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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 portf...

One of the primary components of innovation management in research and development (R&D) organizations is project selection. In public sector R&D environments, it is essential that improvements are made to the selection processes. These improvements should address the unique challenges faced by these organizations in order to increase the quality of the decisions that are made. Modern decision support systems (DSS) have the potential to meet these needs. The implementation of effective DSS backed by solid data management practices and driven by artificial intelligence technologies has the potential to enhance the management of public sector R&D innovation.

The project selection problem has attracted significant attention among construction project participants. Typically, a contractor can execute one or more projects from available projects. This study introduces a new strategy to help contractors in evaluating and selecting suitable construction projects. The new strategy is based on identifying multiple criteria that affect construction projects as well as developing and using a model that supports decision based on the analytic hierarchy process. Furthermore, a fuzzy risk analysis model is proposed to analyze and quantify some of the identified criteria to help contractors select minimally risky projects. Market conditions, financial resources, project conditions, time delays, and cost overruns are five identified criteria that control the selection. Through identifying 70 subsidiary factors, the effects and importance of the first three criteria were determined through a field survey, while time delay and cost overrun criteria were analyzed and quantified through the risk analysis model. The new strategy was applied on a real case study comprising two HVAC system projects in the construction industry (project 1 in Egypt and project 2 in Saudi Arabia). Criteria and causes that yield different effects on the new strategy to support contractor decision and selection control are discussed in detail. Risk factors were ranked according to their importance, and their effects on both time and cost were clarified. The results obtained from the investigated case study showed 55.5% support for project 2, and only 44.5% support for project 1. This strategy is applicable to other case studies in different countries with different projects, criteria, and execution activities.

Innovation agencies, that is organizations with the primary focus of funding applied research and technological development, evaluate project proposals to select the most promising proposals for funding. At the moment, there is only little verified knowledge available on project evaluation and selection processes of innovation agencies. We want to show how projects are evaluated and selected in these organizations. We want to also make a contribution for better understanding the variety of the utilized processes by pointing out the reasoning behind some of the most important practices. This article therefore focuses on the following questions: How are projects selected in innovation agencies? What are the employed procedures and practices? Are there differences in procedures and practices and what would be the reason for these differences? The basis for answering these questions is a study produced for the European Association of National Innovation Agencies, Taftie. There we have analysed the project selection procedures of 18 programmes run by 12 European innovation agencies. To do so, we have produced an overview of existing selection procedures of the innovation agencies, analysed, and compared the procedures along the stages of a typical selection process. The key points of interest were role of evaluators, selection criteria, ranking procedures, and general process issues.

Based on multi-objective programming method, this paper reviews the previous research on the problem of government guarantees in the project financing, and puts forward the importance of the research on project selection of government guarantee in Build-Operate-Transfer (BOT) or Public Private Partnership (PPP) project finance. Through the analysis of the selection criteria of guarantee project, we find that the selection problem of government guarantee of BOT/PPP infrastructure projects is essentially a multi-objective decision problem. Moreover, we establish a chance-constrained objective programming model for the government to make the decision to guarantee project selection.

The benefits of the Six Sigma approach are recognised since the 1990s and Six Sigma project selection is pointed out as being a key success factor in its implementation. Six Sigma project selection...

Information System Project (ISP) Selection is the most significant strategic consideration to each management of organizations, in terms of business intelligence (BI) as critical factors must be co...

Nº Artículo:200 Project selection is a common problem for many companies. Specifically, it consists in identifying which projects should be selected with regard to their economic efficiency, i.e., the projects that maximise the profit they bring in while minimising the cost of the resources consumed. In this paper, we have focused our interest on energy service companies because of the importance of a convenient selection of their projects. In these types of companies, the attractiveness of a project depends on both the profit estimations obtained in simulations of the energy systems to be improved, as well as the probability that the project will be awarded (e.g., in local government bids, where typically several energy service companies compete to win the bid). We propose a new project selection method, especially tailored to energy service companies and based on centralised data envelopment analysis models with limited availability of the resources. This contrasts with all existing project selection methods and allows the proposed approach to make more efficient use of the limited resources. We have applied the model to a real-world case by selecting projects in a Spanish energy service company, showing the benefits of applying this approach, and comparing the results obtained with other data envelopment analysis project selection approaches. Ministerio de Economía, Industria y Competitividad DPI2017-85343-P

Strategic projects are large scale, complex, and require significant investments and resources. These projects aim at gaining long-term social and economic benefits. Therefore, organizations focusing on strategic projects should use a consistent approach that suits their strategy, capability, and long-term expectations. Based on the four research questions and content analysis of the literature, generic processes used for the strategic project selection in tandem with the managerial capabilities are identified in this paper. The generic processes and managerial capabilities are used to develop a generic framework for strategic project selection. The framework is used for literature analysis in the paper. The review shows that both qualitative and quantitative methods are used for strategic project selection. Some possible research directions have also been proposed at the end of the review. The paper provides value to both researchers and practitioners in terms of tools available and a guidance on project selection through a structured process framework. 2020 by the authors. Scopus 2-s2.0-85092545346

Decision making is one of the primary functions of management. Making strategic decisions are important to achieve organisational efficiency and effectiveness. In the selection of projects, it is necessary for organisations to prioritise their projects due to limited resources. Therefore, the selection of projects that bring value and substantial impact on the organisation is very important to ensure that they can compete in the dynamic external environment and meet the goals and objectives. From the case study, the result suggests that the Analytical Hierarchy Process (AHP) method can contribute significantly to the identification of sources that need further clarification of attributes and criteria and at the same time it can provide a systematic analytical tool in making a group decision. This method allows the organisation to prioritize its projects based on certain criteria as it is a quantitative decision support tool. In conclusion, AHP is the best and efficient method for decision makers to consider all facts, weigh the pluses and minuses, reach, reevaluate and communicate their decisions.

With the opening of a new round of electricity reform in China, electrochemical storage power station (ESPS) has broad application prospects in this reform. the state promotes public-private partnership (PPP) model as the main mode of ESPS construction and operation. In China’s PPP system, there are fewer protection provisions for banks, therefore, bank needs to screen all the PPP projects of electrochemical storage power station (ESPS) that apply for loans. However, the PPP project selection of ESPS is subjected to one problem: there is lack of evaluation index system for PPP project selection of ESPS. To this end, this study proposed a selection framework of ESPS from bank’s perspective, which consists of a evaluation index system and an evaluation model based on analytic hierarchy process (AHP) method. This framework can comprehensively evaluate the PPP project of ESPS and the evaluation process is easy to handle. Finally, the effectiveness of decision framework was verified based on a case study of China.

Conservation auctions often follow an ‘all or nothing’ bid selection approach, which restricts the selection of the most suitable parts of a submitted project. The problem with ‘lumpy’ (or all-or-nothing) project selection has been identified in the literature as a major problem in conservation policy; however, the extent of the problem has been rarely quantified. Using an actual conservation tender dataset from Tasmania, the effect of the approach was estimated. This study finds that with a relatively small budget, the cost-effectiveness loss could be as high as one-quarter. To avoid such problem, a partial bid selection could be applied. The basic principle of a partial bid selection is to invite a single project from each landholder with the option for the environmental planning agency to partially select sections of the offer lands that maximise the achievement of the agency's policy objectives. A sensitivity analysis with different bid and ecological value correction factors shows that when t...

For a transition toward the circular economy (CE) at the firm level, circular innovations are an essential requirement. Many companies are still hesitant to introduce circular solutions, as their future success chances are difficult to predict. Circular solutions often imply a high uncertainty and complexity because they are designed over multiple life cycles and are strongly interconnected with diverse stakeholders. Therefore, an effective selection process tailored to circular innovation is of great advantage. This study examines circular project selection by investigating selection processes and evaluation criteria for circular innovation management. A qualitative research design was chosen, including 18 in-depth interviews with CE experts and representatives from CE pioneer companies. Findings on the selection process show that circular innovation projects are often embedded in a strategic CE framework decision. Whereas idea generation is usually approached bottom-up involving different stakeholders, project evaluation is rather performed top-down by top management or in cross-functional teams. Furthermore, the study discusses evaluation criteria and their CE implications in detail and structures them into a criteria framework that can be used in multi-criteria decision models. This paper makes a theoretical contribution by connecting innovation and CE literature and by providing new knowledge on the still scarcely explored topic of circular project selection. As practical contribution, the study guides managers on how to approach project selection in circular innovation management and thus supports their development toward a CE.

A proper maintenance project selection is a key to support performance of power plant. The issue of maintenance is allocation a limited budget to achieve the goals of various maintenance criteria and conflicting objectives. This paper presents an integrated AHP and goal programming. In the first step, since maintenance is measured in qualitative criteria, it is recommended to use AHP to weight and quantify criteria. Second step utilize GP which uses the criteria weights and alternative priorities calculated with AHP to optimize multiple objectives without exceeding available resource constraint. The results show that the approach can aid in selection optimal projects within available budget.

A robust project selection process is critical for the selection of sustainable projects that meet the needs of an organization or community. There are multiple factors or criteria that can be considered in the selection of the appropriate sustainable project, but it can be challenging to find sufficient depth of expert opinion to perform a strong evaluation of these criteria. Several researchers have turned to the sustainable project literature as a source of expert opinion to evaluate the criteria used in sustainable project selection and rank them based on importance using different multi-criteria decision-making (MCDM) methodologies. However, using the literature as a source of expert opinion poses a different set of challenges and may not accurately represent the actual opinions of sustainable project subject matter experts (SMEs) and practitioners. In this study, the fuzzy analytic hierarchy process (FAHP) methodology is used to determine the importance of project cost, project maturity, skill and experience, uncertainty, and technology information transfer as selection criteria using collected opinions from academic sustainable project experts and practitioners. The results are then compared with previous research that used the literature to rank these five criteria based on importance when selecting between multiple sustainable project alternatives. The results show that project cost is still considered the major driver of decision making in sustainable project selection by both the literature and practice. However, unlike the literature-as-experts approach, SMEs prioritize skill and experience and technology information transfer over project maturity and uncertainty. Project managers and decision makers can use these findings to best prioritize the types of challenges that may occur depending on inputs for the FAHP analysis.

Six Sigma is a well-structured and proven methodology for improving organizational performance. It helps achieving the goals of the organization through the use of project-driven approach. The implementation of Six sigma approach depends on proper identification and selection of projects, moreover, the selection of right Six sigma projects is one of the critical success factors of six sigma efforts. The paper provides the sources for the identification of potential projects, top down and bottom up approaches, the process of selection of projects, guidelines that assist the selection of appropriate projects, selection criteria which must be precisely chosen in accordance with the objectives, needs and capabilities of the organization as well as sophisticated techniques and tools recommended in the literature for the selection of projects.

In this study, we address a public R&D project portfolio selection problem with project cancellations. For several reasons, a funded R&D project may be halted before finishing the planned research. When a project is canceled, most of its budget is usually unused and also some of the spendings can return to the funding organization. In the call-based R&D programs, usually project selection decisions are made in one go, and, in the current call, it is not possible to award new projects with the unused budget. Decision-makers (DMs) of funding organizations can benefit from considering possible project cancellation situations to improve the budget utilization. We consider two cases. In the first case, we assume that cancellation probability of a project cannot be assessed but the DM can estimate the number of projects that will be canceled. In the second case, we assume that for each project, a cancellation probability can be assessed. For the first problem, we develop a mixed-integer linear programming formulation and a dynamic programming algorithm. For the second problem, we develop a chance-constrained stochastic programming formulation that can be solved as a mixed-integer second-order cone program. Our computational results show that practical-size problems can be solved by the proposed solution approaches.

This paper reports on a comprehensive research study of hazard-ranking models for prioritization and selection of highway–rail grade crossing improvement projects. The U.S. Department of Transportation (DOT) accident prediction model is the most commonly used hazard-ranking model, although 11 states use state-specific models. Key variables not included in the U.S. DOT model but included in state-specific models may provide useful insight for identifying hazardous crossing locations. Emerging issues, such as the crash modeling approach used by the U.S. DOT model and the application of economic analysis principles in project prioritization, are also discussed. The findings reported in this paper provide a starting point for a broader discussion among all stakeholders in the grade crossing safety community about ways to improve hazard-ranking and project selection for highway–rail grade crossing investments.

Project selection is considered as the first essential part of project portfolio management. Project selection is also considered as a process to evaluate each project idea and chooses the one with the biggest priority. Project selection plays an essential role in the entire life cycle of different projects. This paper presents a survey for project selection using multiple criteria decision mak-ing techniques. The study considers 60 papers from over the period 1980-2017. The results of the survey have indicated that integration of Order of Preference by Similarity to Ideal Solution (TOPSIS) and analytical hierarchy process/analytical network process was the most popular techniques for project selection followed by VIKOR method.

Many organizations have implemented the world class manufacturing (WCM) system, a management method focused on attacking the largest losses and wastes in a manufacturing environment to achieve excellence in manufacturing processes. However, to apply the program successfully, it is necessary to align the goals that the company wishes to achieve with the way the system is developed. For this reason, this study proposes the use of the value-focused thinking (VFT) problem structuring method with the FITradeoff method to provide greater coherence and efficiency throughout the WCM project selection process. As a result, the structuring of the company’s objectives and the problem situation were carried out through the decision maker (DM), and a project with a greater urgency for execution was selected, helping the company to use the WCM program more successfully.

Project selection and the prioritization of activities are configured as classical optimization problems, and one of the most commonly used techniques to solve this kind of problem is Goal Programming (GP), a multi-criteria analysis technique. Problems related to the prioritization of railway investment selection or maintenance processes involve goals and constraints such as budget constraints, the availability of labour and resources, and the degradation of permanent track materials. This chapter presents two applications of Goal Programming in railways. The first model selects projects for a railway-sustaining investment portfolio. The second model allows for the prioritization of railway superstructure maintenance based on the maintenance demand for components and the geometric, environmental and demographic characteristics of a railway. Defining the best investment portfolio or a proper maintenance strategy are essential tasks for railway sustainability to achieve long-term goals involving multiple, often immeasurable and conflicting, objectives. The results show that these two proposed models allow for the prioritization of goals defined as the most important and proved useful in the presentation of scenarios that facilitate the choice of investment portfolio or superstructure maintenance strategy.

Design project selection is the first important part of design project management. However choosing right design projects is seldom easy, especially to consider about the satisfaction of the designer to the design projects and collaboration ability for the designer in the design projects. Even though, there are various methods for selecting projects, most of these methods not consider about the satisfaction of the designer and collaboration ability of the designer. Therefore, we studied designer’s satisfaction to design projects and collaboration ability in design projects in the design project selection process. In this paper, we propose a project selection method, which consider about the designers’ satisfaction to the candidate multi-project and collaboration ability in the candidate multi-project. The main objective of our research is to help project manager to find an adaptable and comfortable multi- project to the designer.

This paper discusses a project selection and adjustment problem in the situation where some project parameters are given by experts’ estimates because of lack of historical data. Uncertain variables are used to describe these project parameters and the use of them is justified. Based on uncertain measure, a cost overrun risk which describes the average amount of investment outlay exceeding the available budget is proposed, and a new optimization model which simultaneously considers the selection of new projects and the adjustment of existing ones is developed. To solve the proposed problem, the deterministic equivalents of the model are provided and a genetic algorithm is offered. As an illustration, an example is also presented and discussed.

Desirable targets and undesirable targets for each DMU are identified.The targets are reachable and more reasonable for the DMUs.Several secondary goal models are proposed considering both kinds of targets.Strong ability of discriminating the DMUs are shown in application examples. Data Envelopment Analysis (DEA) has been extended to cross-efficiency evaluation for ranking decision making units (DMUs) and eliminating unrealistic weighting schemes. Unfortunately, the non-unique optimal weights problem in DEA has reduced the usefulness of this extended method. Aiming at solving this problem, we first incorporate a target identification model to get reachable targets for all DMUs. Then, several secondary goal models are proposed for weights selection considering both desirable and undesirable cross-efficiency targets of all the DMUs. Compared with the traditional secondary goal models, the cross-efficiency targets are improved in that all targets are always reachable for the DMUs. In addition, the proposed models considered the DMUs' willingness to get close to their desirable cross-efficiency targets and to avoid their undesirable cross-efficiency targets simultaneously while the traditional secondary goal models considered only the ideal targets of the DMUs. Finally, the calculation results of our proposed models are compared with those of some other traditional methods for two published examples: an efficiency evaluation of six nursing homes and an R&D project selection.

Appropriate project selection has a significant impact on construction companies’ success. Selecting the appropriate project is complicated due to uncertainties related to many factors that may influence the project selection process. The uncertainties related to a construction project may vary according to the type of the construction project. Therefore, having a project selection tool, which assists construction companies in selecting a particular construction project, can be a significant advantage in achieving success. Urban renewal projects constitute a significant portion of the projects that are carried out by construction companies in Turkey. This study aims to propose an integrated approach for selection of urban renewal projects. The proposed approach combines analytic hierarchy process (AHP) method and PROMETHEE, to help construction companies in selecting the appropriate urban renewal project. AHP and PROMETHEE were used to find the weights of the selection criteria and to rank the alternative projects, respectively. The proposed approach is used to solve a project selection problem of a Turkish construction company, which is mainly specialized in urban renewal projects. In the case study, twelve different projects were ranked according to seventeen evaluation criteria by using the proposed approach. The findings of this study revealed that the proposed approach can be a useful tool for construction companies, which are especially specialized on urban renewal projects.

Six Sigma is a strategic approach of significant value in achieving overall excellence. It helps to accomplish the organizations strategic aim through the effectual use of project controlled methodology. As Six Sigma is a project controlled approach, it is necessary to prioritize projects which give utmost economic benefits to the firm. In real practice, Six Sigma projects selection is very tough assignment because poor project selection also happens even in the well-managed organizations and this can weaken the success and trustworthiness of the Six Sigma practice. The present study aims to develop a project selection approach based on a combination of fuzzy and MADM technique to help organizations determine proper Six Sigma projects and identify the priority of these projects mainly in automotive companies. VIKOR and TOPSIS methods have been used to select the proper Six Sigma project composed with fuzzy logic. In this context, seven critical parameters have been considered for selection of finest alternative. The weights of evaluation criteria are obtained using the MDL (modified digital logic) method and final ranking is calculated through primacy index obtained by using fuzzy based VIKOR and TOPSIS methodology. A factual case study from automotive industry is used to investigate the efficacy of the planned approach.

The project selection process is a crucial step in sustainable development. Effective sustainable development depends on the ability to select the appropriate sustainable project to implement to ensure that the desired goals are met. Some of the most common characteristics or criteria used in evaluating sustainable projects include novelty, uncertainty, skill and experience, technology information transfer, and project cost. Prioritizing these criteria based on relative importance helps project managers and decision makers identify elements that require additional attention, better allocate resources, as well as improve the selection process when evaluating different sustainable project alternatives. The aim of this research is to use the fuzzy analytic hierarchy process (FAHP) methodology in which fuzzy numbers are utilized to realistically represent human judgment to rank the different project criteria based on relative importance and impact on sustainable projects. The results from the FAHP show that the most important criterion to consider in sustainable project selection is project cost, followed by novelty and uncertainty as the second and third most important criteria, respectively. The two least important criteria out of the total of five examined in this research were the skill and experience and technology information transfer, respectively. These results will help project managers and decision makers identify selection criteria with higher weights of importance. Given that the selection criteria chosen for this research are not limited to the evaluation of a specific type of sustainable projects or a specific location, they can be used to evaluate different types of sustainable projects in different environments and locations.

Petroleum tax design analysis usually abstracts from the fact that taxation affects not only project selection but also concept design and drainage strategy, and thereby the extraction and recovery rate. The implicit assumption is that oil companies are unresponsive to tax changes where concept selection and production decisions are concerned. In his novel approach, Smith (2014) develops a broader model which accounts for the additional effects. This provides a consistent framework which permits analysis of specific settings. Berg et al. (2018) apply the model to establish the effect of a reduction in uplift on the Norwegian continental shelf (NCS) and find, for this case, that the traditional assumption of an unresponsive company still holds. Analysing field data from the NCS, we find that the actual production profiles do not resemble the mathematically tractable theoretical model of a constant decline in production used in Smith (2014) and Berg et al. (2018), and we explain why this is the case for offshore fields. We propose a novel theoretical resource model which fits the data from the 92 largest fields on the NCS. It is a general resource model that allows for injection from start of production, and where the model of a constant decline in production is a special case. Our model is also a mathematically tractable model of constant decline, but where the decline is at the level of the individual well rather than the field. The recovery rate is thus responsive to the number of wells, and tax design affects the recovery rate. Our findings suggest that companies are responsive, and that tax design should take account of distortions in the selection of development concepts.

Small and medium sized enterprise (SME) contractors form the bedrock of economic growth in all countries. This study presents a framework for the selection of projects towards the growth of SME contractors in the Cape Coast Metropolis of Ghana. Sixty questionnaires were administered among the SME contractors and forty-seven questionnaires were returned, representing 78.3%. Findings from the study showed that project selection should be done in careful manner for SME’s, resources should not be tied up in less significant activities and the SME management should take decisions that are feasible for the growth and expansion of the company. It was concluded that the development of the final framework work will serve as a guide in predicting the growth of SME contractors in within the study area.

This paper presents a case study of the construction and execution of a model for the ranking of projects submitted to the Scientific Initiation Program (PIBIC) of the University of Brasilia (UnB). In the execution of this work the Multiple Criteria Decision Analysis (MCDA) AHP and Promethee II methods were applied. The results obtained in each method were analyzed and compared. Hence, a model proposal for PIBIC projects selection is presented. The proposed model can be used in several project selection processes, maintaining a coherence and standard in the evaluations.

The highest probability of success, consuming least efforts and greatest business impact are considered major factors for the selection of best software project. The project selection is often referred as a difficult and important task by companies, but correct selection is necessary to achieve the goal. Defined methods of prioritizing the project parameters are helpful to select one best project from multiple projects. To deal with this situation AHP technique can be used to prioritize parameters for software project selection. The research work outlined the objective of AHP technique and presented the relative importance of software project Parameters. Several steps to solve the problem are highlighted. At the end the result is also checked by consistency verification, to know whether the result is acceptable or not.

Article history: Received March 14, 2014 Accepted August 10, 2014 Available online August 12 2014 Nowadays, selection of an optimal project has become a challenging task for managers and decision makers. Project selection for a decision maker can be viewed as a complicated multicriteria decision making (MCDM) problem, which requires consideration of a number of conflicting, tangible and intangible selection criteria. Moreover, decision makers tend to use linguistic terms for expressing their assessments because of their different backgrounds and preferences, some of which may be uncertain and incomplete. Hence, this paper focuses on developing a hybrid fuzzy MCDM approach by combining AHP and VIKOR for solving the project selection problem. Finally, A numerical example is proposed to illustrate an application of the proposed method. © 2015 Growing Science Ltd. All rights reserved.

The importance of the project selection phase in any six sigma initiative cannot be emphasized enough. The successfulness of the six sigma initiative is affected by successful project selection. Recently, Data Envelopment Analysis (DEA) has been proposed as a six sigma project selection tool. However, there exist a number of different DEA formulations which may affect the selection process and the wining project being selected. This work initially applies nine different DEA formulations to several case studies and concludes that different DEA formulations select different wining projects. Also in this work, a Multi-DEA Unified Scoring Framework is proposed to overcome this problem. This framework is applied to several case studies and proved to successfully select the six sigma project with the best performance. The framework is also successful in filtering out some of the projects that have “selective” excellent performance, i.e. projects with excellent performance in some of the DEA formulations and worse performance in others. It is also successful in selecting stable projects; these are projects that perform well in the majority of the DEA formulations, even if it has not been selected as a wining project by any of the DEA formulations.

As user-centric innovation has recently emerged as a successful way of developing new products, services, and concepts, it is worth considering the perspectives of potential technology users during R&D project selection processes. Nevertheless, little effort has been made to reflect customer-perceived value in establishing selection criteria, with the focus mainly on technological potential instead. Therefore, this study aims to develop an R&D project selection model incorporating not only technological potential but also customer-perceived value. For this purpose, a new R&D project evaluation model and process is proposed, and its feasibility is tested by potential users in a real scenario. The automobile industry is suitable for our evaluation model because it is a B2C and system-based industry where customer needs are critical to market success and a number of R&D projects are proposed every year. Finally, a supporting tool is developed to help interact with various evaluators and visualize the evaluation results, as customer involvement is recommended for accurate project evaluation from the perspective of technology users. This study is one of the earliest attempts to reflect customer-perceived value in R&D project selection, and practically, the research outputs are expected to be useful to automobile manufacturers in creating value from R&D projects.

This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of the candidate projects, which turns to be unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four real-world project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure (random search). A second experimental study evaluates the best parameter settings to perform the heuristic search. Experimental results show evidence that the project selection technique can be used in large-scale scenarios and that the genetic algorithm presents better results than simpler search strategies.

To be successful in a competitive environment, contractors have to prepare their bids wisely. The two main decisions they have to make are the project selection decision and the markup selection decision. This paper proposes a new optimization model that combines these two main decisions. It also takes into account the importance of eminence and previous works as the most important non-monetary evaluation criterion used by owners for evaluating bids. These factors combined with the two decisions make this model complex and nonlinear. To solve this model, a customized Genetic Algorithm is developed. Using Monte Carlo simulation, the result of the model is compared to the results of conventional models that only consider bidding markups. The comparison shows that considering eminence can increase the expected profit of the contractor to more than 25% under some evaluation criteria mindsets of the owner.

Este trabalho tem por finalidade desenvolver um modelo para a seleção de projetos oriundos do planejamento estratégico de uma empresa do setor elétrico brasileiro, os quais irão compor o portfólio anual. A seleção desses projetos ocorre em um momento em que ainda não existem informações com nível de detalhamento suficiente para a utilização dos critérios quantitativos amplamente empregados como, por exemplo, a taxa interna de retorno e o valor presente líquido. Para o desenvolvimento desse modelo foi utilizado o método de apoio a decisão PROMETHEE V, o qual permitiu a incorporação das restrições existentes ao modelo por meio da utilização de programação linear inteira. O modelo desenvolvido mostrou ser extremamente robusto e atendeu aos objetivos estabelecidos. This study aims to develop a model for project selection based on the strategic planning of an electric utility in Brazil, which will compose the annual project portfolio. The selection of these projects is made in the absence of information with sufficient detail for the use of the most commonly applied quantitative criteria, for example, the internal rate of return and the net present value. To develop this model, we used the decision support method PROMETHEE V, which allowed for the incorporation of existing restrictions on the model, using integer linear programming. The model is shown to be extremely robust and achieved the study aims.

This paper constructs an efficiency-adjusted public capital stock series and re-examines the public capital and growth relationship for 52 developing countries. The results show that public capital is a significant contributor to economic growth. Although the estimated coefficient for the income share of public capital is larger in middle- than in low-income countries, the opposite is true for the marginal product of public capital. The quality of public investment, as measured by variables capturing the adequacy of project selection and implementation, are statistically significant in explaining variations in economic growth, a result mainly driven by low-income countries.

This paper improves the integrated data envelopment analysis (DEA) model proposed by Asosheh et al. [1], for evaluating and ranking information technology (IT) projects in presence of both cardinal and ordinal data. They used balanced scorecard (BSC) for defining IT projects evaluation criteria and introduced a new DEA model to obtain most efficient IT project by considering imprecise data. This paper shows that their model randomly selects one efficient decision making unit (DMU) or IT project as the most efficient and the selection depends on the program or method is used for solving the problem. We develop their idea and propose a modified model to determine the most efficient DMU. In addition, this paper proposes a new algorithm for finding and ranking other efficient DMUs. The capability and usefulness of the proposed model is indicated by a numerical example.

The selection of maintenance projects that require funding is essential to the well-being of the waterway transportation system. This paper develops optimization models and heuristics to maximize the benefits of the maintenance projects selected for funding in a multimodal transportation network. This paper includes a mixed linear integer model to maximize the overall network capacity while allowing for partial funding of each project. The model is further extended to account for the random nature of shoaling and subsequent vessel draft restrictions after dredging to maximize the expected capacity over a multiyear study period. In addition, two heuristics are proposed to provide approximate solutions efficiently. All the models and heuristics are tested on real network examples.

One of the most important tasks for regional governments is the selection of the most attractive and potentially successful projects for their region. At the regional level, this problem is complicated by the complexity of various factors and their strategic importance. Currently, there are many methods for selecting regional projects to be included in the development program of the territory. However, many methods are only aimed at assessing the financial appeal of a project. The nature of regional projects is very complex and should be evaluated not only from the point of view of their financial success, but also given the specific characteristics of the region. In this study, we have examined a multi-criteria method for selecting of regional projects based on analytical hierarchic process. In particular, we have developed criterion of project selection at the regional level.

Public construction agencies are one of the largest developers within the Danish construction industry, where such agencies own and develop new public construction projects. Most of these projects are put out in European tender. This study analyses the selection and assignment criteria employed by these agencies in different types of public sector projects.Some of the objectives pursued by the study include the determination of 1/ the selection and assignment criteria mostly used in Danish public tenders 2/how different types of projects use selection and assignment criteria in the bidding process, and 3/any significant difference between the use of selection and assignment criteria in Danish public construction projects. The study uses a quantitative research approach where 157 Danish public tender cases were selected from the European Tenders Electronic Daily database between the period: January 2010 to March 2013. Fisher's Exact Test was conducted to determine if there was any significant use of some selection and assignment criteria. The findings of the study showed that invited tenders with pre-qualification and lowest pricein 69.8% of the tenders are the most used selection and assignment criteria, with little regard to project type.

We consider an Approximate Dynamic Programming heuristic to support the selection of defense projects when projects have different values and are originated intermittently but fairly frequently. We show that a simple policy reserving a positive fraction of the available budget for high-value projects not yet originated is superior to a greedy knapsack approach.

Selection of profitable research and development (R&D) projects is one of the major factors affecting sustained growth of firms and countries. This paper analyze what influences the knowledge on the business model exerted on selection of a R&D project. A business model converts the technology value to the customer value, and comprehensively describes the target customers for commercializing a new technology, core values, behaviors within organizations, resources, and external partners. Thus, understanding a business model would make R&D project evaluators place the feasibility and profitability of the business above the merits of the proposed technology in evaluating the technology development. To verify this hypothesis, we had 78 R&D project evaluators acquire the knowledge on the business model and measured how their criteria for R&D project selection have changed using the AHP method. The results shows that feasibility and profitability are more important than the merit of proposed technology, especially capability of company and business development are more important than the levels of technology innovation.

The selection of the present information system project is difficult because of the many factors that influence it. Information system project should pay attention to the user acceptance, technology and the environment in terms of their influence on the information system project selection. The purpose of this paper is to determine how much influence user acceptance, technology and the environment have on the information system project selection. This research uses data obtained from several ministries and analyzed using SEM (Structural Equation Models). The results found that the technology and the environment affects user acceptance. Moreover, technology and environment affect the effectiveness of the information systems project selection through the mediating effect of user acceptance. User acceptance, tested by the incorporation of usefulness and ease of use, the results are more modest and in line with previous theories. Furthermore, the external environment highly impacts the information system project selection.

The rapid development of civilization, population growth and constant increase in the resource consumption level had an effect on changes in methodologies and procedures for approaching the problem in many areas including the traffic engineering. The model of the transport projects selection is presented in this paper. The case study was the street reconstruction into pedestrian area in one city in the south-eastern Balkans. Authors wanted to compare results obtained by the Transportation Master Plan and multicriteria decision making method. The first analyse was based on the traffic parameters, but alternatives cannot be ranked clearly. The inclusion of more criteria, such as environmental parameters, costs, benefits and exterior projects influences, would require the application of some multicriteria decision making method. Authors researched whether the ANP (Analytic Network Process) as a multicriteria decision making approach may represent assistance in making a correct decision related to selection of transport infrastructure project. In this case study developed method favours alternative which provides the best result in terms of effectiveness. Results obtained by the conventional method were not enough to define final alternative rank. As a support, the ANP approach is suitable for the consideration of all aspects of transport projects’ effectiveness.

This paper introduces a quantitative fuzzy weighted average method to assist contractors in deciding whether to bid on a project by simulating a multiple-criteria decision making process that integrates a group of several decision-makers. The triangular fuzzy-set model is implemented to define linguistic terms used to describe subjective judgments related to decision-makers' experience level, criteria weight assessment and selection criteria rating. This paper provides an illustrative case study of project selection to demonstrate its effectiveness. The proposed method applied here to construction projects can be implemented for any type of project in any geographic area.