job schedules can be used for different machines in the production line, since NPFS solutions usually outperform the PFS ones. In this work, a novel mathematical formulation to minimize total tardiness and a resolution method, which considers both PFS and (the more computationally expensive) NPFS solutions, are presented to solve the flow shop scheduling problem with missing operations. The solution approach has two stages. First, a Genetic Algorithm, which only considers PFS solutions, is applied to solve the scheduling problem. The resulting solution is then improved in the second stage by means of a Simulated Annealing algorithm that expands the search space by considering NPFS solutions. The experimental tests were performed on a set of instances considering varying proportions of missing operations, as it is usual in the Industry 4.0 production environment. The results show that NPFS solutions clearly outperform PFS solutions for this problem.