Project Selection Problem Research Articles

A hybrid fuzzy MCDM approach for project selection problem

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.

On optimization of R&D Project Selection and Scheduling

There is discussed the multi-period problem of project selection and scheduling for research and development (R&D) projects. The problem is a NP-hard RCPSP problem with discrete time periods (weeks), available budget constraints, deadlines, interruption of stages and dynamic change of initial data. Due to this peculiarities and the computational complexity, splitting the problem into series of subproblems for each period is proposed. These subproblems can be solved using GPU programming.

New Closeness Coefficients for Fuzzy Similarity Based Fuzzy TOPSIS: An Approach Combining Fuzzy Entropy and Multidistance

This paper introduces new closeness coefficients for fuzzy similarity based TOPSIS. The new closeness coefficients are based on multidistance or fuzzy entropy, are able to take into consideration the level of similarity between analysed criteria, and can be used to account for the consistency or homogeneity of, for example, performance measuring criteria. The commonly known OWA operator is used in the aggregation process over the fuzzy similarity values. A range of orness values is considered in creating a fuzzy overall ranking for each object, after which the fuzzy rankings are ordered to find a final linear ranking. The presented method is numerically applied to a research and development project selection problem and the effect of using two new closeness coefficients based on multidistance and fuzzy entropy is numerically illustrated.

New intuitionistic fuzzy approach with multi-objective optimisation on the basis of ratio analysis method

Application of MCDM methods in different fields of knowledge is increasing and new techniques are invented to help decision experts achieving optimised solution and getting reliable and facilitated route to final decision. Multi-objective optimisation on the basis of ratio analysis (MOORA) is a recent and novel tool invented and employed in many scientific projects. In other side, in every decision environment, there are massive information and undetermined situations which affect decision process and evidently its outcomes. Therefore, to improve decision preciseness and to overcome vagueness in uncertain environments, fuzzy theory and newly intuitionistic fuzzy approach try to get more reliable solutions. In this paper, a new version of intuitionistic fuzzy MOORA technique is proposed with triangular fuzzy numbers in a group decision making situation. Ultimately, a case of project selection problem is supposed to validate the applicability of the proposed approach.

Scaling up a Project Portfolio Selection Technique by using Multiobjective Genetic Optimization

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.

Modeling consensus using logic-based similarity measures

One of the key issues when it comes to measuring similarity is the discrepancy that exists between the idealized measures and actual human perception. The aim of this paper is to explore the possibility of using logic-based similarity measures for modeling consensus. We propose a soft consensus model for calculating the consensus and proximity degrees on two different levels. The proposed model relies on logic-based similarity measures and the appropriate aggregation functions. It is a fresh approach as it includes logic when perceiving similarity. Several similarity measures based on min, product and Lukasiewicz fuzzy bi-implications are introduced for modeling consensus. We also define a measure of similarity based on interpolative Boolean algebra (IBA) equivalence, and provide its comprehensive theoretical background. In our approach, we analyze how these different logic-based measures treat similarity, and whether they are appropriate to explain the notion of consensus. Finally, we show that IBA equivalence is the only measure that is both appropriate for modeling consensus and interpretable at the same time. The proposed model is illustrated on a problem of project selection in the context of sustainable development and the numerical results are discussed.

Optimal multinational project adjustment and selection with random parameters

With the globalization of the economy, more and more companies have made and will continue to make project investment abroad. This paper discusses a multinational project adjustment and selection problem in which project parameters are regarded as random variables. Special cash flows and value sources brought from the new foreign projects and the adjustment of the existing foreign projects are introduced. A new risk measurement for budget exceeding is proposed and a new model is developed based on the new risk measurement. In addition, a genetic algorithm is designed for solving the problem and an example is given as an illustration. The results of the example show that the algorithm is effective for solving the proposed problem. In addition, the results show that the budget can be better used and bigger investment return in net present form can be obtained when new foreign project selection and the existing project adjustment are simultaneously considered than when only new projects are selected.

A Comprehensive Framework for Sustainable Project Portfolio Selection Based on Structural Equation Modeling

Project selection problems are inherently complex problems with multiple and often conflicting objectives. The complexity of project selection problems is due to the high number of projects from which a subset (portfolio) has to be chosen. Various analytical methods, ranging from the simple weighted sum to complex mathematical programming have been proposed to solving these problems. We propose an integrated approach for strategic and sustainable project portfolio selection, which is composed of two distinct but interrelated modules. In the first module, we use the strategic planning and sustainability concepts to select a set of promising projects. In the second module, we use a project portfolio selection procedure to choose among the promising projects identified in the first module. A structural equation model is used to analyze and explain the relationships among different factors in the proposed framework. More specifically, we investigate the effects of: (1) strategic level performance on sustainability, post-implementation, and overall performance; (2) implementation performance on post-implementation and overall performance; (3) portfolio selection performance on implementation and overall performance; and (4) post-implementation performance on overall performance. A case study in investment banking is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

A New Hybrid Model Based on Least Squares Support Vector Machine for Project Selection Problem in Construction Industry

Effective project selection necessitates considering numerous conflicting factors for the decision making in construction industry. Multiple factors, such as resource requirements, budget control, technological implications and governmental regulations, influence the decision to select an appropriate project selection in construction industry. Among the recent methods and models, an artificial intelligence can be recommended to achieve higher performance than traditional methods in the field. This paper introduces an effective artificial intelligence (AI) model based on modern neural networks to improve the decision making for the projects owners. A hybrid AI model based on least squares support vector machine and cross validation technique is proposed to predict the overall performance of construction projects. The presented model can be successfully utilized for long-term estimation of the performance data in construction industry. Finally, the proposed model is implemented in a real case study for construction projects. To illustrate the capabilities of the proposed model, two well-known AI models, known as back propagation neural network and radial basis function neural network, are taken into consideration. The comparisons demonstrate the superiority of the presented model in terms of its performance and accuracy through the real-world prediction problem.

A simple model of project selection with strategic communication and uncertain motives

I analyze a problem of project selection where two agents, privately informed of both the true value and their bias in favor of their alternatives, make non-verifiable proposals to an uninformed decision-maker. The analysis makes two contributions. First, I examine the consequences of preference uncertainty for the information aggregation problem and show that the relative attractiveness of allowing a given agent to cast the decisive vote is increasing in the stability of his preferences. Second, I analyze the costs and benefits of pre-communication disclosure of the realized biases. I find that, conditional on the decision structure, disclosing the bias of the decisive agent is never optimal while disclosing the bias of the other agent is always optimal. This result makes an asymmetric treatment of the agents in terms of disclosure policies often optimal, despite the additional benefit of disclosure, which is the ability to condition the decision-making structure itself on the disclosed biases.

A multistage stochastic programming approach in project selection and scheduling

In this paper, the joint problem of project selection and project scheduling under uncertain environment is formulated, analyzed, and solved by multistage stochastic programs. First of all, a general mathematical formulation which can address several versions of the problem is presented. A multi-period project selection and scheduling problem is introduced and modeled by multistage stochastic programs, which are effective for solving long-term planning problems under uncertainty. A set of scenarios and corresponding probabilities is applied to model the multivariate random data process (costs or revenues, available budget, chance of success). Then, due to computational complexity, a scenario tree of the resulted scenarios is constructed by scenario reduction algorithms. Finally, assuming resources of the projects to be limited and renewable, and present worth of profit of projects as the objective, the objective of the problem is maximized. Eventually, a case study is introduced and solved, and the results are presented. The effectiveness of the proposed algorithm is shown by the numerical results.

A decision-making framework for justifying a portfolio of IT projects

This paper presents a comprehensive decision-making framework for evaluating a portfolio of IT projects. A problem of IT project selection with and without project interdependencies is considered. The problem is subject to maximum project funding budget constraint. When IT project portfolio contains independent projects, a dynamic programming (DP) solution procedure is proposed to efficiently solve the portfolio of IT projects' problem. However, when IT project portfolio contains project interdependencies, a mixed integer programming (MIP) approach is needed to solve the problem optimally. Experiments and results using simulated data using Monte Carlo simulation are provided. The results indicate that a large set of project selection problems containing up to 60 projects can be solved easily using the proposed decision-making framework.

Robust Control of the Multi-Armed Bandit Problem

We study a robust model of the multi-armed bandit (MAB) problem in which the transition probabilities are ambiguous and belong to subsets of the probability simplex. We characterize the optimal policy as a project-by-project retirement policy but we show that arms become dependent so the Gittins index is not optimal. We propose a Lagrangian index policy that is computationally equivalent to evaluating the indices of a non-robust MAB. For a project selection problem we fi nd that it performs near optimal.

Mean-Variance Model for Optimal Multinational Project Adjustment and Selection

This paper discusses the multinational project adjustment and selection problem in which project parameters are regarded as random variables. In the paper, adjustment of foreign existing projects and selection of the new foreign projects are considered simultaneously. Typical cash flows and value sources of foreign projects are introduced and a mean-variance optimal multinational adjustment and selection model is proposed. As an illustration, a numerical example is also presented.

Project selection problem under uncertainty: An application of utility theory and chance constrained programming to a real case

A B S T R A C T Selecting from a pool of interdependent projects under certainty, when faced with resource constraints, has been studied well in the literature of project selection problem. After briefly reviewing and discussing popular modeling approaches for dealing with uncertainty, this paper proposes an approach based on chance constrained programming and utility theory for a certain range of problems and under some practical assumptions. Expected Utility Programming, as the proposed modeling approach, will be compared with other well-known methods and its meaningfulness and usefulness will be illustrated via two numerical examples and one real case.

Solving multi-period project selection problems with fuzzy goal programming based on TOPSIS and a fuzzy preference relation

Project portfolio managers are multi-objective Decision-Makers (DMs) who are expected to select the best mix of projects by maximizing profits and minimizing risks over a multi-period planning horizon. However, project portfolio decisions are complex multi-objective problems with a high number of projects from which a subset has to be chosen subject to various constraints and a multitude of priorities and preferences. We propose a Goal Programming (GP) approach for project portfolio selection that embraces conflicting fuzzy goals with imprecise priorities. A fuzzy goal with an aspiration level and a predefined membership function is defined for each objective. The impreciseness in the priorities of the membership values of the fuzzy goals is modeled with fuzzy relations. This leads to type II fuzzy sets since fuzzy relations are organized between the membership values of the fuzzy goals which are themselves fuzzy sets. The proposed model is based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy preference relations. TOPSIS is used to reduce the multi-objective problem into a bi-objective problem. The resulting bi-objective problem is solved with fuzzy GP (FGP). The fuzzy preference relations are used to help DMs express their preferences with respect to the membership values of the fuzzy goals. The proposed approach is used to solve a real-life problem characterized as a fuzzy Multi-Objective Project Selection with Multi-Period Planning Horizon (MOPS–MPPH). The performance of the proposed approach is compared with a competing method in the literature. We show that our approach generates high-quality solutions with minimal computational efforts.

A decision support system for fuzzy multi-objective multi-period sustainable project selection

In this paper, a Decision Support System (DSS) is developed to solve sustainable Multi-Objective Project Selection problem with Multi-Period Planning Horizon (MOPS-MPPH). First, a TOPSIS based fuzzy goal programming (FGP) is proposed which considered uncertain DM preferences on priority of achievement level of fuzzy goals. The FGP essentially considers economic factors like cost, profit, and budget. The output of FGP and other affecting factors (i.e. social and environmental effects, risk of investment, strategic alliance, and organizational readiness) are treated as inputs of a fuzzy rule based system to estimate fitness value of an investment. Properties of the proposed DSS are discussed. It also is compared with an existing procedure on historical data of a financial and credit institute.

Strategic framework for sustainable project portfolio selection and evaluation

Project selection problem (PSP) is a general paradigm. Although the economic analysis is the most common used criteria, it is not sufficient. In this paper, five main research themes are introduced for PSP. Then, a comprehensive framework is proposed for project portfolio selection and evaluation. The proposed framework has two phases. First phase mainly talks about planning and selection through sustainable strategic planning and comprehensive portfolio management. In the second phase a balance score card (BSC) approach is supplied to assess and evaluate the results of planning and selection phase. The proposed procedure supplies a comprehensive framework for PSP. The implementation of the final set of selected projects is assessed through BSC.

An integrated multi-objective framework for solving multi-period project selection problems

Investment managers are multi-objective decision-makers (DMs) who make portfolio decisions by maximizing profits and minimizing risks over a multi-period planning horizon. Portfolio decisions are complex multi-objective problems which include both tangible and intangible factors. We propose an integrated multi-objective framework for project portfolio selection with respect to both the profits and risks objectives. The proposed method is based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and an efficient version of the epsilon-constraint method. TOPSIS is used to reduce the Multi-Objective Decision Making (MODM) problem into a bi-objective problem. The efficient epsilon-constraint method is used to generate non-dominated solutions with a pre-defined and arbitrary resolution on the Pareto front of the aforementioned bi-objective problem. The results from the integrated framework proposed in this study are compared with the results from the conventional epsilon-constraint method based on a series of simulated benchmark cases. A sensitivity analysis is performed to study the sensitivity of the relative importance weights of the objective functions in re-generating the Pareto front. The practical application of the proposed framework illustrates the efficacy of the procedures and algorithms.

A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection

Project selection is a complex decision making process that is influenced by multiple and often conflicting objectives. The complexity of the project selection problem is due to the high number of projects from which a subset (portfolio) has to be chosen. We present a hybrid fuzzy rule-based multi-objective framework for sustainable project portfolio selection. The multiple and conflicting objectives are considered as the input variables in a Fuzzy Rule-Based (FRB) system developed to estimate the overall fitness (suitability) of the potential project portfolios. A hybrid multi-objective framework integrates and synthesizes the results from a data mining model with the results from a Data Envelope Analysis (DEA) model and an Evolutionary Algorithm (EA) to design the structure of the proposed FRB system. The proposed framework simultaneously considers the accuracy maximization and the complexity minimization objectives. A Genetic Based Machine Learning (GBML) method is utilized to design an alternative FRB system for comparison purposes. The proposed framework and the GBML method are used to assess the alternative project portfolios in a real-world financial services institution. The statistical analysis shows the performance dominance of the proposed hybrid framework over the GBML method based on selected accuracy and interoperability measures.