Project Selection Model Research Articles

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 multi-objective mean–semivariance model for project selection using reinvestment and synergy under uncertainty

Project selection problems with imprecise parameters is one of the hot spots that have attracted many scholars’ interest. In this paper, we first propose a multi-objective mean–semivariance model to solve the multi-objective project selection problem considering reinvestment and synergy between projects with different investment and operation periods by applying the uncertainty theory. The investment outlay and profit are treated as uncertain variables with an uncertainty distribution function that are determined based on experts’ evaluations. The objectives are to maximize the expected value of uncertain net present value (NPV) and to minimize its risk (semivariance). The difference between the models taking variance and semivariance as the risk of NPV is compared, and the effects of reinvestment and synergy on project selection are analyzed. We next propose new binary versions of Jaya and Rao algorithms and develop multi-objective binary meta-heuristic algorithms binMOJaya, binMORao1 and binMORao2 to solve the proposed model. The performances of the proposed three algorithms are demonstrated through comparison with other well-known binary multi-objective algorithms for the 10 problems including large-scale problems. Finally, the validity of proposed model is illustrated through an example problem using the zigzag uncertainty distribution. Computational experiments have shown that the proposed uncertain model explains reinvestment and synergy well, and that the proposed algorithms, especially the binMORao2 algorithm, are very suitable for solving the proposed model.

A TWO-STEP APPROACH FOR SELECTION OF RAILWAY MODERNIZATION PROJECTS BASED ON ANALYTIC HIERARCHY PROCESS

This paper presents a simulation of the Project Portfolio Management (PPM) model usage in evaluation and selection of modernization projects of Serbian Railways. Performance review of the selected projects is conducted in regards to the strategic goals of the company, which have been established during the process of strategic planning. The project selection model is methodologically set up as a two-step selection model. The number of portfolio projects is narrowed down by ranking their importance in regards to the success of the modernization program (benefits, risks, and resources), which is followed by the ranking of the projects on the basis of their contribution to the realization of previously chosen liquidity and rentability goals, which are always a challenge when it comes to railway companies. This selection procedure is simulated by applying the Analytic Hierarchy Process (AHP), a mathematical model of linear programming, embedded in the software package “Expert Choice”. The simulation results show that, in this way, it is possible to achieve a considerable drop in the amount of necessary financial resources for the realization of modernization projects, which is also the main contribution of this paper. This proves the efficiency of the PPM concept application as a modern model of strategic management. The showcased model of project portfolio selection can find a significant usage among the current processes of railways restructuring, especially in financing of the projects of public–private partnerships.

Selection of New Projects Considering the Synergistic Relationships in a Project Portfolio

Multiple internal conflicts and external emergencies can occur when an enterprise implements a project portfolio (PP), making the PP inevitably deviate from the enterprise’s strategic objectives. As a means of project portfolio change (PPC) that aims to align the PP with strategic objectives, adding new projects can compensate for this deviation. Furthermore, the synergistic relationships in the PP can significantly impact the achievement of the enterprise’s strategic objectives. Therefore, this study presents a procedure for the selection of new projects that considers the synergistic relationships in the PP. First, the deviation between the PP and the enterprise’s strategic objectives is identified. Second, the synergistic relationships between candidate new projects and the projects in the PP are analyzed, based on which a model of new project selection is built. Third, by comparing the model simulation results of the attainment of the strategic objectives of several PPs, the new projects that can best achieve these strategic objectives are added to the PP. This procedure is illustrated using a numerical example showing its applicability and efficacy. For academia, this study provides a theoretical framework for the selection of new projects. Moreover, the straightforward procedure can help manage PPs in business practice.

Exploring the Relationship between Project Selection and Approval Criteria and Infrastructure Sustainability

This research aimed at compiling project selection criteria and modelling a robust criterion for infrastructure project selection and approval to enhance sustainability. The decision to select and approve sustainable infrastructure project for development must base on a robust criterion. To handle this properly, first, decision-makers must adopt a robust criterion that aid infrastructure sustainability. The study employed a systematic literature review, methodology and thematic analysis procedure to identify infrastructure project selection and approval criteria from literature. A total of 34 project selection criteria were identified and grouped into five categories. Besides, four approval requirements and four sustainable indicators have been discussed. To avoid any ambiguous and unclear questions in the criterion, 40 professionals were selected purposively for the pilot survey. Descriptive and inferential statistical analyses were employed, and findings were used to improve the criteria. The quantitative phase of the study was conducted on 320 stakeholders who were either project selectors, approvers, advisors, or designers responsible for government projects. S.E.M. were adopted for the analysis and testing of the hypothesis. Moreover, the study has proposed a structure model that integrates identified criteria and indicators to enhance infrastructure selection and approval. Continuous professional development training is vital for all stakeholders to understand and adopt the proposed project selection and approval criteria model to improve infrastructure sustainability performance and to enhance project selection and approval practices. And it was also included in the design brief as a deliverable factor. This outcome serves as preliminary and exploratory research to aid further study on the project selection and approval criteria model for infrastructure sustainability.]

Formation of a portfolio of energy saving projects at metallurgical enterprise

Purpose. To build a model of project selection for the portfolio in accordance with the energy strategy of the metallurgical enterprise. The urgency of solving these issues for the metallurgical industry of Ukraine is caused by the need to improve the economic stability of enterprises, increase the competitiveness of products and reduce dependence on energy suppliers.
 Methods. Methods of program and portfolio management, system approach, models and methods of strategic management in the field of project management and mathematical apparatus of set theory are used in formalizing the model of forming a portfolio of energy saving projects at a metallurgical enterprise.
 Results. The analysis of the general problem of portfolio management of energy saving projects at metallurgical enterprises is performed. The main stages of formation and implementation of the portfolio of energy saving projects of the metallurgical enterprise are considered in detail. A formalized mathematical model for the selection of energy saving projects at a metallurgical enterprise for the portfolio has been developed. With the help of the proposed approach and models, a portfolio of projects of the metallurgical enterprise PJSC “Dniprospetsstal” was formed, which included promising projects for implementation in accordance with the energy strategy.
 Scientific novelty. A model of project portfolio formation in accordance with the energy efficiency strategy of a metallurgical enterprise has been developed, which comprehensively takes into account the current state of the enterprise and its future strategic directions, resource and financial support of energy saving projects and risks of adverse events.
 Practical meaning. The use of this model allows consistent analysis of portfolio projects in order to identify opportunities for their implementation at the metallurgical enterprise, to coordinate project implementation plans and plans at different levels of planning, to select the most promising projects for implementation in accordance with the energy strategy.

Обґрунтування економічних проєктних параметрів ракетно-космічної техніки в умовах сталої космічної діяльності

The publication is aimed at determining the project parameters of the latest rocket and space technics. It is stated that when developing projects, it is necessary to take into account all phases of the life cycle of rocket and space technics in accordance with European standards as follows: phase 0 «Mission analysis/needs definition», phase A «Feasibility», phase B «Preliminary design», phase C «Detailed design», phase D «Qualification & Production», phase E «Operation», phase F «Utilization». On the basis of fractal analysis, a graphic interpretation of the comparison of SWOT-analysis components for the development of the rocket and space industry is constructed. The advantage of the model is the compactness of development due to the use of one platform and operational efficiency of use due to the absence of the need to upload data to a specialized system. Practical recommendations on the development of a national strategy for the innovative development of the rocket and space industry of Ukraine in the conditions of sustainable space activities are provided. It is identified that the weakness and a threat to the activities of the aerospace industry is insufficient financing for implemented projects and inefficient distribution of funds among its components: science – production. It is proposed to use Blockchain technology as an effective instrument for the cyber security of space objects. Possibilities of Agyle management are considered as a means for overcoming barriers to the introduction of innovative solutions in space activities. The mathematical model of project selection when maximizing the economic effect is presented, which takes into account both the economic components (the costs of «life» of the project, the budget for implementation, the cost of deferred decisions), as well as the social and ecological effects. The system of criteria for selecting and limiting a rocket and space technics project is substantiated. Prospect for further research is the substantiation of the project parameters of the latest rocket and space technics from the positions of other components of general efficiency: technical, social and ecological.

Robust Optimization Model for R&D Project Selection under Uncertainty in the Automobile Industry

In a company, project management is responsible for project selection from candidates under some limited constraints to achieve the company’s goal before the project begins as well as the project operations in progress. The development of new technologies and products can broaden a company’s market share, and to do so, research and development (R&D) projects are significant. However, limited funds force a company to select projects that can best represent the company’s interests. As projects may take a long time to develop, a number of uncertainties may occur, and the most concerning uncertainty is cost uncertainty. In this study, a robust optimization decision model for project selection considering cost uncertainty is proposed to assist the decision-making process for companies that need to select projects from a number of candidates due to limited funds. The model considers project selection in view of the total cost of ownership, which is a key factor for customers and companies in the automobile industry. The proposed model is tested in the automobile industry environment with different conservatism levels about cost uncertainty, and an analysis of expected market changes and a company’s income is performed with the solutions obtained from the proposed model. The result shows that the presented model reacts to cost uncertainty robustly for assisting the decision-makers in the company.

AHP AND WAFGP HYBRID MODEL FOR INFORMATION SYSTEM PROJECT SELECTION

The aim of this study is to propose an integrated Analytic Hierarchy Process (AHP) and Weighted Additive Fuzzy Goal Programming (WAFGP) method for the selection of information system projects that can use all types of linear membership functions and offer more flexibility. The proposed methodology includes three steps. First, an expert team was formed to identify the decision criteria and build a hierarchical model for the information system project selection. Then, the AHP was used to estimate the relative weights of the criteria. Finally, a WAFGP model was formulated and used to select the projects. A hypothetical example is given to show how to use this methodology and its advantages. In comparison to other approaches, the AHP-WAFGP hybrid model gives better support for information system project selection by selecting projects that make the best use of available resources and better satisfy the decision goals. Furthermore, the sensitivity analysis reveals that the proposed model is robust, adaptable, and not sensitive to small changes. Nevertheless, the proposed methodology does not include interdependencies among criteria and alternatives.

Applying the Analytical Hierarichy Process and Weighted Sum Model for Small Project Selection in Iraq

The assessment and selection of small projects are usually carried out using commercial, official, environmental and marketing data before the investment decision is made. Small projects are among the most important sources of job opportunities and revenue in many nations. The contribution of small enterprises the provision of work has been a controversial issue around the world, it is therefore important to clarify how best to choose between projects. This paper shows an example from Karbala governorate, which demonstrates project prioritization through two project selection methods. A questionnaire was completed and scores were computed for prioritizing potential projects. The first category used an analytical hierarchy process (AHP) model for investment selection when comparing a number of options (alternatives) with respect to specific criteria, then investment with the highest weight was used to select small projects in this field for second stage transference. The second category used a weighted sum model (WSM) for top small project selection, which operated by obtaining all small project scores, and declaring that with the highest project to score to be the best small project. After combining the data and research procedure, we found that the best option from among small projects in Karbala was an industrial dairy factory.

МОДЕЛЬ СТРУКТУРНОГО СИНТЕЗУ ПОРТФЕЛЯ ЕНЕРГОЗБЕРЕГАЮЧИХ ПРОЕКТІВ МЕТАЛУРГІЙНОГО ПІДПРИЄМСТВА

The features of the formation of a portfolio of projects following the energy-saving strategy of metallurgical enterprises are considered. As a result of the analysis of the energy-saving program structure at the metallurgical enterprises, it was found the problems that are solved by the energy-saving projects are inefficient consumption (significant losses) of energy resources (gas, thermal energy, electricity), control over the costs formation and the results of improvements in energy consumption. The overall goal of improving energy efficiency at the enterprise is realized through the management of an energy-saving project portfolio, which is aimed at fulfilling the following tasks: optimization of energy balance; minimization of natural gas consumption; optimization of energy efficiency, etc. Success criteria for an energy-saving project include efficiency; operating costs, losses, etc. It should be noted that it is difficult to objectively estimate the share of each energy resource in the total flow, to determine the energy intensity of individual production, departments, and the whole enterprise, etc. We propose the concept of managing a portfolio of energy-saving projects, which is based on several interconnected adaptive systems: planning and building, monitoring and change management. To establish priority problems, a necessary and complete analysis of the economic trends formation, to characterize the process of formulating the fuel-energy balance of metallurgical enterprise. A system model for the formation of a portfolio of energy-saving projects of a metallurgical enterprise is presented. We describe a mathematical model of project selection is described, which comprehensively takes into account the current state of the enterprise and its future strategic areas of work, resource and financial support for projects and risks. The use of this model allows a consistent analysis of portfolio projects to identify the possibility of their implementation at a metallurgical enterprise, coordinate project implementation plans and plans of the enterprise at different levels of planning, select the most promising projects for implementation following a defined energy strategy.

Project Portfolio Selection Considering Total Cost of Ownership in the Automobile Industry

Project portfolio selection for developing a new product is critical to a company because the attributes of the project reflect on the price, quality, and functionality of the developed product. The market evaluates the developed product and the assessment of the product value determines its sale on the market. The project portfolio selection and sales are interconnected from a corporate perspective. The automobile industry is an industry which responds to the issue of business sustainability sensitively because their business directly relates to the environment. In this study, business sustainability was approached through the perspective of total cost of ownership (TCO) embedded into the project selection model after the attributes of the project, investment and efficiency, and the TCO of the product were combined to generate a sales function. The sales and revenue models were proposed, and the validity of the models was confirmed using a case from the automobile industry. As a result of the experiments, the sales model tended to show that the market share increased by selecting the proper number of projects to maximize sales. In contrast, the revenue model showed a tendency to select projects more than the sales model in order to maximize the profits of the company. By suggesting project selection models in a new perspective different from the ones in existing studies, this study is valuable with regard to the fact that the suggested models preserve project interrelationships, TCO, and product sales in a practical manner to enhance business sustainability.

Regional development programming

Purpose: the purpose of the article is to study the features of regional development in terms of the need to take them into account in the formation of development strategies. The revealed features are the level of development, potential, geographical location, etc. These features are displayed in the strategy of formation of development projects in the region.Methods: the methodological basis for the formation of prospects for the development of the region is the methodology of program-target management to solve the problems of development of complex hierarchical systems. The main elements of this methodology are the basis for programming development processes in the space "forecast-strategy-program-project".Results: the results of the study are to determine the stages of programming the sequence of regional development, finding ways and conditions for the transition between the stages and intermediate goals of development, the formation and selection of development projects. To overcome the contradictions in the formation of the composition of the objectives of the development of projects in a hierarchical system: “macro level – the level of regions – the level of municipalities”, the top priority rule is used over the following. There is also the possibility of adding a lower level of own goals and projects, in accordance with the interests of a particular region and municipality. The task of project selection is one of the most important. Its importance lies not only in the required and available financial resources, but also in shaping the structure of the objective function of each region, adequate to the development goals. Fragments of the scheme of programming of development of the Astrakhan region are given.Conclusions and Relevance: the current external conditions for the development of the domestic economy dictate the need for the full use of domestic resources and the capabilities of the scientific and technological potential to ensure the competitiveness of Russia. This implies a critical attitude towards the development prospects, which are formed without taking into account the national interests of our country. The complexity of strategy formation is due to the hierarchical structure of the country's economy and the difference in interests in the system “macro level – meso level – micro level”. The basis of the proposed approach to development programming is the formation of strategic forecasts. The authors proposed a sequence of actions for programming the formation and selection of development projects at the regional level, as well as a possible list of promising development goals for the region. The analysis of the peculiarities of the development of the regions showed their considerable heterogeneity, which is important to take into account when forming promising strategies. The proposed scheme for the formation of regional development strategies and the mathematical model of project selection are illustrated by the example of one of the municipalities of the Astrakhan region.

Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts' assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Natural Science Foundation of China is conducted to show the effectiveness of the proposed model.The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historical statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.

A two-phase transfer learning model for cross-project defect prediction

Context: Previous studies have shown that a transfer learning model, TCA+ proposed by Nam et al., can significantly improve the performance of cross-project defect prediction (CPDP). TCA+ achieves the improvement by reducing data distribution difference between source (training data) and target (testing data) projects. However, TCA+ is unstable, i.e., its performance varies largely when using different source projects to build prediction models. In practice, it is hard to choose a suitable source project to build the prediction model.Objective: To address the limitation of TCA+, we propose a two-phase transfer learning model (TPTL) for CPDP.Method: In the first phase, we propose a source project estimator (SPE) to automatically choose two source projects with the highest distribution similarity to a target project from candidates. Next, two source projects that are estimated to achieve the highest values of F1-score and cost-effectiveness are selected. In the second phase, we leverage TCA+ to build two prediction models based on the two selected projects and combine their prediction results to further improve the prediction performance.Results: We evaluate TPTL on 42 defect datasets from PROMISE repository, and compare it with two versions of TCA+ (TCA+_Rnd, randomly selecting one source project; TCA+_All, using all alternative source projects), a related source project selection model TDS proposed by Herbold, a state-of-the-art CPDP model leveraging a log transformation (LT) method, and a transfer learning model Dycom with better form of TCA. Experiment results show that, on average across 42 datasets, TPTL respectively improves these baseline models by 19%, 5%, 36%, 27%, and 11% in terms of F1-score; by 64%, 92%, 71%, 11%, and 66% in terms of cost-effectiveness.Conclusion: The proposed TPTL model can solve the instability problem of TCA+, showing substantial improvements over the state-of-the-art and related CPDP models.

Project selection and scheduling for phase-able projects with interdependencies among phases

Abstract This research proposes a model for project selection and scheduling when some of the projects in the available pool of projects can be implemented in phases. We present a mixed integer programming (MIP) model that maximizes the Net Present Value (NPV) of future investments in situations where temporal budget limitations and reinvestment strategies exist. The MIP reveals the optimal phasing solution. It models the Interdependencies among different phases of a project and also takes the foundation/infrastructure requirements for development of future phases into consideration. To solve large-size problems, we present a solution method that initially reduces the problem size. Then, a two-step heuristic is presented that in the first step adds projects to the pool of selected projects one by one based on a favorability measure and in the second step, eliminates some phases of the chosen projects with some probability. The performance of the heuristic is illustrated through five small-size and four large-size examples. We perform sensitivity analysis by altering various parameters that affect the heuristic's performance such as different favorability measures, and different initial available budgets. The results are favorable for the preprocessing step and solution heuristic. On small-size scenarios, the heuristic can find the optimal solution from the MIP in almost all cases. Furthermore, on large-size scenarios, the heuristic finds solutions within approximately 100 s that are better than the ones found by solving the MIP given a 10,000 s time limit.

Multi-objective IT Project Selection Model for Improving SME Strategy Deployment

Due to the limited financial resources of small and Medium-sized enterprises (SMEs), the proven approaches for selecting IT project portfolio for large enterprises may fail to perform in SMEs; SME top management want to make sure that the corporate strategy is carried out effectively by IT project portfolio before investing in such projects. In order to provide automated support to the selection of IT projects, it seems inevitable that a multi-objective approach is required in order to balance possible competing and conflicting objectives. Under such an approach, individual projects would be evaluated not just on their own performance but on the basis of their contribution to balance the overall portfolio. In this paper, we extend and explore the concept of IT project selection to improve SME strategy deployment. In particular, we present a model that assesses an individual project in terms of its contribution to the overall strategic objectives of the portfolio. A simulation using the model illustrates how SME can rapidly achieve maximal business goals by deploying the multi-objective algorithm when selecting IT projects.

A hybrid integer grey programming for an integrated problem of project selection and scheduling with interval data

Projects are inherently subject to uncertainty since in most cases, the information at hand is either approximate or partial. Grey numbers can handle the uncertainty in projects without requiring a predefined membership or probability function. The current paper proposes a hybrid solution technique for the integrated problem of project selection and scheduling with interval grey data. The presented method combines the concept of grey programming with branch and bound algorithm to achieve Pareto interval solutions. The algorithm is then applied to a grey bi-objective project selection and scheduling model. Additional experiments are carried out regarding the effects of grey weights and objective weights on the goal values. Moreover, the proposed algorithm is compared with fuzzy goal programming method. The results indicate that the developed approach has more precision than fuzzy programming and can achieve acceptable alternate solutions for small to medium scale problems.

Knowledge Contribution as a Factor in Project Selection

Project selection is a crucial decision-making process in many organizations. By adopting a project-based learning perspective, this study sets out to develop a framework to integrate organizational knowledge development with project selection. We utilize various knowledge management models to create a structured evaluation metric to measure project contribution to organizational knowledge. A project selection model, which involves project knowledge contribution as one of the evaluation perspectives, is proposed. Results of a focus group study effectively validate the proposed evaluation metric. The article concludes with an empirical implementation of the model in an electronic component manufacturing company.

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.