Optimal Project Selection Research Articles

Enhancing IT Project Portfolio Management Through Dynamic Dashboard Visualization and Integrated Chatbot and Mathematical ML Model for Strategic Decision Making

This project paper presents an innovative approach to enhance IT Project Portfolio Management (IT PPM) using dynamic dashboard visualization and machine learning (ML) techniques. Effective IT PPM is essential for organizations to align IT projects with business objectives and optimize resource allocation. However, conventional methods often lack innovation and real-time visibility, leading to suboptimal decision-making and costly software investments. The objective of this project is to develop a dynamic dashboard visualization system integrated with a machine learning model derived from the Meta-Portfolio Method (MPM). The MPM leverages advanced analytics and historical project data to dynamically allocate resources and prioritize projects based on risk-return profiles. By incorporating the MPM into the dashboard, organizations gain real-time insights into project status, progress, and resource allocation, enabling informed decision-making and proactive portfolio optimization. The ML model derived from the MPM analyzes historical project data, including success rates, resource intensity, criticality, regulatory compliance, and portfolio diversification, to predict optimal project selection and resource allocation. By dynamically adapting to changing market conditions and project dynamics, the ML model facilitates strategic decision-making and enhances portfolio performance. Through the integration of dynamic dashboard visualization and ML-driven decision support, organizations can streamline IT PPM processes, improve project outcomes, and achieve strategic objectives effectively and efficiently. This innovative approach empowers organizations to navigate the complexities of IT project portfolios with agility, foresight, and confidence.

Optimal performance selection of sustainable mobility service projects based on IFSS - Prospect theory - VIKOR: A case study of electric vehicle sharing program.

Current mobility trend indicates that the number of private cars will decline in the near future. One of the reasons for this trend is the development of Mobility as a Service (MaaS), which in conjunction with information and communication technologies (ICT) drive the application of transport services in smart city, respond to environmental issues, and provide users with reliable mobility. Electric vehicle sharing (EVS) travel has been regarded as a feasible mainstream model of sustainable mobility services in the future, which can effectively improve the utilization rate of motor vehicles, solve the problems of traffic congestion, environmental pollution and urban land, and promote low-carbon and sustainable development. To help electric vehicle operators improve service quality, the establishment of EVS program service performance evaluation is an urgent problem to be solved. Based on this, this paper firstly constructs the evaluation index system from 5 aspects: electric vehicle, charge station, user experience, payment and intelligent services through literature review and Delphi method. Secondly, the criteria importance though intercriteria correlation (CRITIC) and the improved G1 method are introduced to overcome the shortcomings of the single method, and the combined weights are calculated by the multiplication normalization method. Finally, a decision model based on intuitionistic fuzzy soft set (IFSS)-prospect theory and VIse Kriterijumski Optimizacioni Racun (VIKOR) method is constructed to select the best service performance of EVS program, and its feasibility and effectiveness are verified by sensitivity analysis and comparative analysis. The result shows that EVCARD is the best performing EVS program, and shared electric vehicle and charge station are the key factors to be considered in the selection. This study provides scientific and feasible guidance for the optimal service performance selection of EVS programs, which is of great significance for users to choose EVS programs.

МЕТОДЫ АНАЛИЗА СОВОКУПНОСТИ ИСТОЧНИКОВ ИНВЕСТИЦИЙ И КОММЕРЧЕСКИХ ИДЕЙ

The successful development of an enterprise in a market economy is inextricably linked to the process of long-term investment. Investment activity is an essential part of the functioning of any organization. A very important place in the analysis of an investment project is given to the study of economic efficiency, which consists in measuring (evaluating) its final financial results. Economic efficiency in the most general sense is a comparison of the results of economic activity with the resources spent on this activity: labor, material, natural. Currently, many enterprises carry out long-term investments, however, this activity is not the main one for them. In this regard, there is a need to receive advice and assistance for the correct choice of a commercial idea and its analysis. The article contains theoretical aspects of the development of a project support strategy, namely, the essence of investment activity and the content of the investment strategy are considered, as well as the methods used in its construction are analyzed. To justify the choice of investment decisions, homogeneous classes of types of credit and business projects are distinguished, and methods of optimal project selection are considered. The main provisions, an indicator for evaluating the work of marketing, sales and business for quantitative and qualitative scales (distance), methods and quality functions are defined. The authors also analyzed the selected factors and identified the first four main components, the totality of which determines 70% of the labor productivity level of workers employed in the field of material production. The study presents specific data on the rating of regional agricultural organizations in the Orenburg region.

Optimal operation mode selection of sponge city PPP projects using IFSS-prospect theory and VIKOR approach: a case study in China.

The construction of sponge city (SC) public private partnership (PPP) projects is considered to be a feasible measure to solve the "water problems" in China. To ensure the success of SC PPP projects, how to select an appropriate operation mode for SC PPP projects is an urgent problem to be solved. In this paper, first, the evaluation index system of operation mode is established from the aspects of project characteristics, government capabilities, project benefits, project implementation, and cooperative partnership. Second, the DEMATEL method and entropy method are introduced to overcome the drawbacks of a single method, and the combination weights are calculated by game theory. Finally, the decision-making model based on intuitionistic fuzzy soft set (IFSS)-prospect theory and VIKOR approach is constructed to select operation mode for SC PPP projects. In this paper, the framework is applied to select an operation mode for the Qingshan demonstration zone project, and its feasibility and effectiveness are verified through sensitivity analysis and comparative analysis. The results show that BOT is the optimal operation mode for SC PPP projects, and government capabilities and project implementation are critical factors to be considered when selecting an operation mode. This study can provide guidance to the government in the OMS of SC PPP projects and improve the sustainability of the projects.

Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

RETRACTED ARTICLE: The optimal project selection in portfolio management using fuzzy multi-criteria decision-making methodology

The main purpose of the paper is to provide and apply the concept and techniques of multi-criteria decision-making under fuzzy environment in the prioritization and selection of projects in a portfolio management. In this study, the preference weights of the criteria were identified using fuzzy AHP. Then, the weights are embraced in fuzzy TOPSIS to improve the gaps of projects (alternatives) to achieve the organization objectives as well as interactions between projects. Twenty Iraqi Oil Company projects were evaluated against five key criteria. The results showed that in fuzzy TOPSIS technique the measurement of criteria weights is important and they could adjust the ranking for other projects as well as figure out the best project to achieve the desired levels. This research as expected will serve as a helpful tool for stakeholders in improving the quality level of portfolio management projects.

AGREEMENT CONCEPT FOR DECISION-MAKING IN MANAGING PROJECT OF RESIDENTIAL REPAIR BASED ON THE MULTI-MODEL ASSESSMENT MECHANISMS AND FORECASTING PARAMETERS

Decision-making concept managing facilities tasks in field of housing and communal services is discussed. This concept characterized by the human factor significant consideration. This approach can affect the class socio-economic systems effectiveness level. The optimal project selection is based on the coordination and consideration the preferences of all interested parties for changing property state. Mechanism application for the comprehensive assessment of characteristics in a multifactorial space, which are subject to optimization in the presence of restrictions is based by the selection process, The operating procedure of the organizational system for managing housing repair projects is considered. A conceptual model for coordinating decision-making in the managing housing repair projects tasks is formulated, its main provisions are presented. The concept use original mechanisms for reconciling the interests of key decision-makers in various stages of repair and restoration work project management. Estimating and predicting mechanisms of the estate object parameters, due to in fact they form the information basis of all targeted choice subsequent steps. In the second part of the article they are in more detail presented. Matrix of direct and transitive relations of coordination between the active elements of the organizational system under consideration has been developed for systematize the required coordination mechanisms. Transitive relationships can be multivariate in depending on the contextual complexity of the organizational system, it raises the question of target choice on a variety of alternatives, that differ in the degree of influence on the organizational system and the complexity of implementation. Possible options are formulated for the problem of estimating the characteristics parameters of real estate building structures. Fuzzy active examination procedure is formulated for cases where the statements of experts may differ from each other in a subjective attitude to the quantitative assessment of the results of visual observation, based on generalized median schemes, characterized by a more accurate establishment of the agreed results of characteristics of building structures assessing in conducting technical expertise process. Possible options are formulated for the problem of the forecasting characteristics parameters of real estate building structures. For each of the three options for setting the problem is illustrated the procedure for building structures parameters predicting changes. Modification of the extrapolation method is proposed, implemented by Steve Jobs taking combining creativity and manufacturability according into accounts the logistic physical wear curves.

APPLYING A CONTINGENCY APPROACH TO PROJECT MANAGEMENT WITHIN MESO-LEVEL PUBLIC-PRIVATE PARTNERSHIPS

This paper investigates the application of a contingency approach to managing meso-level public-private partnership projects. It is argued that situational management in implementing public-private partnership projects should rely on the so-called risk-contributing factors. It is emphasized that the contingency approach in the context of public-private partnerships is based on developing a set of indicators and integral index calculation used to rank both the projects themselves and their application in terms of territorial and sectoral aspects. It is proved that the integral coefficient at the meso-level reflects the most favorable environment for doing business, including basic frameworks for realization of public-private partnership projects. According to the calculations carried out within this study, at the regional level by selected indicators, the Mykolayiv region has demonstrated the most favorable conditions. Accordingly, the Donetsk region has the highest level of transaction costs and the highest rate of project implementation risk, i. e. the absolute risk value makes up 0.482. The high risk index for this region stems from military activities, low infrastructure development, lack of sufficient production capacity and, consequently, significant commercial and administrative costs for current operations. Thus, it is concluded that the assessment and multilevel analysis based on integral indicators, absolute and relative risk indicators are sufficient for the optimal selection of public-private partnership projects upon the criterion "the lowest project implementation risk – the greatest effects in different areas of activity.

English

The purpose of this paper is to investigate the factors that influence decision making in the selection of projects in renewable energy sector of Zambia. Project selection and development of a portfolio of projects in line with corporate strategy is an important task of decision makers in the renewable energy sector of both public and private organizations. The selected projects have to meet the appropriate time frame for completion and delivery, a suitable risk profile and other distinct factors in order to pursue corporate objectives successfully. A modified Delphi approach was applied in this study to investigate the factors that contribute to optimal selection of projects in the renewable energy sector of Zambia. The significance of the findings in this research established which critical factors decision makers must be considered when building up their project portfolio and considering the value of their investment. Key words: Project selection, renewable energy, strategic decision making, Zambia.

Optimal investment decision making on the model of production enterprise with limited resources

Investments are among the most important factors of national economic growth. Selection of optimal investment project is the first priority for any enterprise with limited financial resources. This study is dedicated to a choice among mutually exclusive projects, which are impossible to complete partially, so, one project must be chosen and all others must be rejected. An investor must find among all possible projects the one that allows to better achieve all investor’s aims. A mathematical model of multi-purpose multi-criteria investor decision making is proposed for investment project selection problem. Efficiency and riskiness of studied projects are evaluated using such indicators as profit, rate of return, payback period, marginal cost of capital, also taking into account subjective characteristics, namely the investor’s attitude towards financial risks, importance assessment of decision making criteria, etc. Decision making assessment methods for the situations of risk and uncertainty are applied to resolve the problem of optimal project selection, such as Wald’s pessimistic criterion, maximax optimistic criterion, as well as Hurwicz’s, Laplace’s, Bayes- Laplace, Hodges-Lehmann criteria, and Savage’s minimax risk criterion. Calculations carried out and results obtained indicate that the best investment project chosen that way will provide the highest absolute profit, despite certain disadvantages such as lower rate of return, longer payback period and higher risk than other projects.

Sustainable project selection: Optimal project selection considering sustainability under reinvestment strategy

Reflecting the call being made by the United Nations Environmental Protection and society to adopt innovative approaches to resolve current sustainability challenges, there is a strong need for studies that incorporate sustainability concerns in decision-making processes. Yet the quantitative method of measuring sustainability in project investment practice is still limited. Besides, reinvestment is seldom considered. In light of this, the authors have proposed an integrated novel optimization approach in which sustainability cost is quantitatively quantified and reinvestment strategy is adopted. Our method creates win-win situation which prevents environmental damages and in the meantime efficiently allocates budget and maximizes the investors' income. A case study is presented to illustrate the applicability of the proposed method and the impact of sustainability measure with reinvestment strategy incorporated in project selection decision-making processes. Expected net present value is employed to calculate the return of the project investment. Based on demonstrated optimization results, some significant observations have been got: (1) range of sustainability cost significantly influences investors' objective value and optimal project selection decision. Investors' project selection decision and maximum objective value is obtained at 3% sustainability cost; (2) compared to traditional project selection practices, consideration of sustainability can maximize investors' net profit value; (3) objective value varies with different reinvestment strategies and it is more sensitive with total reinvestment strategy than with partial reinvestment strategy; (4) appropriate use of reinvestment strategy ensures efficient budget allocation to the projects for the effective implementation of project investment without exceeding investors' available capital even when additional 3% cost on sustainability is employed. The results verify the proposed methodology.

Optimal Selection of Overseas Oil Development Projects of Sinopec Based on a Multi-Objective Programming Model*

With the continuous low oil price, the national oil companies in China are under the pressure of lowering the development cost and improving their investment efficiency of their overseas investments. Optimal methods are needed in screening the overseas assets within the constraint of investment. This paper presents a multi-objective integer programming model for the optimal selection of overseas oil development projects for Sinopec based on the economic evaluation of various projects in different countries and regions. Combination of economic parameters and practical constraints are discussed and presented in the mathematical models. Application of the model shows its advantages over the traditional screening and ranking methods.

Optimizing project selection: a perspective from resource constraints

In reality, projects usually consume complex resources. Making good use of the various resources is vital for optimal project selection and maximum profit earning. This paper proposes a new project selection model from the perspective of complex resource constraints. In the model, the resources are divided into non-renewable and renewable categories, and some resources of the two categories can both be shared by different projects. In addition, the paper considers the situation where the company has resources in stock and can purchase them in the marketplace if they are out of stock. The paper proves that the proposed model which considers renewable resource and resource sharing produces higher profit than the ones that do not consider renewable resource and resource sharing. To solve the complex model problem, an improved genetic algorithm is presented. For the sake of illustration, a case study is provided.

Model for Optimal Selection of Projects to Improve Running Time and Operating Cost Efficiency on Passenger Rail Corridors

Recent proposals for expanded intercity passenger rail service in the United States have included plans for incremental improvements to existing Amtrak service. Improvements to existing services aim to accommodate faster and more frequent passenger train operation, generally on track owned and operated by freight railways. Various projects and approaches can be considered when the running time of passenger trains is being decreased on a particular corridor. Raising the maximum operating speed can yield different benefits on different sections of the route, and conditions on adjacent sections can interact. For instance, the marginal travel time benefit of improving segments of a line from a maximum speed of 79 to 110 mph is less than the benefit of other improvements to eliminate segments currently restricted to lower speeds. Therefore, to maximize the potential of limited resources, project investments must be selected carefully to improve performance in a cost-effective manner. This paper presents a methodology for optimally selecting projects or establishing program budgets to reduce running time on a passenger rail corridor with consideration of capital, maintenance, and operating costs. The proposed project selection model is formulated with genetic algorithms. In the model, a route is divided into sections that can be independently upgraded, and the objective function is formulated as minimization of running time along the route. This model can aid in quickly and efficiently developing a strategic plan for improving running time on passenger rail corridors.

Uncertain mean-variance and mean-semivariance models for optimal project selection and scheduling

This paper discusses a joint problem of optimal project selection and scheduling in the situation where initial outlays and net cash inflows of projects are given by experts’ estimates due to lack of historical data. Uncertain variables are used to describe these parameters and the use of them is justified. A new mean-variance and a mean-semivariance models are proposed considering relationship and time sequence order between projects. In order to solve the complex problems, the methods for calculating uncertain lower partial semivariance and higher partial semivariance values are introduced and a hybrid intelligent algorithm which integrates genetic algorithm with cellular automation is provided. In addition, two examples are presented to illustrate the application and significance of the new models, and numerical experiments are done to show the effectiveness of the proposed algorithm.

The effect of spatial interdependencies on prioritization and payments for environmental services

Empirical studies and on-the-ground policies assessing optimal selection of projects in the context of payments for environmental services programs rarely consider spatial proximity of one project to other projects. This occurs despite evidence from theoretical and ecological studies that benefits are often spatially interdependent. This paper develops a flexible construct of “spatial synergy benefits” using the principles of Newtonian gravity similar to efforts in other application areas. This approach is novel to the literature on environmental preservation and, as a systematic method, can account for a wide variety of spatial interdependencies. The empirical setting for the application is farm and forest preservation in Delaware, with a quadratic knapsack algorithm used to select the optimal set of parcels. Application results show that the specific level of the spatial synergy benefit measure does not significantly alter the number of parcels and acreage preserved, but that the composition of the optimal set changes as agglomeration preferences increase. These changes in the optimal targeted set indicate a potential bias in past research on PES selection. Policy makers informed by methods that do not explicitly account for spatial agglomeration preferences often make incorrect investment choices from a cost-effectiveness perspective.

Time-dependent discrete road network design with both tactical and strategic decisions

This paper aims to model and investigate the discrete urban road network design problem, using a multi-objective time-dependent decision-making approach. Given a base network made up with two-way links, candidate link expansion projects, and candidate link construction projects, the problem determines the optimal combination of one-way and two-way links, the optimal selection of capacity expansion projects, and the optimal lane allocations on two-way links over a dual time scale. The problem considers both the total travel time and the total CO emissions as the two objective function measures. The problem is modelled using a time-dependent approach that considers a planning horizon of multiple years and both morning and evening peaks. Under this approach, the model allows determining the sequence of link construction, the expansion projects over a predetermined planning horizon, the configuration of street orientations, and the lane allocations for morning and evening peaks in each year of the planning horizon. This model is formulated as a mixed-integer programming problem with mathematical equilibrium constraints. In this regard, two multi-objective metaheuristics, including a modified non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective B-cell algorithm, are proposed to solve the above-mentioned problem. Computational results for various test networks are also presented in this paper.

A mean-risk index model for uncertain capital budgeting

This paper discusses the capital budgeting problem of projects using annual cash inflows, cash outflows and initial investment outlays given by experts’ evaluations when no historical data are available. Uncertain variables are used to describe the projects’ parameters. A profit risk index and a capital risk index are proposed, and a mean-risk index model is developed for optimal project selection. In addition, the deterministic forms of the model are given and a solution algorithm is provided. For the sake of illustration, a numerical example is also presented. The results of the example show that both profit risk index and capital risk index are important in investment risk control. However, when the profit risk control requirement is strong, the selected project portfolio may be insensitive to the capital risk constraint; when the profit risk control requirement is moderate, the capital risk constraint plays an important role. The results also show the tendency that when either the tolerable profit risk level or the tolerable capital risk level becomes higher, the obtained expected net present value of the project portfolio becomes larger, which is in agreement with the investment rule that the higher the risk, the higher the return.

Using real options to determine optimal funding strategies for CO2 capture, transport and storage projects in the European Union

Several projects in the European Union (EU) are currently under development to implement the carbon capture, transport and storage (CCS) technology on a large scale and may be subject to public funding under EU support initiatives. These CCS projects may develop any combination of three types of operating levels: pilot, demonstration and full-scale, representing progressing levels of electric power generation capability. Several projects have commenced at the demonstration level, with full-scale commercial levels planned for approximately 2020.Taking the perspective of a funding agency, we employ a real options framework for determining an optimal project selection and funding strategy for the development of full-scale CCS plants. Specifically, we formulate and solve a stochastic dynamic program (SDP) for obtaining optimal funding solutions in order to achieve at least one successfully operating full-scale CCS plant by a target year. The model demonstrates the improved risk reduction by employing such a multi-stage competition. We then extend the model to consider two sensitivities: (1) the flexibility to spend that budget among the time periods and (2) optimizing the budget, but specifying each time period's allocation a priori. State size and runtimes of the SDP model are provided.

Optimal project adjustment and selection

These days companies are competing in a fast changing environment. To keep its competitiveness, the company needs not only to seek and select new investment opportunities but also to adjust its existing projects. This paper discusses an optimal project selection and adjusting problem under capital and land resource limitations. Due to the complex and dynamic nature of the economic environment, the project parameters such as initial outlays, upgrade expenditures and net cash flows are treated as random variables. Net present value method is employed to calculate the investment return, and a mean–variance optimal adjustment and selection model is developed. To solve the proposed optimization problem with big number decision variables, a cellular binary particle swarm optimization which hybridizes cellular automation and particle swarm optimization is proposed. As an illustration of the proposed algorithm, a numerical example is also presented.