All projects require a structure to meet project requirements and achieve established goals. This framework is called project management. Therefore, project management plays an important role in national development and economic growth. Project management includes various knowledge areas such as project integration management, project scope management, project schedule management, etc. The article focuses on the resource-constrained project scheduling known as problem so- called the resource-constrained project scheduling problem (RCPSP). The RCPSP is a part of schedule management. The standard RCPSP has two important constraints, resource constraints and precedence relationships of activities during project scheduling. The objective of the problem is to optimize and minimize the project duration, subject to the above constraints. In this paper, we develop a convolutional neural network approach to solve the standard single mode RCPSP. The advantage of this algorithm over conventional methods such as metaheuristics is that it does not need to generate many solutions or populations. In this paper, the serial schedule generation scheme (SSGS) is used to schedule the project activities using an evolved convolutional neural network (CNN) as a tool to select an appropriate priority rule to filter out a candidate activity. The evolved CNN learns according to the eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, etc. The above parameters are the inputs of the network and are recalculated at each step of the project planning. Moreover, the developed network has priority rules which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter an activity from the eligible activities. In this way, the algorithm is able to schedule all project activities according to the given project constraints. Finally, the performance of the Convolutional Neural Network (CNN) approach is investigated using standard benchmark problems from PSPLIB in comparison to the MLFNN approach and standard metaheuristics.

Project management has a fundamental role in national development, industrial development, and economic growth. Schedule management is also one of the knowledge areas of project management, which includes the processes employed to manage the timely completion of the project. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective of the problem is to optimize and minimize the project duration while constraining the resource quantities during project scheduling. There are two important constraints in this problem, namely resource constraints and precedence relationships of activities during project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been developed by researchers to solve the problem, but there is a lack of investigation of the problem using methods such as neural networks and machine learning. In this article, we develop a multi-layer feed-forward neural network (MLFNN) to solve the standard single- mode RCPSP. The advantage of this method over evolutionary methods or metaheuristics is that it is not necessary to generate numerous solutions or populations. The developed MLFNN learns based on eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, percentage of remaining work, etc., which are calculated at each step of project scheduling, and identified priority rules, which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter out an unscheduled activity from the list of eligible activities and schedule all activities of the project according to the given project constraints. Finally, we investigate the performance of the presented approach using the standard benchmark problems from PSPLIB.

This study's objective is to analyze the factors that influence whether or not small and medium-sized enterprises (SMEs) use business intelligence. Based on an exhaustive assessment of the literature, the study offers a model dependent on the diffusion of innovation and augmented with factors expressing the idea of project management maturity (PMM). The research applied structural equation modelling (SEM) to examine data obtained from 112 Jordanian company workers. The findings showed that the adoption of business intelligence has a positive and significant relationship to the complexity, compatibility, and relative advantage of business intelligence; the level of project management maturity has a significant effect on the level of relative advantage, compatibility, and complexity; and the level of project management maturity is significantly associated with the change management and knowledge sharing practices in SMEs. However, we contend that further study has to be carried out, particularly in the context of developing nations, in order to get a comprehensive understanding of how different SMEs may effectively deploy and make use of business intelligence.

The purpose of this paper is to present the relief operations (RO), responding to a sudden, natural, national disaster (SNND) as a multi-mode resource-constrain multi-project scheduling problem (MRCMPSP). A conceptual framework at a strategic level is constructed and the Colombian RO for an earthquake response is shown as an illustrative case. We concluded that RO can be addressed as a MRCMPSP and that for Colombian case, it is a convenient way to board it. Addressing RO as a MRCMPSP allows managers to implement different project scheduling tools successful in other contexts.

Within the context of the financial industry, the purpose of this research is to investigate the influence that management of scope, time, cost, and quality has on the performance of projects and business overall performance in the financial sector. Following the collection and examination of the study materials, a total of 331 samples were deemed appropriate for analysis by means of the PLS software. According to the findings, the implementation of scope, time, cost, and quality management has a direct and beneficial impact on the enhancement of project performance in the organization that deals with financial matters. As an additional point of interest, the project scope has a favorable impact on the performance of the project through the implementation of work breakdown structure, project scope definition, and scope change control. The formulation of a timetable, the estimation of the duration of activities, and the critical path analysis are all ways in which project time can have an impact on the performance of the project. Additionally, the cost of a project has an impact on the performance of the project through the processes of cost estimation, budgeting, and cost control. And finally, project quality effects positively on project performance and business overall performance through Based on the findings, it appears that the successful management of these four knowledge domains greatly improves the outcomes of the project. To be more specific, Scope Management ensures that the company's goals are aligned with the expectations of the organization, Time Management assists in meeting deadlines, Cost Management optimizes budgets, and Quality Management guarantees that high standards are maintained. Based on the findings of the study, it is concluded that combining these areas results in higher project performance, highlighting the crucial importance of these areas in projects involving the financial industry.

Digital transformation is a challenge that also impacts the selection of tools for implementing projects. Which tools are suitable for handling complex digital twins? Project management must respond to this with suitable approaches. The challenge for decision-makers is to choose the right one. Based on literature research and a case study, influencing factors are derived and practice-relevant project management approaches are collected. Furthermore, a decision model is developed that, on the one hand, supports the decision-maker in selecting tools before and during the project, and on the other hand makes empirical values from past projects usable for future decisions. The results show that the number of influencing factors is large, and the approaches are di-verse. In complex projects, this can lead to complex decision-making situations that require appropriate decision models. The developed “Supervised Decision Model – L5” is based on five levels (L): (L1) Building a database; (L2) Derivation of algorithms; (L3) Initial approach selection; (L4) Review of the initial selection; (L5) Using experiences for future decisions. In practice it turns out that complex projects – like Digital Twins - often fail. Modified decision models for selecting suitable approaches should therefore take the following as-pects into account: (a) decision-makers are actively supported in the initial decision phase; (b) initial decisions once made are checked in the early phase of the project and corrected if necessary; (c) the lessons learned are recorded in the database as empirical value and used for future decisions.

Disruptive innovation has attracted much interest, discussion and argument in academia and industry since it was defined. Based on a review of the related research publications, this paper aims to clarify the concept of disruptive innovation on controversies, summarize ex-ante studies on project management for disruptive product innovation, and propose potential research areas of the disruptive product innovation. The ex-ante studies are classified into three aspects: timing and conditions of disruptive innovation, factors for the implementation of disruptive innovation, forecasting and implementation of disruptive innovation. After a review of the existing methods of disruptive innovation, the potential research of disruptive innovation is presented.

Poverty is one of the significant problems, and almost all countries worldwide face poverty issues that negatively impact society. This study is written to investigate the students' knowledge and awareness of poverty and interest level in conducting e-business and garage sales. Also, examine the relationship of knowledge and awareness on students' intention to conduct e-business and garage sales. There were 152 respondents from the same university who participated in this study. The data analyzed using descriptive and inferential statistics performed by SPSS-23 and SmartPLS 3.3.3. The survey showed that the student's knowledge and awareness of poverty are still lacking and need improvement. Half of the respondents said they are not familiar with SDG1, and they were not aware that their university has done an event related to SDG1 and poverty. There were 25% and 85% of students interested in conducting e-business and garage sales that increased their pocket money and reduced poverty rates among university students. Overall, this study's result shows a positive response from all respondents, and most of them have general ideas of poverty. In addition, this study has identified that the variable knowledge and awareness have a significant positive effect on student's intention to conduct e-business and garage sales.

The purpose of this study is to determine the critical success factors (CSF) of IT projects in Pa-kistan. The identified factors cannot only be functional to exact type IT projects but also to all types of IT projects, their success directly affects the achievement of whole organization. The proposed study of this paper has determined 15 factors influencing the most on the success of IT projects through multiple regression analysis. The survey has disclosed that many CSFs were found related to IT projects but these 15 factors are also the backbone of IT projects. The re-search results obtained clearly indicated that the Leadership Qualities played a significant role in obtaining Top Management support in order to access to resources however, the Leadership Qualities did not play any role on the trained and capable Project Team Members. Besides, it is undoubtedly defined effective communication of the project was established to be influential on the conclusion and contributing factor towards the Success of IT projects in Pakistan. Top Man-agement Support as a whole was not found to play a key role in the IT Project Success.

In this paper, a new hybrid AHP and Dempster-Shafer Theory of Evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, four groups of risk factors, including Properties, Operational and Technological, Financial, Strategic risk factors, are considered. Then using an L24 Taguchi method, several experiments with various dimensions have been designed and solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index (VI), Reduced Risk Indicator (R.R.I%), and Solving time. The findings indicated that, compared to the classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.